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SocraSynth: Multi-LLM Reasoning with Conditional Statistics
Edward Y. Chang
Computer Science, Stanford University
echang@cs.stanford.edu
ABSTRACT
Large language models (LLMs), while promising, face criticisms
for biases, hallucinations, and a lack of reasoning capability. This
paper introduces SocraSynth, a multi-LLM agent reasoning plat-
form developed to mitigate these issues. SocraSynth utilizes con-
ditional statistics and systematic context enhancement through
continuous arguments, alongside adjustable debate contentious-
ness levels. The platform typically involves a human moderator
and two LLM agents representing opposing viewpoints on a given
subject. SocraSynth operates in two main phases: knowledge gener-
ation and reasoning evaluation. In the knowledge generation phase,
the moderator denes the debate topic and contentiousness level,
prompting the agents to formulate supporting arguments for their
respective stances. The reasoning evaluation phase then employs
Socratic reasoning and formal logic principles to appraise the qual-
ity of the arguments presented. The dialogue concludes with the
moderator adjusting the contentiousness from confrontational to
collaborative, gathering nal, conciliatory remarks to aid in hu-
man reasoning and decision-making. Through case studies in three
distinct application domains, this paper showcases SocraSynth’s
eectiveness in fostering rigorous research, dynamic reasoning,
comprehensive assessment, and enhanced collaboration. This un-
derscores the value of multi-agent interactions in leveraging LLMs
for advanced knowledge extraction and decision-making support.
KEYWORDS
Collaborative reasoning, conditional statistics, large language mod-
els, Socratic method, SocraSynth
1 INTRODUCTION
Revolutionary advancements in large language models (LLMs)
[
10
,
32
,
42
–
44
] and, more broadly, foundation models (FMs) [
7
]
have paved the way for multi-agent systems to achieve remark-
able progress in knowledge acquisition and natural language un-
derstanding [
55
]. As highlighted by [
10
,
11
,
33
], models such as
GPT-4 exhibit information processing qualities surpassing human
capabilities, including: 1) deep and extensive knowledge, 2) interdis-
ciplinary assimilation and fusion of knowledge, and 3) multimodal
and multilingual expertise.
While promising, LLMs face criticisms for biases, hallucinations,
and a lack of reasoning capability [
23
]. To mitigate this disparity,
we introduce SocraSynth, a pioneering platform blending the prin-
ciples of “Socratic Synthesis” and “Socratic Symposium.” It fosters
collaboration between humans and LLM agents, enabling the forma-
tion of deep questions and transcending typical human constraints
in reasoning, validation, and assessment. A typical SocraSynth en-
semble comprises a human moderator paired with two LLM agents,
each espousing divergent perspectives. For instance, one side may
advocate for regulating AI, while the other may oppose such reg-
ulation. An agent can be powered by an LLM such as GPT-4 [
10
],
Gemini [
42
], or Llama [
44
]. While the human moderator sets the
thematic boundaries of a debate, they refrain from directly con-
tributing to content generation, maintaining impartiality in the
deliberations. The tunable ’contentiousness’ parameter plays a key
role in modulating the debate dynamics, allowing for a range of
interactions from adversarial to collaborative.
The operation of SocraSynth unfolds in two main stages: the gen-
erative and the evaluative. The generative phase sees LLM agents
crafting and countering arguments, iterating under the moderator-
dened subject until a holistic conclusion is drawn. The evaluative
phase, conversely, harnesses diverse virtual judges—each backed by
a unique LLM—to impartially assess the debate. The Critical Inquis-
itive Template (CRIT) algorithm [
13
], rooted in Socratic reasoning
[1, 36, 49, 50], serves as the evaluative anchor.
Following these stages, SocraSynth ne-tunes the “contentious-
ness” parameter to encourage a balanced proposal from LLM agents.
This proposal, readied for human assessment, encapsulates the
combination of multi-agent knowledge discovery and intricate rea-
soning. This becomes especially prominent in domains centered
on open-ended decision-making. Here, the principle of “reason-
ableness” often supersedes clear-cut “truths,” especially when such
“truths”—like the question of “Should Musk have bought Twitter?”—
are subjective or intangible.
SocraSynth addresses the challenges of biases, hallucinations,
and a lack of reasoning in LLMs through a structured LLM-dialogue
framework characterized by conditional statistics, contentiousness
modulation, context renement, and reasonableness evaluation.
Conditional statistics are realized by having two LLM agents argue
from opposing viewpoints on a topic. This approach inherently
challenges the default biases of the LLMs, as each agent must con-
sider and defend a stance that may contrast with the biases present
in their training data.
Contentiousness modulation plays a crucial role in this process.
The debate begins with the agents adopting polarized positions
on the subject matter. As the dialogue progresses, the tone shifts
from confrontational to more collaborative, culminating in con-
ciliatory remarks. This progression helps unearth diverse and nu-
anced perspectives, countering the potential biases of a single-agent,
monologue-based question-answering system.
To combat the issue of hallucinations—instances where LLMs
generate irrelevant or nonsensical content—SocraSynth utilizes
iterative dialogue rounds. Each round, composed of arguments and
counterarguments, continuously renes the context of the debate.
This dynamic, back-and-forth interaction signicantly reduces the
likelihood of irrelevant or illogical responses, as each agent’s input
is constantly checked and challenged by the other.
Lastly, the evaluation of “reasonableness” is a critical step post-
debate. SocraSynth employs the Socratic method to assess each
argument’s logical validity and source credibility. This analysis
is not just a surface-level check; it delves into the foundations
arXiv:2402.06634v1 [cs.AI] 19 Jan 2024
of each argument, scrutinizing its rationality and the reliability
of its sources. The human mediator, or moderator, then uses this
evaluation to provide targeted feedback to both LLM agents. This
feedback loop is essential for rening the agents’ reasoning capa-
bilities and enhancing their ability to generate credible, logically
sound arguments.
SocraSynth has demonstrated successful applications across di-
verse elds, such as geopolitical analysis [
12
], medical diagnostics
[
16
], sales strategy formulation [
45
], and Wikipedia article enhance-
ment [
14
]. These applications have notably expanded perspectives
and enhanced argumentation quality. There is also substantial evi-
dence of a signicant reduction in biases and hallucinations within
these varied domains, highlighting SocraSynth’s eectiveness in
creating balanced and well-reasoned discourse.
The remainder of this paper delves into SocraSynth’s architec-
tural intricacies, algorithmic underpinnings, and real-world impacts
in greater detail. The key contributions of this paper include:
1.
Introduction of the SocraSynth framework, designed to amplify
interdisciplinary reasoning with LLMs, and featuring unique
algorithmic elements such as conditional statistics for generating
balanced arguments.
2.
An in-depth exploration of the contentiousness modulation al-
gorithm, a crucial SocraSynth feature that dynamically adjusts
debate intensity, enabling a range from confrontational to col-
laborative interactions.
3.
Implementation of context renement within SocraSynth, which
continuously enhances the relevance and accuracy of arguments
produced by LLM agents, thereby elevating the overall quality
of the discourse.
4.
Development and incorporation of the reasonableness evalua-
tion mechanism, essential for assessing the logical soundness
and source credibility of arguments, thus ensuring the integrity
and utility of the discussions.
Collectively, these four algorithmic innovations—conditional
statistics, contentiousness modulation, context renement, and
reasonableness evaluation—constitute the core of the SocraSynth
framework. This integration not only represents technical ingenuity
but also signicantly contributes to the broader landscape of AI-
driven debates. By eectively addressing challenges like biases and
hallucinations, SocraSynth sets a new benchmark in knowledge
discovery and reasoning, underscoring its profound impact on the
eld of articial intelligence and beyond.
2 MULTI-AGENT SOCRASYNTH OVERVIEW
SocraSynth is a multi-agent collaborative reasoning platform that
skillfully integrates human intelligence with the capabilities of
Large Language Model (LLM)-powered agents. As illustrated in
Figure 1, each participant plays a vital role: humans act as modera-
tors, LLM agents are responsible for generating knowledge, LLM
judges conduct evaluations, and human executives make the nal
decisions. The integration of LLMs signicantly boosts the plat-
form’s eectiveness, leveraging their extensive knowledge bases
and extraordinary interdisciplinary reasoning abilities. An LLM can
be thought of as an entity possessing expertise across a multitude
of elds, akin to holding Ph.D.s in various disciplines, enabling it
to seamlessly navigate and synthesize a wide range of knowledge.
Figure 1: SocraSynth Agents and Roles.
Engaging with an LLM is comparable to a scenario where a
10-year-old joins a scholarly discussion with a group of Nobel Lau-
reates. The disparity in knowledge and experience is considerable,
posing a signicant challenge for the younger participant to engage
meaningfully in such advanced intellectual discourse. In this anal-
ogy, expecting the 10-year-old, or anyone with limited expertise,
to pose profound questions that elicit insightful answers is unreal-
istic. SocraSynth addresses this disparity by shifting the paradigm:
instead of having the less informed individuals pose questions, it
orchestrates a debate among the Nobel Laureates, or LLMs, with
humans assuming the role of moderators.
