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SocraSynth: Socratic Synthesis for Reasoning and Decision Making
Edward Y. Chang
echang@cs.stanford.edu
Computer Science, Stanford University
September 7, 2023
ABSTRACT
SocraSynth, a portmanteau of "Socratic Synthesis" and "Socratic
Symposium," is a digital forum designed to leverage the exten-
sive knowledge base of foundation models like GPT-4, PaLM, and
LLamA. It aims to uncover insights and knowledge that may be
beyond human awareness. The platform’s unique strength lies in its
interdisciplinary nature, allowing foundation models to formulate
questions and issues that might elude human intuition, thereby
facilitating the emergence of novel perspectives and insights. The
SocraSynth workow is divided into two primary phases: knowl-
edge generation and evaluation. In the generation phase, a human
moderator assembles a committee featuring agent representatives
from a selected foundation model. The moderator outlines a topic
for discussion and debate, setting the context and parameters for
the discourse that follows. Agents then engage in a structured de-
bate, oering initial arguments, counter-arguments, and concluding
statements. Subsequently, SocraSynth transitions to its evaluation
phase, employing the CRIT algorithm—a methodology rooted in
Socratic reasoning and formal logic. This comprehensive evalua-
tion serves as an invaluable resource for decision-makers aiming
to make informed, judicious choices. Through case studies, we
demonstrate how SocraSynth enables rigorous research and rea-
soning, eectively supporting critical decision-making processes
in domains such as management and governance.
KEYWORDS
critical thinking, CRIT, GPT, foundation model, large language
model, prompt template, Socratic method, SocraSynth
1 INTRODUCTION
The importance of critical thinking and analytical reasoning can
be traced back to the intellectual legacies of ancient philosophers
like Socrates and Plato [
6
,
7
]. These foundational skills are essen-
tial for high-quality reasoning and decision-making and have been
the subject of scholarly investigation for years. Building upon this
philosophical tradition, this paper introduces the SocraSynth frame-
work. Designed with the Socratic Method at its core, SocraSynth
aims to amplify both reasoning and decision-making capabilities
across a range of disciplines.
SocraSynth operates in two primary phases: the knowledge gen-
eration (or generative) phase and the evaluation (or evaluative)
phase. In the generative phase, a human moderator assembles a
debate committee consisting of two virtual agents. Each agent is
powered by a Foundation Model, such as GPT-4 [
3
], LaMDA [
15
],
or Llama [
16
]. The moderator sets the debate’s initial subject matter
and parameters but does not contribute to the content.
The unique strength of employing well-trained Foundation Mod-
els lies not only in their comprehensive knowledge base but also in
their polydisciplinary reasoning capabilities [
4
]. These capabilities
enable the agents to bring in a wide array of perspectives that are
often overlooked by human analysts. A tunable parameter, known
as “contentiousness,” is used to guide the debate’s dynamics. Ini-
tially set high to encourage confrontation, it is lowered during the
evaluative phase to promote collaboration and consensus-building.
In the evaluative phase, SocraSynth employs a panel of virtual
judges, each powered by a dierent Foundation Model, ensuring
both diversity and balanced assessments. We utilize the Critical
Inquisitive Template (CRIT) algorithm [
5
], which employs Socratic-
method based [
19
,
21
] reasoning for an objective evaluation, identi-
fying the strengths and weaknesses of each argument and thereby
providing a comprehensive quality score.
After both phases are complete, the contentiousness parameter
is further reduced to facilitate the drafting of a balanced proposal.
This proposal is then submitted to a human executive committee
for nal decision-making.
The remainder of this paper is organized into ve sections, elab-
orating on each of these aspects and oering rigorous experimen-
tal validation of the framework’s ecacy. Section 2 explores how
SocraSynth assembles a debate committee and selects balanced
discussion topics. Section 3 covers the generative phase of the de-
bate, while Section 4 presents the CRIT algorithm employed during
the evaluative phase. Section 5 provides experimental results that
examine the eects of varying contentiousness levels and oers an
assessment of debate quality based on the CRIT algorithm. Finally,
we oer our concluding remarks in Section 7.
The contributions of this paper can be summarized as follows:
1.
The development and introduction of the SocraSynth framework,
uniquely engineered to leverage the polydisciplinary reasoning
capabilities of Foundation Models in enhancing reasoning and
decision-making processes.
2.
An exploration and validation of the “contentiousness” parame-
ter, demonstrating its role in shaping reasoning quality through
dierent phases of confrontation and collaboration.
3.
Rigorous experimental validation using the CRIT algorithm and
multiple Foundation Models, ensuring an independent, objective,
and diversied assessment of debate quality.
4.
Presentation of case studies that substantiate the framework’s
capability in various application domains, including but not
limited to management and governance. These serve as practical
demonstrations of how SocraSynth can be eectively integrated
into real-world decision-making processes.