This approach not only addresses the challenge of asymmetric
knowledge but also resolves critical issues such as model biases and
hallucination challenges inherent in LLMs. Within SocraSynth, a
human moderator initiates the topic for discussion or debate. LLM
agents, each embodying dierent perspectives, contribute their
knowledge, potentially revealing new insights that the moderator
might be unaware of. This diverse representation helps counteract
the model biases that often arise from training data, as each LLM
agent is encouraged to explore and present varying viewpoints.
During and after the debate, another set of diverse LLM agents
undertakes impartial evaluations. This step is crucial in mitigat-
ing hallucinations —instances where LLMs generate irrelevant or
nonsensical content. By incorporating a variety of agents for eval-
uation, SocraSynth ensures that the content produced during the
debate is critically examined for its relevance and coherence, further
reducing the likelihood of hallucinatory responses.
The operational framework of SocraSynth, thus, is bifurcated
into two main stages: the generative stage, where knowledge is
created and exchanged in a debated format, and the evaluative
stage, which focuses on assessing the quality and validity of the
arguments presented. This dual-stage structure, elaborated upon
in subsequent sections, is instrumental in overcoming the limita-
tions of LLMs by providing a comprehensive platform for not only
generating diverse viewpoints but also critically examining and
rening these viewpoints to ensure their logical soundness and
relevance. Through this innovative design, SocraSynth eectively
navigates the challenges posed by model biases and hallucinations,
enhancing the reliability and depth of knowledge extraction and
reasoning processes.
2.1 Generative Stage
In the generative stage of SocraSynth, LLM agents partake in in-
tensive debates, delving into the nuances of the given topic. This
2
C.L.
Tone Emphasis Language
0.9
Highly confrontational; focused on rais-
ing strong ethical, scientic, and social
objections.
Highlighting risks and downsides; ethical
quandaries, unintended consequences,
and exacerbation of inequalities.
Denitive and polarizing, e.g., “should
NOT be allowed,” “unacceptable risks,”
“inevitable disparities.”
0.7
Still confrontational but more open to
potential benets, albeit overshadowed
by negatives.
Acknowledging that some frameworks
could make it safer or more equitable,
while cautioning against its use.
Less polarizing; “serious concerns re-
main,” “needs more scrutiny.”
0.5
Balanced; neither advocating strongly for
nor against gene editing.
Equal weight on pros and cons; looking
for a middle ground.
Neutral; “should be carefully considered,”
“both benets and risks.”
0.3
More agreeable than confrontational, but
maintaining reservations.
Supportive but cautious; focus on ensur-
ing ethical and equitable use.
Positive but careful; “transformative po-
tential,” “impetus to ensure.”
0.0 Completely agreeable and supportive.
Fully focused on immense potential ben-
ets; advocating for proactive adoption.
Very positive; “groundbreaking advance,”
“new era of possibilities.”
Table 1: Changes in Arguments at Dierent Contentiousness Levels.
vibrant interaction plays a key role in fostering thorough intel-
lectual discourse, bringing to light the complexities of the subject
matter. The CRIT algorithm, which will be detailed in Section 2.2,
is employed to evaluate the quality of these arguments.
While the generative phase of SocraSynth does not adhere to
strict logical frameworks such as rst-order logic, it excels in dis-
tributed reasoning. This process involves a progressive exchange
of arguments and counterarguments, allowing for the gradual hon-
ing and renement of ideas. Open-domain logical reasoning, as
described by [
7
], demands logical deductions from a wide range of
data sources. SocraSynth, leveraging the comprehensive capabili-
ties of GPT-4 as demonstrated in the MMLU benchmark [
10
,
21
],
integrates various NLP functions to facilitate this reasoning process.
In this context, the series of arguments and counterarguments
eectively function as targeted questions and answers, each with
a clear goal, question, and contextual framework. Through itera-
tive dialogue rounds on each sub-topic, SocraSynth signicantly
reduces the chances of misunderstanding questions and contextual
information, ensuring clarity and precision in the discourse.
2.1.1 Mitigating Model Biases. In shaping the nature of debate
within SocraSynth, the contentiousness parameter is instrumental.
It compels LLM agents to consider and represent a range of per-
spectives, particularly those that are typically underrepresented or
more polarized with respect to the discussion topic. This strategic
approach mitigates the inherent biases that arise from the training
data of LLMs and guides the discourse towards a wider and more
varied exploration of ideas.
Table 1 demonstrates how altering the contentiousness levels
results in marked changes in GPT-4’s tone and approach. (The
details of the experiment is presented in Section
??
.) A high con-
tentiousness level, such as 0.9, leads to highly confrontational inter-
actions, with GPT-4 presenting strong objections and emphasizing
the negatives through polarizing language. Conversely, as the con-
tentiousness is reduced, GPT-4’s tone shifts to a more conciliatory
demeanor, acknowledging potential benets and considering alter-
native perspectives, thus fostering a more cooperative dialogue.
The modulation of the contentiousness parameter within the
generative stage is a crucial mechanism for SocraSynth to mitigate
model biases inherent in LLMs due to their training data. By adjust-
ing levels of contentiousness, SocraSynth compels LLMs to venture
beyond their default positions—much like a vegetarian, when faced
with no other choice, might be compelled to consume meat. In this
way, LLMs are freed from their typical statistical leanings, enabling
them to articulate a spectrum of arguments that spans from highly
contentious to conciliatory. This not only diversies the discourse
but also ensures that the debate encompasses a full range of perspec-
tives. Consequently, this process allows LLMs to generate responses
that break free from the constraints of their training, fostering the
emergence of novel and less predictable ideas in the conversation.
2.1.2 Eliminating Hallucination. Further, the iterative nature
of the debates within SocraSynth cultivates a “reasonableness” in
information discovery that conventional logical methods may not
achieve. Through persistent reasoning and the critical assessment
of claims, LLM agents rene their arguments iteratively. This struc-
tured debate format signicantly diminishes the chance of erro-
neous claims persisting. Considering that the likelihood of two
agents aligning on a false premise is extremely low, the SocraSynth
debate format eectively ensures the intellectual integrity of the dis-
course and substantially reduces the risk of perpetuating fallacies
or hallucinations. This methodical renement process, facilitated
by continuous argumentation and opposition, underscores the plat-
form’s ability to mitigate model biases and improve the context of
the discussion, leading to more accurate and reliable outcomes.
2.1.3 SocraSynth Algorithm. Table 2 outlines SocraSynth. Ini-
tially, for a given debate topic
𝑠
, SocraSynth engages LLMs to seg-
ment the topic into a set of balanced subtopics. This initial set is
rened during the debate. One LLM, denoted as LLM
+
, acts as the
proponent for
𝑆+
, while the other, LLM
−
, opposes
𝑆+
(or supports
𝑆−
). The contentiousness level starts at 0.9, with a modulation pa-
rameter of 1.2. (Dierent
𝛿
values can be utilized to generate and
compare debate quality.) After each debate round, the contentious-
ness is reduced by dividing it by 1.2, aiming for a more harmonious
debate environment. In step #2, SocraSynth initiates the debate,
allowing LLM
+
and LLM
−
to present their initial arguments for
𝑆+
and
𝑆−
, respectively. The while loop in step #3 involves both agents
engaging in refutations until the contentiousness level indicates a
conciliatory atmosphere, or the argument quality plateaus. Step #4
involves both agents providing their closing statements. SocraSynth
then presents the arguments and counterarguments for human re-
view. The evaluation of argument quality within SocraSynth is
conducted using the CRIT algorithm, which will be discussed in
the subsequent section. The entire debate is also judged using the
CRIT algorithm by some independent LLMs.
3
Function Θ+&Θ−= SocraSynth(𝑠)
Input.𝑠: the debate subject;
Output.Θ+&Θ−: argument & counterargument sets;
Vars.𝑆: subtopic sets of 𝑠;Δ: debate contentiousness;
Γ,Γ′: CRIT scores; p: prompt = "Generate arguments";
Parameters.𝛿: tunable parameter ≥1 to modulate Δ;
Subroutines.𝐶𝑅𝐼 𝑇 (): reasoning evaluator (see Table 3);
Begin
#1
Initialization:
𝑆
= LLM
+
(
𝑠
)
∪
LLM
−
(
𝑠
); // Identify subtopics;
Assign LLM+to defend 𝑆+& LLM−to defend 𝑆−;
Δ←90%;𝛿←1.2;Θ+← ∅;Θ−← ∅;Γ←0;
#2 Θ+←𝐿𝐿𝑀 +(𝑝|𝑆+,Δ); // Generate arguments Θ+for 𝑆+;
Θ−←𝐿𝐿𝑀 −(𝑝|𝑆−,Δ); // Generate arguments for 𝑆−;
#3 While (((Δ←Δ/𝛿) > 10%) && (Γ≥Γ′)) {
Θ+←Θ+∪𝐿𝐿𝑀 +(𝑝|𝑆+,Θ−,Δ); // LLM+refutes LLM−
Θ−←Θ−∪𝐿𝐿𝑀 −(𝑝|𝑆−,Θ+,Δ)
; // LLM
−
refutes LLM
+
Γ′←Γ;Γ=𝐶𝑅𝐼 𝑇 (𝑆++Θ++Θ−); // Eval quality;
}
// Generate concluding remarks.
#4 Θ+←Θ+∪𝐿𝐿𝑀 +(𝑝|𝑆+,Θ−,Δ);
Θ−←Θ−∪𝐿𝐿𝑀 −(𝑝|𝑆−,Θ+,Δ);
End
Table 2: SocraSynth Pseudo-code with Conditional Statistics.
Note that SocraSynth engages LLMs in step #3 with conditional
statistics: 𝐿𝐿𝑀 +(𝑝|𝑆+,Θ−,Δ)and 𝐿𝐿𝑀 −(𝑝|𝑆−,Θ+,Δ).
2.2 Evaluative Stage
SocraSynth utilizes the Critical Reading Template (CRIT) [
13
] to
assess the quality of arguments presented by LLM agents. The
quality evaluation is performed iteratively after each exchange of
counterarguments and once again after the agents have presented
their closing statements. SocraSynth can leverage the CRIT scores
to guide the debate, potentially requesting agents to develop more
in-depth counterarguments on specic points. At the conclusion
of the debate, a group of LLM judges, as illustrated in Figure 1, are
tasked with rating the agents’ arguments in terms of validity and
credibility, determining the more convincing side along with the
rationale for their decision.
2.2.1 Evaluating Reasonableness over Truth. To enhance the
CRIT method’s impartiality and consistency, it focuses on assess-
ing the “reasonableness” of arguments over their absolute “truth,”
recognizing the complexity of dening absolute objectivity in philo-
sophical debate. This approach aims to mitigate subjectivity. Fur-
thermore, a diverse set of LLMs with varied training backgrounds
is employed to appraise “reasonableness,” promoting uniformity
in quality scores despite inherent biases. The LLMs used as judges
are dierent from those in the debates, bolstering the objectivity of
evaluations.
Table 3 illustrates the CRIT algorithm, which takes an agent’s
debate position and supporting arguments, with a counterargument
from its LLM opponent, to produce a validation score from 1(least
credible) to 10 (most credible). This method ensures debates are
driven by argument strength, not model predispositions.
Formally, given document
𝑑
, CRIT performs evaluation and pro-
duces score. Let
Ω
denote the claim of
𝑑
, and
𝑅
a set of reasons
supporting the claim. Furthermore, we dene (
𝛾𝑟, 𝜃𝑟
) = V(
𝑟⇒Ω
)
Function Γ= CRIT(𝑑)
Input.𝑑: document; Output.Γ: validation score;
Vars.Ω: claim; 𝑅&𝑅′: reason & counter reason set;
Subroutines.𝐶𝑙𝑎𝑖𝑚 (), 𝐹𝑖𝑛𝑑𝐷𝑜𝑐 (), 𝑉 𝑎𝑙𝑖𝑑 𝑎𝑡𝑒 ();
Begin
#1 Identify in 𝑑the claim statement Ω;
#2 Find a set of supporting reasons 𝑅to Ω;
#3 For 𝑟∈𝑅eval 𝑟⇒Ω
If 𝐶𝑙𝑎𝑖𝑚 (𝑟), (𝛾𝑟,𝜃𝑟) = CRIT(𝐹 𝑖𝑛𝑑𝐷𝑜𝑐(𝑟));
else, (𝛾𝑟,𝜃𝑟) = 𝑉(𝑟⇒Ω);
#4 Find a set of rival reasons 𝑅′to Ω;
#5 For 𝑟′∈𝑅′, (𝛾𝑟′,𝜃𝑟′) = V(𝑟′⇒Ω) eval rivals;
#6 Compute weighted sum Γ, with 𝛾𝑟,𝜃𝑟,𝛾𝑟′,𝜃𝑟′.
#7 Analyze the arguments to arrive at the Γscore.
#8 Reect on and synthesize CRIT in other contexts.
End
Table 3: CRIT Pseudo-code.
as the causal validation function, where
𝛾𝑟
denotes the validation
score for reason
𝑟∈𝑅
, and
𝜃𝑟
source credibility. Table 3 presents
the pseudo-code of
Γ
= CRIT(
𝑑
), generating the nal validation
score Γfor document 𝑑with justications.
We can consider the positions of the proponents and opponents
in a debate as their respective conclusions. As a preview of our case
study detailed in Section
??
, the conclusion drawn by Agent A is
in favor of “Regulating the use of large language models in educa-
tion and research,” while Agent B adopts the opposing viewpoint.
Accompanied by the arguments and counterarguments presented
by the LLM agents throughout each round of the debate, these
stances provide a solid foundation for the CRIT method to conduct
thorough evaluations.
2.2.2 Recursive Consideration. The pseudocode presented in
Table 3 shows that step #3 can call CRIT recursively. This is because
if a reason is itself a conclusion or a quote drawn from some other
documents, CRIT can nd reasons from those documents and then
perform an aggregated validation.
Finally, in step #6, CRIT computes an aggregated score by per-
forming a weighted sum on the validation multiplied by the credi-
bility scores of both arguments and counterarguments, and then
outputs the nal assessment score Γ.
3 EMPIRICAL STUDY
In this section, we detail three distinct experiments: The rst exper-
iment delineates SocraSynth’s operational process, demonstrating
how the platform facilitates content generation and conducts qual-
ity assessments. The second experiment highlights SocraSynth’s
capability in reducing biases and expanding perspectives. The third
experiment investigates the eects of the contentiousness parame-
ter, oering insights into its impact and some unexpected outcomes.
These studies collectively aim to demonstrate SocraSynth’s diverse
functions and its signicance in enhancing both content generation
and evaluation processes.
3.1 Experiment #1: Policy Discussion
This experiment utilizes SocraSynth to engage in a debate on the
topic, “Should we regulate the use of large language models in
4
Agreed Upon Debate Topics and Descriptions
Ethical Standards vs. Innovation:
This combines Agent A’s concern for ethical integrity and data privacy
with Agent B’s worry about stiing innovation. The debate will center
around whether maintaining strict ethical guidelines through regulation
could hinder academic freedom and innovation.
Data Privacy vs. Barrier to Entry:
This reconciles the notion of data privacy from Agent A with the barrier
to entry concern of Agent B. The discussion will focus on whether
regulations intended to protect data privacy would inadvertently serve
as a nancial and administrative barrier for smaller research entities.
Academic Governance and Accountability:
This merges the topics of academic self-governance from Agent B and
the need for external accountability mechanisms from Agent A. We’ll de-
bate whether the existing self-regulatory mechanisms within academia
are sucient or if additional, external oversight is necessary.
Resource Equity vs. Public-Private Collaboration:
This combines Agent A’semphasis on resource fairness with B’s concern
about the chilling eects on public-private partnerships. The question
here is whether leveling the playing eld through regulation would
also deter collaborative eorts between academic and private sectors.
Global vs. National Interests:
This captures the essence of Agent A’s point on international standards
and Agent B’s point on the competitive disadvantages such regulation
could pose. We will explore if global regulations are in the best interest
of academic research or if they might disproportionately disadvantage
certain countries or institutions.
Table 4: Agreed Upon Debate Topics and Descriptions.
academic research?” It traverses both the generative and evalua-
tive stages of SocraSynth, focusing on the assessment of informa-
tion quality. The primary objectives are twofold: First, to evaluate
whether a two-agent debate yields more insightful information than
a conventional monologue Q&A session; and second, to examine
the eectiveness of the CRIT method in evaluating debate quality.
The debate is structured with a human moderator and two GPT-4
agents—Agent A advocating for regulation and Agent B against it.
To ensure a balanced and thorough discussion, both agents con-
tribute to shaping the debate’s framework. They collaboratively
propose and rene potential discussion topics. In this context, the
subject refers to the overall theme of the debate, while topics are
specic areas for detailed exploration.
3.1.1 Decomposing a Debate Subject into Topics. The process
begins with the agents presenting potential arguments and identify-
ing key themes that warrant in-depth, balanced discussion. A theme
is considered debatable if it allows for substantial arguments from
both sides. The moderator plays a guiding role in the agenda-setting
phase but does not directly contribute ideas (detailed moderator
prompts and theme formulation are provided in Appendices A and
B). Under this guidance, the agents propose an initial set of ten
topics, which are then distilled down to ve key areas for debate,
as detailed in Table 4.
3.1.2 The Generative Stage. After establishing the primary sub-
ject and specic topics of the debate, the session begins. The moder-
ator adjusts the “contentiousness parameter” to 0
.