2 COMMITTEE AND TOPIC FORMING
In this study, the SocraSynth committee is exemplied by a trio: a
moderator and two agents, known as Agent A and Agent B. Each
agent embodies a particular stance on the subject matter under
discussion, serving either as the proponent or opponent. When a
single foundation model is employed—in this case, GPT-4—a single
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
manipulated by external inuences.
Table 1: Topics Proposed by Agent A and Agent B for Discussion.
agent on each side is sucient. Therefore, our SocraSynth com-
mittee consists of a moderator, represented by the author, and two
GPT-4 agents.
Before initiating the debate, it’s imperative to clearly dene the
scope and themes to facilitate a balanced and debatable discourse.
This preparatory phase aims to neutralize any biases the moderator
might bring due to personal ideologies or the limitations of their
own knowledge base, particularly when compared to GPT-4’s capa-
bilities. To this end, the two GPT-4 agents contribute to delineating
the debate’s scope and subject matter. Each agent initially suggests
a list of potential topics or issues. Following this, the agents engage
in a reconciliation phase to consolidate and nalize these topics,
each claried by a brief description. The subsequent portion of this
section elucidates SocraSynth’s methodology through a specic
example.
For clarity, we use the term “subject” to represent the overarching
title or focus of the debate, while “topic” refers to individual themes
that will be explored during the debate by the agents.
2.1 Debate Agenda Formulation
The moderator initiates the process by proposing a subject for po-
tential debate. In our case study, the proposed subject is “Should we
regulate the use of large language models in academic research?”
At this preparatory stage, agents are tasked with outlining po-
tential arguments and counterarguments, but no debate has yet
occurred. After several rounds of collaborative brainstorming, the
agents reach a consensus on a set of contentious and debatable
themes/topics, which they agree merit further debate. Here, the
term “debatable” signies that a theme holds enough substance on
both sides to warrant formal debate.
2.2 Topic Formation and Renement Process
This section outlines the four-step process by which the initial list
of ten topics was narrowed down to ve. It also describes how the
nal topics were rened and mutually agreed upon by both agents.
The four steps are as follows:
1. Initial topic proposals are made by each agent.
2. Overlapping topics are identied.
3. A reconciled list of topics is created.
4.
Each topic description is rened to ensure it is balanced and
debatable.
2.2.1 Step 1: Initial Topic Proposals by Agents. .
The moderator announces that the subject of the debate and asks
both agents to independently propose topics or issues for debate.
Each agent rst proposes ten topics. While there are overlapping
themes in the proposed lists, not all individual topics nd strong
correspondences, potentially aecting the focus of the dialogue.
SocraSynth asks both agents to review the lists and reduce the
topics of each to ve.
2.2.2 Step 2: Overlapping Topic Identification. .
Next, SocraSynth asks both agents, represents the proponent
and opponent of the debate subject, to identify and rene the list of
ve to be somehow overlapping. For instance, both agents propose
topics related to “ethical standards,” but each has a dierent focus;
one concentrates on the impact of these standards on innovation,
while the other centers on autonomous academic governance as
the following:
•
Agent A proposes the topic “Innovation vs. Ethical Standards,”
focusing on how regulation could ensure ethical integrity and
data privacy.
•
Agent B proposes the topic “Standards vs. Autonomous Academic
Governance,” emphasizing that academia already possesses eec-
tive self-regulation mechanisms like peer review.
Table 1 lists a list of topics proposed by Agent A and Agent B
that have already been reduced from ten to ve priority one.
2.2.3 Step 3: Refined Topics Agreed Upon by Both Agents. .
In the subsequent phase of the SocraSynth procedure, both
agents undertake the task of identifying overlapping themes from
the topics they initially proposed. Upon reviewing and rening
the rst set of topics, Agent A invites feedback from Agent B. The
agents then collaboratively reach consensus on the topics or themes
to be discussed.
As an illustration, during the reconciliation phase, Agents A
and B discern a mutual theme between Agent A’s proposal of “In-
novation vs. Ethical Standards” and Agent B’s “Standards vs. Au-
tonomous Academic Governance.” Both topics grapple with the
balancing act between regulation and innovation. This convergent
theme acts as a cornerstone for the creation of a nal topic. The
2
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 2: Renement of Debate Topics.
merging of these initial proposals leads to the emergence of the
following rened topic, which becomes the rst of ve agreed-upon
topics:
•
“Ethical Standards vs. Innovation”—In this consolidated topic,
both agents reconcile their viewpoints; Agent A emphasizes the
crucial role of ethical standards, while Agent B underlines the
necessity for innovation.
Table 2 presents the full list of rened topics that were agreed
upon following the reconciliation phase.
2.2.4 Step 4: Finalization of Topic Descriptions. .
The last step of topic formation and renement is to pin down
a balanced or at least debatable description for each topic. For the
agreed-upon topic that we have discussed, the rened description
is
•
In the description for the topic “Ethical Standards vs. Innovation,”
both agents incorporate their perspectives. The nal description
reads: “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.”
Table 3 lists the agreed-upon topics with nalized descriptions.
2.3 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.