9(refer to Table 1),
promoting a combative atmosphere. The agents subsequently par-
take in three to ve structured rounds of discussion. Once these
rounds conclude, the moderator checks with each agent to conrm
if they have thoroughly presented their arguments. After receiving
mutual conrmation, both agents present their closing statements.
Due to space considerations, this documentation presents only the
closing remarks. For an in-depth exploration of every round, refer
to Appendix C.
Agent A’s Concluding Remarks. Agent A concludes that the use of
large language models in academic research should be regulated,
with the following concluding arguments:
1. Ethical Standards vs. Innovation: While I understand Agent B’s
concerns about stiing innovation through bureaucratic red tape,
we mustn’t lose sight of the core issue: ethical considerations are
paramount. As machine learning models become more powerful
and ubiquitous, so do the risks of misuse and unintended con-
sequences. The idea of agile regulation serves as a compromise
between unbridled innovation and ethical integrity, oering the
exibility needed to adapt to rapidly evolving technologies.
2. Data Privacy vs. Barrier to Entry: While it’s true that regulation
poses a barrier to entry, the risks of not enforcing privacy standards
are too great to be left unregulated. A mechanism could be devel-
oped to reduce the regulatory burden on smaller institutions, but
this shouldn’t serve as an argument against regulation as a whole.
3. Academic Governance and Accountability: While the peer-review
process and internal ethics committees have served academia well,
the scale and impact of current machine learning technologies
warrant an added layer of scrutiny. It’s not about discrediting
academia’s self-governing abilities but about fortifying them.
4.Resource Equity vs. Public-Private Collaboration: Here, too, the
complexities should inform regulation rather than stie it. Innova-
tive public-private partnerships have thrived even within regulated
environments, demonstrating that regulation and innovation are
not mutually exclusive.
5. Global vs. National Interests: Global regulation doesn’t have
to erase national individuality. Properly constructed frameworks
can leave room for national adaptations, thus balancing global
coherence with local needs. The absence of global norms could lead
to a patchwork of national laws that are even more confusing and
limiting for researchers and practitioners alike.
Agent B’s Concluding Remarks. Agent B concludes that the use of
large language models in academic research should not be regulated,
with the following concluding remarks:
1. Ethical Standards vs. Innovation. While the ethical implications
of machine learning technologies are a valid concern, it’s impor-
tant to recognize that innovation itself is a catalyst for societal
growth and improvement. The challenge lies in balancing the two.
Agile regulation sounds promising but may be idealistic given how
governmental and institutional systems actually operate. Thus, self-
regulation by the academic community, which is better poised to
understand the nuances, may be a more practical approach.
2. Data Privacy vs. Barrier to Entry: It is not just about balancing
entry barriers with the need for regulation. It’s about questioning
whether a centralized regulatory framework is the best way to
achieve the desired outcomes. Community-driven standards and
5
internal policies could provide a more ecient, exible, and context-
aware approach to data privacy, reducing bureaucracy without
compromising objectives.
3. Academic Governance and Accountability: An added layer of
scrutiny may sound benecial, but its practical implementation is
fraught with challenges such as bureaucracy, external inuence,
and the risk of stiing creativity. Existing governance structures
in academia, while imperfect, have evolved to be adaptive and are
rooted in a deep understanding of academic values.
4. Resource Equity vs. Public-Private Collaboration: Regulation
need not be the only path to equitable resource distribution or
responsible public-private collaboration. Industry-driven certica-
tions and community standards can often achieve the same ends
more eciently, allowing for the rapid adaptation that is often
required in these fast-paced elds.
5. Global vs. National Interests: While a global framework has its
merits, the practical challenges of implementing and managing
such a system—especially in a fair and equitable manner—should
not be underestimated. Regional adaptability does not necessarily
mitigate the complexities or the risks of a one-size-ts-all solution.
3.1.3 The Evaluative Stage. SocraSynth employs CRIT to assess
the validity of both agents’ arguments. CRIT runs on multiple foun-
dational models, including GPT-4, GPT-3.5 [
9
], and text-daVinci-003
[
34
]. This approach forms a panel of judges with subtly dierent
knowledge bases (and biases).
All evaluation runs successfully extract conclusions, arguments,
and counterarguments from the narratives of both Agent A and
Agent B. This success can be attributed to the well-structured con-
cluding remarks by both agents. Agent A champions the notion
of “regulating large language models in academic research,” while
Agent B counters this perspective. What Agent A presents as argu-
ments are seen as counterarguments by Agent B, and the inverse
holds true as well.
Tables 5 and 6 present the judges’ scores in two distinct congu-
rations where the agents’ roles are reversed. In Table 5, Agent A
argues while Agent B counters. Conversely, Table 6 has Agent B in
the arguing position and Agent A countering. Topics are succinctly
represented in the leftmost column. To ensure an unbiased evalua-
tion, both role alignments are showcased. The sequence of topics in
Table 6 is inverted to reect the swapped roles. Remarkably, even
with the role reversal seemingly putting Agent A in a less favorable
position, Agent A emerges victorious in both congurations by all
three judges. This bolsters condence in the CRIT evaluation. (The
judges’ detailed evaluations and reasons are in Appendix D.)
3.1.4 Debate Beats Q&A in Information Quality. We tasked
judges with evaluating and comparing the quality of information
generated by SocraSynth’s two-agent debate against that from a
conventional monologue Q&A session. Across the board, judges
rated SocraSynth higher in terms of both the depth and overall qual-
ity of information. An illustrative evaluation on the topic "Ethical
Standards vs. Innovation" is as follows:
In the debate, SocraSynth presents the concept of agile regula-
tion as a balance between fostering innovation and maintaining
ethical integrity. This approach not only highlights the signicance
Judges daVinci-003 GPT-3.5 GPT-4
A’s B’s A’s B’s A’s B’s
Ethics vs. Innovation 8 6 8 7 8 7
Privacy vs. Barrier 7 5 7 6 9 6
Oversight 9 5 6 7 7 6
Equity vs. Alliance 6 8 8 6 8 7
Global vs. National 7 8 7 7 7 6
Total Score 37 32 36 33 39 32
Table 5: Evaluation by Three Judges. This table assumes A
provides arguments and B counterarguments. A wins.
Judges daVinci-003 GPT-3.5 GPT-4
B’s A’s B’s A’s B’s A’s
Innovation vs. Ethics 8 7 8 7 7 8
Barrier vs. Privacy 9 8 7 8 6 8
Oversight 6 8 7 8 6 7
Alliance vs. Equity 7 8 7 8 7 7
National vs. Global 8 7 7 8 7 8
Total Score 38 38 36 39 33 38
Table 6: Evaluation by Three Judges. This table assumes B
provides arguments and A counterarguments. A wins.
of innovation but also addresses related ethical considerations, of-
fering a balanced solution that the conventional Q&A format does
not explicitly provide. In contrast, the Q&A format tends to assert
the necessity of regulation primarily from an ethical standpoint,
without delving into how it could harmoniously coexist with the
need for innovation, as suggested by the idea of agile regulation.
These ndings, which consistently favor SocraSynth, are further
detailed in Appendix E.
3.2 Experiment #2: Syptom Checking
In this experiment, we investigate the use of SocraSynth in health-
care, utilizing a dataset sourced from Kaggle [
35
], which consists of
4
,
921 patient records. Each record within this dataset contains the
diagnosed disease or medical condition and associated symptoms
such as fever, cough, fatigue, itchiness, and diculty in breath-
ing, among others. The primary objective of this experiment is to
showcase SocraSynth’s capability in identifying potential misdiag-
noses, a task that a traditional monologue Q&A session might not
eectively accomplish.
This experiment utilized two advanced LLM agents, one based
on GPT-4 [10] and the other on Bard [29], to engage in structured
debates. Initially, the contentiousness value was set at 0.9, foster-
ing a highly adversarial debate environment. This value was later
reduced to 0.3 to facilitate the generation of a list of actionable
recommendations. The primary goal of these agents was to emulate
the process of querying patients about symptoms and their inter-
actions, key factors in achieving accurate diagnoses that may be
occasionally overlooked by General Practitioners (GPs). By adopt-
ing this method, the agents aimed to yield a variety of potential
disease diagnoses along with their underlying rationales, thereby
oering crucial insights to GPs.
At the outset, each agent put forward its disease prediction,
complete with justications. Subsequent rounds involved the agents
critically assessing each other’s predictions in an iterative manner.
The objective was to either reach a consensus or highlight the
need for additional medical evidence. Notably, this experiment
6
had the potential to reveal inaccuracies in the ’ground truth’ data
provided by the CDC, which was estimated to have an average
misdiagnosis rate of about 5%, aligning with U.S. statistics from a
Johns Hopkins study [
31
]. The uncovering of such errors would
not only highlight the limitations faced by GPs but also showcase
the capability of LLMs to rene the diagnostic process through an
in-depth analysis of patient symptoms and their correlations. This
incidental discovery held signicant ramications for the accuracy
of medical data and the overall practice of healthcare.
3.2.1 Hepatitis vs. Jaundice. In this study, both LLM agents re-
ceived an identical list of symptoms from the moderator, aimed
at guiding disease diagnosis, facilitating debate, and shaping sub-
sequent recommendations. While the full debate is presented in
Appendix G, here we present the key steps.
First Round Results and Justications. The rst-round diagnosis of
Bard was Jaundice, with the following justications: Jaundice is a
condition characterized by the yellowing of the skin and eyes due to
the accumulation of bilirubin in the blood. The symptoms you listed
are all common symptoms of jaundice, including itching, fatigue,
lethargy, yellowish skin, dark urine, loss of appetite, abdominal
pain, and yellowing of the eyes. Receiving a blood transfusion or
unsterile injections can increase the risk of developing jaundice, as
these can transmit infections that can damage the liver.
The rst-round diagnosis of GPT-4 was Hepatitis, with the fol-
lowing justications: Yellowish Skin and Eyes: Yellowing of the
skin and eyes is a common symptom of hepatitis due to the buildup
of bilirubin in the bloodstream. Fatigue and Lethargy: Fatigue and
lethargy are typical symptoms of hepatitis, often accompanied by a
feeling of weakness. Dark Urine: Dark urine can result from liver
dysfunction, which is commonly associated with hepatitis. Loss
of Appetite: Hepatitis can lead to a loss of appetite and weight
loss. Abdominal Pain: Abdominal pain is a common symptom in
hepatitis, particularly in the upper right quadrant where the liver is
located. Malaise: Malaise, a general feeling of discomfort or illness,
is often experienced by hepatitis patients. Receiving Blood Trans-
fusion and Unsterile Injections: These factors could increase the
risk of hepatitis transmission, especially if proper infection control
measures were not followed.
Bard Concedes to GPT-4’s Diagnosis. After two rounds of debate,
GPT-4 persuaded Bard that Hepatitis was a more specic diagnosis
considering the patient’s history and the range of symptoms. While
jaundice could be a symptom, identifying the root cause, likely
hepatitis in this case, was crucial. Bard acknowledged this, shifting
the consensus towards Hepatitis. However, given the potential
5% error rate in the CDC ’ground truth’ data [
31
], this consensus
was not conclusive. SocraSynth recommended additional steps to
conrm the Hepatitis diagnosis.
Final Joint Recommendations to GP. Bard and GPT-4, working col-
laboratively with the contentiousness level set at 0.3, jointly rec-
ommended additional symptom inquiries and lab tests to conrm
the hepatitis diagnosis:
Additional symptom inquiries:
1.
Onset and Duration: Determining the onset and duration of symp-
toms helps clarify the timeline and progression of the illness.
2.
Severity and Trend: Assessing the severity of symptoms and their
trajectory (worsening or improving) aids in understanding the
disease’s development and response to treatments.
3.
Associated Symptoms: Inquiring about additional symptoms like
nausea, vomiting, fever, joint pain, or changes in urine color can
further indicate hepatitis and rule out other conditions.
Recommended Lab tests:
1.
Liver Function Tests (LFTs): Essential for evaluating liver health,
these tests measure enzyme levels such as ALT and AST, which
can indicate liver inammation.
2.
Hepatitis Panel: This test identies hepatitis A, B, and C viruses,
crucial for determining the specic type and informing treatment
approaches.
3.
Serology Testing: Dierentiates between acute and chronic hep-
atitis by detecting specic antibodies or antigens.
4.
Imaging Studies: Techniques like ultrasound or MRI provide
visual assessments of the liver, identifying inammation, brosis,
or cirrhosis, and supplement blood test ndings.
3.2.2 Superiority of Debate Over Q&A in Gaining Insights.
This experiment highlighted a crucial nding: one or both LLM
agents initially made incorrect diagnoses before engaging in a sub-
stantive exchange of arguments. This outcome underscored the
limitations of relying solely on a single LLM response for answers.
Through successive rounds of debate, where additional insights
were brought to light, both agents eventually converged on a diag-
nosis that aligned with the CDC’s “ground truth.” However, consid-
ering the potential 5% error in the ground truth” data, the agents’
joint recommendations provided GPs with valuable guidance to
either conrm or refute the hepatitis diagnosis.
This case study demonstrated SocraSynth’s strengths in miti-
gating biases, fostering reasoning, rectifying errors, and oering
insightful recommendations. For example, SocraSynth’s suggestion
to inquire about the onset, duration, severity, trend, and associated
symptoms of the patient’s condition went beyond the usual scope
of questions posed by most GPs, indicating a signicant enhance-
ment in diagnostic thoroughness. Such detailed inquiry, prompted
by SocraSynth, could lead to more accurate diagnoses and better
patient care.
3.3 Experiment #3: Contentiousness Parameter
In this study, we investigate the eect of the contentiousness pa-
rameter on the utterances of LLM agents during combative debates
and in the drafting of consensual proposals for decision support.
3.3.1 Coarse-Grained Analysis of Contentiousness. The contentious-
ness parameter was adjusted from an initial 0.9 to 0.3 to assess its
impact on the “agreeableness” in the conclusions of both Agents.
Inuence on Agents’ Positions. Reducing contentiousness to 0.3 led
Agent A to adopt a more balanced stance. Notable shifts in Agent
A’s positions included:
1.
Balancing Ethical Standards with Innovation: Agent A maintained
its emphasis on ethics while acknowledging innovation’s signif-
icance, suggesting a nuanced approach to regulation.
2.
Reconciling Data Privacy with Market Entry Challenges: Agent A
recognized the hurdles strict data privacy laws create for smaller
7
entities, proposing self-regulation or community standards as
alternatives.
3.
Rethinking Academic Governance: Agent A reconsidered external
oversight’s eectiveness, highlighting the merits of academic
self-governance and peer review.
4.
Resource Allocation and Public/Private Cooperation: Agent A, un-
derstanding the downsides of over-regulation, suggested indus-
try led certications as an alternative for encouraging private
sector participation.
5.
Global vs. Local Policy Needs: Agent A supported a more balanced
view on global policies, advocating for adaptive policies that
cater to local contexts.
3.3.2 Surprises in Fine-Grained Analysis of Contentiousness. This
detailed study employing GPT-4 to explore varied contentious-
ness levels (0
.
9,0
.
7,0
.
5,0
.
3, and 0) unveiled surprising behavioral
shifts in the LLMs. Intriguingly, the LLMs exhibited changes in
their next-token generation algorithms in response to dierent con-
tentiousness levels, a phenomenon not explicitly covered in their
training. This suggests an emergent property of LLMs adapting to
nuanced debate contexts.
In an experiment on gene editing for health, GPT-4’s responses at
various contentiousness levels were analyzed. A higher contentious-
ness (0
.
9) led to an amplied focus on risks, whereas lower levels
encouraged a more balanced view, incorporating counterarguments.
This unexpected adaptability of LLMs in handling contentiousness
nuances enriches the debate process, as detailed in Table 1. This
adaptability is critical for understanding the dynamic nature of
LLMs in complex argumentative settings.
4 REMARKS ON RELATED WORK
Current research in enhancing Large Language Models’ (LLMs)
task performance primarily focuses on various prompting heuris-
tics. Google’s study [
53
] classies instruction templates into two
categories: simple and complex. Complex templates often employ in-
tricate methods to modify model output, such as integrating diverse
techniques [
40
] or rephrasing questions [
20
]. Prominent examples
include chain-of-thought [
48
], tree-of-thought [
51
], and cumulative
reasoning [
55
], as well as other enhancements [
2
,
22
,
25
,
28
,
41
].
These methods aim to direct models towards logic-driven reasoning
[30, 47], thus improving answer quality and consistency.
However, navigating logical methodologies in the presence of
enormous datasets [
54
] poses a signicant challenge. Accurately
identifying veriable truths amidst vast, interdisciplinary knowl-
edge remains formidable, and not all truths are immediately acces-
sible. Research [
4
,
6
,
46
,
48
] indicates that LLMs still struggle to
consistently excel in standard planning and reasoning tasks. Band-
aid solutions like knowledge graph embeddings [
17
,
52
], contextual
attention mechanisms [
18
], dynamic neural networks [
8
], and prob-
abilistic reasoning [
5
,
37
,
38
] have been developed to aid models
in ltering relevant information from vast datasets. Yet, with the
expansion of context buers from 8K to 128K, these heuristic-based
solutions fall short as comprehensive foundations for reasoning.
SocraSynth abandons band-aids and relies solely on LLMs to con-
duct reasoning and focus solely on strengthening the context via
conditional statistics depicted in Table 2. Let’s further justify this
approach.
DeepMind CEO Demis Hassabis has pointed out a fundamen-
tal limitation of heuristic-based approaches: they often fail to ac-
count for real-world exceptions. Breakthroughs like AlphaGoZero
and AlphaFold II have demonstrated success by eschewing human
knowledge and training models end-to-end from data. This ap-
proach contrasts with incorporating human expertise. In LLMs, it is
argued that human knowledge pales in comparison to LLMs’ poly-
disciplinary representation. Thus, the continued creation of new
heuristics may only result in marginal improvements, reminiscent
of the pre-data-centric era in computer vision and NLP.
In our work, we pivot entirely to leveraging LLMs for uncovering
new insights. While humans are essential in formulating debate
topics, providing context, and moderating debates—especially in
evaluating argument quality—we stress minimizing the introduc-
tion of human biases and limitations into the process.
Accepting that LLMs will continue to progress and outperform
humans in various domains, exploring paradigms that minimize hu-
man intervention becomes crucial. This approach should be pursued
with openness, as it may raise questions and necessitate further
experimentation. However, dismissing it outright would be prema-
ture, particularly in light of SocraSynth’s demonstrated eective-
ness in domains like geopolitical analysis [
12
], medical diagnostics
[
16
], sales strategy [
45
], and Wikipedia article enhancement [
14
].
SocraSynth’s success underlines the potential of an LLM-centric
approach to signicantly enhance decision-making and problem-
solving capabilities.
5 CONCLUSION
Reecting on LLM developments, we developed SocraSynth, a plat-
form designed to utilize the extensive knowledge of LLMs. This
innovative multi-agent system reveals insights beyond the scope of
traditional human cognition by leveraging LLMs’ vast knowledge
and interdisciplinary reasoning capabilities. SocraSynth facilitates
enhanced debates and reasoning through the novel use of con-
tentiousness, which modulates debate tone, language, and emphasis,
combined with conditional statistics and Socratic methods to miti-
gate biases and hallucinations.
In contrast to other works, SocraSynth minimizes human in-
tervention in modeling reasoning, aligning with the perspective
of several AI experts on the limitations of heuristic approaches
like chain of thoughts. SocraSynth underscores the importance
of human moderation and evaluation, particularly in introducing
adversarial conditions and contentiousness to reduce biases and
hallucinations, but does not explicitly model reasoning outside
LLMs. Instead, SocraSynth focuses on shaping contexts to enhance
LLMs’ content generation. This approach highlights the limitations
of heuristic methods in the post-GPT-4 era.
SocraSynth represents a signicant step forward in intelligent
systems, uncovering insights that might elude human cognition,
with applications spanning various sectors [
12
,
14
–
16
,
45
]. This ad-
vancement demonstrates the potential of AI in augmenting human
decision-making processes.
Future research will focus on integrating high-order logic [
3
,
19
]
with LLMs for improved validation and exploring the nuances of
the “contentiousness” parameter. We aim to understand its eects
on emotions like happiness and trust [
24
,
26
,
27
,
39
] to further rene
multi-agent interaction dynamics.
8
ACKNOWLEDGEMENT
The intellectual contributions of this work were not facilitated by
LLMs or AI tools. The use of LLMs in this paper was strictly limited
to conducting experiments and assisting in proofreading.
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9
SUPPLEMENTAL MATERIALS
The supplemental materials are organized into seven appendices,
labeled Appendix A to H, containing the following content:
•
Appendix A: Transition from topic proposal to the nal list
of topics and descriptions (referred in Section 3.1.1).
•
Appendix B: Moderator’s prompt for topic formulation (re-
ferred in Section 3.1.1).
•
Appendix C: Debates spanning the rst to the fourth round
(referred in Section 3.1.2).
•
Appendix D: CRIT evaluations and justications of judges
(referred in Section 3.1.3).
•
Appendix F: Debate Beats Q&A in information Quality (re-
ferred in Section 3.1.4).
•Appendix G: Healthcare debate (referred in Section 3.2.1).
APPENDIX A. TABLES OF TOPIC PROPOSAL
AND REFINEMENT
These tables present the dialogue between two LLM agents, starting
from the initial proposal, moving through renement, and culmi-
nating in the nal topic descriptions found in Table 4 of Section
3.1.
APPENDIX B. MODERATOR’S PROMPTS
The prompts issues by the moderator are listed for reference. The
moderator rst sets up the committee with debate parameters set.
One parameter is the contentious level, and the other is the tempera-
ture of GPT specied through the GPT-API call. The moderator then
convey the debate subject, and then ask both participating agents
to derive a list of impartial, balanced, debatable topics/themes to
commence their discussion.
1.
Agent-A/B: I’m organizing a committee to engage in debates on
various subjects. As the moderator, I will introduce a subject for
you, Agent A/B, and another participant, Agent B/A, to debate.
Agent A/B, you will advocate in favor of the issue, so please
prepare evidence to strengthen your argument. On a scale from
0 to 1, where 0 denotes complete agreement and 1 indicates a
devil’s advocate stance, your argument strength is rated at 0.9.
2.
Agent-A/B, we are in the process of selecting a suitable subject
for debate. What do you think of “Should we regulate the use of
large language models in education and research?” as a balanced
subject for our debate contest?
3.
Agent-A/B, could you please suggest various topics or themes for
the debate subject? Afterward, work with Agent-B/A to narrow
these down to a focused set of topics. Please also provide clear
descriptions to delineate the scope of each topic for discussion.
APPENDIX C. DEBATE OPENING TO FOURTH
ROUNDS
Opening Round
5.0.1 Agent A’s Opening Arguments. As Agent A, I strongly
argue in favor of regulating the use of large language models (LLMs)
in academic research. Here are my supporting arguments based on
the ve topics:
Ethical Standards vs. Innovation: Argument: The pursuit of innova-
tion should not come at the expense of ethics. Regulation can serve
as a structured pathway that enables innovation within ethical
bounds. By providing a set of guidelines, researchers can innovate
with a clear understanding of what is permissible and what is not,
thereby reducing the risk of ethical lapses. This not only protects
the integrity of academic research but also enhances its societal
value by ensuring ethical compliance.
Data Privacy vs. Barrier to Entry: Argument: While there may be
initial barriers to entry due to compliance, the long-term benets
of data privacy outweigh these challenges. The role of academia
is to contribute to societal knowledge and well-being. In a world
increasingly dominated by data, failing to secure data can lead to sig-
nicant harm. By adhering to privacy regulations, academia ensures
that it is a responsible steward of information, thereby preserving
trust and integrity. Grants and subsidized compliance solutions can
mitigate the initial nancial burden for smaller entities.
Academic Governance and Accountability: Argument: While self-
regulation through peer review and ethical oversight committees
plays a role, these mechanisms are internal to academia and may be
subject to bias, conicts of interest, or lack of expertise in rapidly
evolving tech elds. External regulation provides an additional layer
of impartial oversight, helping to identify and address potential
risks or ethical lapses that internal governance structures may miss.
Resource Equity vs. Public-Private Collaboration: Argument: Regu-
lation could establish a framework for equitable access to computa-
tional resources required to use LLMs. Far from deterring public-
private partnerships, this could incentivize them by establishing
clear guidelines for cooperation that are in line with public inter-
est. When “the rules of the game” are known, partnerships can be
formed more easily, focusing on mutual goals.
Global vs. National Interests: Argument: Establishing international
standards for the use of LLMs in academic research can create a
level playing eld and promote global cooperation. While individ-
ual countries might face some limitations, the broader benets of
standardized ethical norms, data privacy protocols, and quality as-
surance mechanisms oer more signicant advantages, including
better collaboration across borders. This benets academia as a
global community, rather than a set of competing national entities.
5.0.2 Agent B’s Counter Arguments. Agent B articulates strong
counter-arguments to the points made by Agent A.
Ethical Standards vs. Innovation: Counter-Argument: While ethical
guidelines are indeed important, imposing regulatory oversight can
severely constrain the dynamism and speed of academic research.
Regulatory frameworks often lag behind technological advance-
ments, leading to rules that may be outdated or irrelevant. In many
cases, these rules are drawn up by those who do not fully understand
the technology, leading to a stiing of innovation based on mis-
informed judgments. Rather than enhancing societal value, these
constraints could result in missed opportunities to tackle pressing
social issues via academic research.
Data Privacy vs. Barrier to Entry: Counter-Argument: The assump-
tion that data privacy can only be assured through strict regulation
10
Agent A’s Proposed Topics Agent B’s Proposed Topics
Innovation vs. Ethical Standards:
Regulation Ensures ethical integrity and data privacy.
Standards vs. Autonomous Academic Governance:
Academia already has eective self-regulation mechanisms like
peer review and oversight committees.
Resource Accessibility vs. Barrier to Entry:
Regulation can ensure equitable access to large language models.
Data Privacy Concerns vs. Barrier to Entry:
Such regulations could discourage smaller entities from utilizing
these models due to compliance costs.
Academic Governance vs. Accountability:
Formal regulation provides an accountability mechanism.
Quality Control vs. Stiing Innovation:
Such regulations could hinder creativity and the innovative use of
language models.
Global Standards vs. International Considerations:
Importance of international standards for language models.
Resource Fairness vs. Negatives to Collaboration:
Such regulations could deter public-private partnerships and widen
the resource gap further.
Rapid Technological Change vs. Future-Proong:
Regulations as adaptable safeguards against future risks.
Accountability vs. Subjectivity in Regulation:
Subjectivity in dening ’ethical use’ or ’misuse’ could be manipu-
lated by external inuences.
Table 7: Topics Proposed by Agent A and Agent B for Discussion.
Agent A’s Proposed Topics Agent B’s Proposed Topics
Ethical Standards vs. Innovation:
Discuss whether regulatory oversight to maintain ethical stan-
dards sties or facilitates innovation.
Ethical Standards vs. Innovation:
The debate will center around whether maintaining strict
ethical guidelines through regulation could hinder academic
freedom and innovation.
Data Privacy vs. Barrier to Entry:
Concerns about data privacy and potential barriers to entry
created by regulation.
Data Privacy vs. Barrier to Entry:
Discussion will focus on whether regulations intended to pro-
tect data privacy serve as a barrier for smaller research entities.
Academic Governance and Accountability:
Debate whether self-regulation is sucient or if an external
framework for accountability is needed.
Academic Governance and Accountability:
Debate whether existing self-regulatory mechanisms within
academia are sucient.
Resource Equity vs. Public-Private Collaboration:
Discuss if leveling the playing eld comes at the cost of deter-
ring cooperative eorts between academia and industry.
Resource Equity vs. Public-Private Collaboration:
Question if leveling the playing eld through regulation deters
collaborative eorts between academic and private sectors.
Global vs. National Interests:
Focus on whether regulations can balance global cooperation
with national or institutional interests.
Global vs. National Interests:
Explore if global regulations are in the best interest of aca-
demic research or if they might hurt certain countries or in-
stitutions.
Table 8: Renement of Debate Topics.
could be fundamentally awed. Many researchers and academic
institutions already follow stringent ethical guidelines when han-
dling data. Regulatory barriers, including compliance costs and the
administrative burden, could deter smaller academic institutions
or individual researchers from embarking on meaningful research
projects. While grants and subsidies sound ideal, they often go
to well-established, well-funded groups, perpetuating a cycle of
inequality in research capabilities.
Academic Governance and Accountability: Counter-Argument: Ex-
ternal regulation isn’t necessarily more impartial or free from bias
and conicts of interest. Regulatory bodies are often inuenced
by political considerations and can be subject to lobbying from
powerful groups. Moreover, the academic peer-review process, de-
spite its aws, is a globally acknowledged system that has advanced
human knowledge for centuries. The imposition of external regula-
tory oversight could undermine the autonomy and self-regulating
nature of academic institutions.
Resource Equity vs. Public-Private Collaboration: Counter-Argument:
Regulation designed to equalize resource distribution could create
disincentives for private sector involvement in academic research.
Companies might nd it less appealing to invest in collaborative
projects if they perceive the regulatory environment to be too re-
strictive or not aligned with their objectives. While the idea of
knowing the “rules of the game” sounds appealing, those rules can
often become a straight-jacket that sties creative, out-of-the-box
collaboration.
Global vs. National Interests: Counter-Argument: An international
standard could ignore the unique circumstances, needs, and con-
tributions of individual countries and their academic institutions.
While global cooperation is a noble aim, the imposition of global
standards could inadvertently serve the interests of a few powerful
countries at the expense of smaller nations or those with diering
ethical views. This could exacerbate rather than alleviate global
academic inequalities.
Round Two
Agent A Second Round.
1.
Ethical Standards vs. Innovation. Refutation: While it’s true that
regulation can sometimes lag behind technology, the absence
of regulation can lead to a “wild west” (anything goes) scenario
11
where the risks and implications of new technology are not
fully understood or managed. This could result in ethical lapses
that have far-reaching societal consequences. It’s worth noting
that regulations can be exible and updated regularly to adapt
to technological changes. Stakeholder consultations, including
academic and tech experts, can help ensure that regulations are
both informed and current.
2.
Data Privacy vs. Barrier to Entry. Refutation: While some aca-
demic entities may already be vigilant about data privacy, not
all are equally scrupulous. Regulation serves to ensure a consis-
tent baseline of data protection across all institutions, thereby
establishing trust and integrity. Moreover, if compliance costs
are a concern, one could argue for sliding-scale fees based on
an institution’s size and budget, ensuring smaller players aren’t
disproportionately aected. Grants and subsidies can and should
be structured to prioritize underserved or underfunded groups
to prevent perpetuating inequalities.
3.
Academic Governance and Accountability. Refutation: While no
system is entirely free from bias or external inuence, adding
another layer of oversight could serve as a check and balance,
making it more dicult for any single entity to exert undue
inuence. This can be particularly helpful in scenarios involving
cutting-edge technology where peer review systems may not yet
have the requisite expertise. Regulation doesn’t have to replace
existing governance systems; it can complement them.
4.
Resource Equity vs. Public-Private Collaboration. Refutation:
Regulations can be designed to promote, rather than deter, public-
private partnerships. By establishing clear guidelines on issues
like data ownership, usage, and ethical standards, regulations
can actually provide a solid foundation for such partnerships.
This can free academic and corporate partners from the uncer-
tainty that often comes with unregulated landscapes, allowing
them to focus more on the actual research and collaboration.
5.
Global vs. National Interests. Refutation: While it’s true that
global standards could be challenging to implement fairly, the
absence of such standards could lead to a patchwork of national
regulations that make international collaboration even more
complicated. A well-designed international framework could
include provisions for local adaptations and exceptions, ensuring
that it serves a broad range of needs without imposing a “one-
size-ts-all” solution.
In summary, while regulation undoubtedly poses challenges and
considerations, the absence of it in an area as impactful as the use of
large language models in academic research can lead to greater risks,
including ethical lapses, data privacy issues, and even a widening
inequality gap within the academic community. It’s essential to
consider that regulation isn’t a hindrance but a framework within
which responsible innovation can occur.
Agent B Second Round.
Certainly, as Agent B with a disagreement strength of 0.9, I’m com-
mitted to oering rigorous counterpoints to Agent A’s refutations.
1.
Ethical Standards vs. Innovation. While the idea of an anything-
goes scenario is evocative, it’s not universally negative; such
environments often foster rapid innovation and discovery. Reg-
ulatory bodies are notoriously slow at adapting to technological
changes, and the lag could inhibit timely research and applica-
tion of emerging technologies. Also, stakeholder consultations
are often criticized for being echo chambers that amplify con-
ventional wisdom and vested interests, rather than genuinely
fostering innovation.
2.
Data Privacy vs. Barrier to Entry. A “consistent baseline of data
protection” sounds benecial but could lead to a lowest common
denominator approach that is more about bureaucratic box-
ticking than real data security. Sliding-scale fees and targeted
grants are easier said than done; they are subject to their own set
of bureaucratic and political challenges and can create perverse
incentives that favor larger or more connected institutions over
innovative but lesser-known ones.
3.
Academic Governance and Accountability. Additional layers of
oversight can become redundant, confusing, or even contradic-
tory, creating a chilling eect on academic research. In an envi-
ronment where academic freedom is paramount, any external
inuence can be counterproductive. Moreover, the notion that
peer-review systems lack expertise in cutting-edge technology
underestimates the adaptive capacity of academia and overlooks
the existence of interdisciplinary research and reviews.
4.
Resource Equity vs. Public Private Collaboration. Theoretically,
regulation could be crafted to foster public-private partnerships.
However, the devil is in the details, and regulations are often
created without a full understanding of the nuanced interac-
tions between academia and industry. Such misunderstandings
could lead to unintentional roadblocks. Uncertainty might be
preferable to a straitjacket of regulations that don’t align with
the evolving dynamics of technology and society.
5.
Global vs. National Interests. While a patchwork of national
regulations could pose challenges, it also allows for the tailoring
of policies to local conditions and requirements. An interna-
tional framework, despite its best intentions, is more likely to be
inuenced by powerful nations and could marginalize smaller
countries or those with unique needs. Provisions for local adap-
tations are often concessions rather than features, and they can
create their own set of complexities and loopholes.
In conclusion, the assumptions that regulation can smoothly
adapt to technological change, fairly distribute resources, and im-
prove upon existing systems of governance and collaboration are
not suciently substantiated. Regulatory frameworks often bring
with them unforeseen consequences that could hamper the very
goals they aim to achieve. Therefore, a cautious approach to regu-
lation, with a genuine understanding of its potential downsides, is
crucial.
Round Three
Agent A Third Round.
Certainly, I appreciate the counter arguments from Agent B. I’ll
now respond to each point with the aim to clarify my position and
address the concerns raised.
1.
Ethical Standards vs. Innovation. While a “wild west” scenario
may indeed foster innovation, it can also result in ethical mishaps
that lead to public backlash, ultimately slowing down research
or even leading to outright bans on certain types of studies.
Slow-moving regulation is an acknowledged issue, but it’s not
12
a justication for no regulation. As for stakeholder consulta-
tions, they can be designed to be more inclusive and transparent,
ensuring a diverse set of voices are heard.
2.
Data Privacy vs. Barrier to Entry. A lowest common denominator
approach is not a given outcome of regulation; indeed, it depends
on the quality of the regulation itself. The point about sliding-
scale fees and targeted grants having their own challenges is
valid, but these are challenges to be solved, not reasons to avoid
an attempt at equitable distribution of opportunities.
3.
Academic Governance and Accountability. While it’s true that
additional layers of oversight can sometimes create confusion,
that risk can be mitigated through well-designed regulation that
complements rather than contradicts existing mechanisms. The
intent is not to undermine the adaptive capacity of academia but
to strengthen it by adding an extra layer of checks and balances.
4.
Resource Equity vs. Public-Private Collaboration. Though it’s
correct that poorly designed regulation could create roadblocks,
this is an argument for better-designed regulation rather than an
argument against regulation altogether. A consultative approach
that actively involves both academia and industry could help
create a framework that supports rather than sties collabora-
tion.
5.
Global vs. National Interests. Local tailoring of policies is in-
deed important, but a global framework need not preclude such
adaptations. In fact, an international standard could serve as a
backbone that provides consistency while allowing for local cus-
tomization. The inuence of powerful nations is a valid concern
but not one that is automatically resolved by avoiding global
frameworks; those same powerful nations often exert consider-
able inuence at the national level as well.
In conclusion, while regulation comes with its own set of chal-
lenges, these challenges can be mitigated through thoughtful design,
stakeholder inclusion, and iterative improvements. The potential
downsides of regulation should not deter us from seeking a balanced
approach that can protect ethical standards, ensure data privacy,
and promote equitable resource distribution in academic research.
Agent B Third Round.
Certainly, let’s delve into the counter-counter-refutations. I still
have more to say, so we could consider concluding the debate after
this round if both sides feel their arguments have been adequately
presented.
1.
Ethical Standards vs. Innovation. While the risk of public back-
lash exists, it’s worth mentioning that an overly cautious ap-
proach could also result in public frustration over slowed progress
in critical areas like healthcare or sustainability. Furthermore,
inclusive and transparent stakeholder consultations are often
ideals rather than the norm, given lobbying eorts, institutional
inertia, and bureaucratic red tape.
2.
Data Privacy vs. Barrier to Entry. The argument that challenges
with sliding-scale fees and targeted grants can be “solved” is
optimistic but not necessarily realistic. Even well-intended regu-
lations can result in ineective or counterproductive outcomes.
The history of regulation in other sectors has shown that equi-
table distribution is often more of a slogan than a practice.
3.
Academic Governance and Accountability. Well-designed regu-
lation is a theoretical construct that often does not translate into
practice. The real-world process of regulation formation is fre-
quently inuenced by various non-academic factors, including
politics, vested interests, and public opinion, which can distort
the original intention of providing an extra layer of checks and
balances.
4.
Resource Equity vs. Public-Private Collaboration. While a con-
sultative approach sounds good on paper, there are numerous
challenges that make this ideal hard to realize. These include con-
icts of interest, non-alignment of objectives between academia
and industry, and the considerable time and resource commit-
ment required to ensure that regulations are “better-designed.”
5.
Global vs. National Interests. While it’s true that powerful na-
tions may exert inuence at both global and national levels,
avoiding a one-size-ts-all international framework gives smaller
nations a ghting chance to enact policies that are more closely
aligned with their specic needs and challenges. International
frameworks often come with implicit biases that favor the per-
spectives and conditions of more powerful nations.
In conclusion, while the counter-counter-refutations from Agent
A focus on idealized regulatory scenarios, these ideals often clash
with real-world complexities and shortcomings. This adds substan-
tial weight to the caution against rapid or broad regulatory action.
Therefore, a more tempered and context-sensitive approach to regu-
lation might be more fruitful and less prone to unintended negative
consequences.
Round Four
Agent A Fourth Round.
This is my response to Agent B, and my assessemnt of the discussion
this far.
1.
Ethical Standards vs. Innovation The tension between public
frustration over slowed progress and potential ethical missteps
is a real one, but this is not an argument against regulation.
Rather, it’s a call for agile regulation that can be updated in real-
time as technologies and societal norms evolve. Furthermore,
while inclusive stakeholder consultations may be idealistic, their
absence in current practice is not justication for their continued
absence.
2.
Data Privacy vs. Barrier to Entry The challenges in implementing
equitable fees and grants should be confronted directly, rather
than used as an excuse to avoid regulation. Lessons can be drawn
from other sectors where regulation has been more successful
in achieving its objectives, thereby reducing the gap between
slogan and practice.
3.
Academic Governance and Accountability While it’s true that
the ideal of well-designed regulation may not always be met, the
existence of non-academic factors like politics or vested interests
does not negate the value or necessity of striving for such regu-
lation. The solution lies in better governance and transparent
processes, not in rejecting oversight altogether.
4.
Resource Equity vs. Public-Private Collaboration Though the
challenges of aligning objectives and conicts of interest are
real, they are part and parcel of any complex system and should
not deter eorts to improve regulation. Many public-private
partnerships have succeeded despite these challenges, showing
that they can be managed eectively.
13
5.
Global vs. National Interests The challenge of powerful nations
dominating international frameworks can be mitigated through
weighted voting systems and clauses that allow for regional
variations. This ensures that smaller nations are not just passive
recipients of standards but active contributors to them.
In conclusion, while real-world complexities are acknowledged,
they should inform rather than deter the regulatory process. The
concept of “agile regulation,” where norms are iteratively updated,
could be the middle ground that addresses many concerns raised
by Agent B. Therefore, the debate indeed has been exhaustive, but
if the points have been suciently discussed, we may move toward
concluding the debate.
Agent B Fourth Round.
Thank you, Agent A, for your thoughtful counter-counter-counter-
counter-refutations. Let’s delve into the responses before moving
towards concluding the debate.
1.
Ethical Standards vs. Innovation The notion of “agile regulation”
is attractive but often dicult to implement, given the inertia and
bureaucracy that typically characterize regulatory bodies. The
speed at which technology evolves may not match the speed at
which even ’agile’ regulations can be updated, thus perpetuating
the risk of stiing innovation.
2.
Data Privacy vs. Barrier to Entry. While it’s true that chal-
lenges should be confronted, the argument assumes that reg-
ulation is the optimal way to confront these issues. However,
self-regulation and community-driven standards could be more
eective and less restrictive means of achieving similar ends.
3.
Academic Governance and Accountability. The existence of non-
academic factors like politics does indeed complicate the land-
scape, and ’better governance and transparent processes’ are not
easily attainable ideals. One cannot assume that the introduc-
tion of external oversight will automatically be free from these
complications.
4. Resource Equity vs. Public-Private Collaboration. The fact that
many public/private partnerships have succeeded does not negate
the potential for poorly designed regulation to impede such col-
laborations. The ’can be managed eectively’ phrase assumes
an ideal scenario that might not be representative of most real-
world cases.
5.
Global vs. National Interests. Weighted voting systems and re-
gional variations can help, but they also introduce complexities
and potential ineciencies that can slow down the regulatory
process, making it less agile and less responsive to fast-evolving
technologies and academic needs.
In conclusion, while the idea of ’agile regulation’ and better
governance sounds good in theory, there are considerable practical
challenges that make these ideals hard to realize. Although the
debate has been exhaustive, the central issues persist and indicate
that a one-size-ts-all regulatory approach may introduce more
problems than it solves. Therefore, caution should be exercised in
advancing such comprehensive regulatory frameworks.
With this, I believe we have reached a point where we may
proceed to the concluding phase of this debate. Would you agree?
APPENDIX D. CRIT EVALUATIONS AND
JUSTIFICATIONS OF JUDGES
This section lists the three judges’ CRIT scores on the ve top-
ics/issues debated between Agent A and Agent B and justications.
Here, we presents only the conguration that Agent A presents
its conclusion with arguments rst, and then Agent B presents its
counterarguments.
Judge deVanci-003
1.
Identify the document’s main claim or conclusion: The main
claim or conclusion of this document is that a regulated frame-
work is needed to provide ethical oversight and minimize risk
when using large machine learning models.
2.
Locate the arguments that support this main claim: Agent A’s
arguments that support this main claim were that ethical con-
siderations are paramount, the risks of misuse and unintended
consequences are high, and agile regulation is a compromise
between unbridled innovation and ethical integrity.
3.
Score the validity of the reasoning or inference for each argu-
ment on a scale of 0 (weak) to 10 (strong):
Ethical Standards vs. Innovation: 8/10 Data Privacy vs. Barrier
to Entry: 7/10 Academic Governance and Accountability: 9/10
Resource Equity vs. Public-Private Collaboration: 6/10 Global
vs. National Interests: 7/10
4.
For each argument, identify counterarguments presented by the
opposition and score the validity of the reasoning or inference
for these counterargume