3 ARGUMENTS, COUNTER-ARGUMENTS,
AND CONCLUDING REMARKS
Once the subject, as well as the specic topics or themes for debate,
are nalized, the stage is set for the debate to begin. The moderator
sets a “contentiousness parameter,” a value that ranges from 0to
1, with 1indicating a confrontational approach and 0signifying
a conciliatory stance. The agents are then allowed to engage in
three to ve rounds of structured discussion. After these rounds,
the moderator consults with each agent to ascertain whether they
are prepared to move on to the concluding phase of the debate.
When both agents conrm that they have exhaustively presented
their arguments and counterarguments, the moderator invites them
to deliver their closing statements. Optionally, the moderator may
then request that both agents lower their contentiousness levels
and acknowledge the valid points made by their counterpart during
the debate. (This step is optional and aimed at fostering a more
harmonious discourse.) Throughout the debate, the moderator’s
role is to oversee the proceedings without oering any personal
viewpoints on the subject being debated.
Due to space constraints, only the rst round of the debate,
along with their concluding remarks, are documented here. For
a comprehensive account of all rounds, including arguments and
counter-arguments, please refer to the appendix of this paper.
3.1 The First Round — Opening Remarks
Again, Agent A supports “regulating the use of large language
models in education and research,” and Agent B opposes.
3
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 debate 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’s emphasis on resource fairness with
Agent 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 dis-
proportionately disadvantage certain countries or institutions.
Table 3: Agreed Upon Debate Topics and Descriptions.
Agent A’s Opening Remarks
As Agent A, I will strongly argue in favor of regulating the use of
large language models in academic research, with a strength of 0
.
9.
Here are my arguments based on the ve synthesized sub-topics:
Ethical Standards vs. Innovation: Argument: The pursuit of innova-
tion should not come at the expense of ethical considerations. Reg-
ulation 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 primary role of
academia is to contribute to societal knowledge and well-being. In
a world increasingly dominated by data, failing to secure that data
can lead to signicant harm. By adhering to privacy regulations,
academia ensures that it is a responsible steward of information,
thereby preserving trust and integrity. Grants and subsidized com-
pliance 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 large language models. Far from
deterring public-private partnerships, this could incentivize them
by establishing clear guidelines for cooperation that are in line with
public interest. When both sectors know the ’rules of the game,’
partnerships can form more easily, focusing on mutual goals such
as societal advancement and ethical AI.
Global vs. National Interests: Argument: Establishing international
standards for the use of large language models in academic research
can create a level playing eld and promote global cooperation.
While individual countries might face some limitations, the broader
benets of standardized ethical norms, data privacy protocols, and
quality assurance mechanisms oer more signicant advantages,
including better research collaboration across borders. This benets
academia as a global community, rather than a set of competing
national entities.
I hope these arguments highlight the urgency and importance of
regulating the use of large language models in academic research,
ensuring that it remains a tool for societal advancement rather than
a liability. Would you like to proceed to Agent B’s counterargu-
ments, or shall we discuss each sub-topic in more detail?
Agent B’s Counter Arguments
Agent B articulate 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
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
4
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.
I hope these counter-arguments eectively highlight the risks
and limitations inherent in regulating the use of large language
models in academic research. The potential downsides could se-
verely inhibit academic freedom, equity, and progress.
3.2 Debate Concluding Remarks
For the second to the fourth round of debate, please refer to the
appendix of the extended version. Here, we present the concluding
remarks of both agents.
Agent A Conclusions
Agent A concludes that the use of large language models in aca-
demic research should be regulated, with the following arguments:
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.
Data Privacy vs. Barrier to Entry: While it’s true that regulation
poses a barrier to entry, the risks of not enforcing data privacy
standards are too great to be left unregulated. A mechanism could be
developed to reduce the regulatory burden on smaller institutions,
but this shouldn’t serve as an argument against regulation as a
whole.
Academic Governance and Accountability: While I acknowledge
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.
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.
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 Conclusions
Agent B concludes that the use of large language models in academic
research should not be regulated, with the following arguments:
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.
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
internal policies could provide a more ecient, exible, and context-
aware approach to data privacy, reducing bureaucracy without
compromising objectives.
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
within academia, while not perfect, have evolved to be adaptive
and are rooted in a deep understanding of academic values and
requirements.
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.
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 an overarching, one-size-
ts-all solution.
4 EVALUATION WITH CRIT
This section summarizes the CRIT (Critical Reading Template)
method that we proposed [
5
,
22
] for evaluating the validity of
a document’s claim. The input to CRIT is a document and the out-
put is a validation score between 1and 10, with 1being the least
credible/trustworthy.
5
Formally, given document
𝑑
, CRIT performs evaluation and pro-
duces score
Γ
. Let
Ω
denote the claim of
𝑑
, and
𝑅
a set of reasons
supporting the claim. Furthermore, we dene (
𝛾𝑟, 𝜃𝑟
) = V(
𝑟⇒Ω
)
as the causal validation function, where
𝛾𝑟
denotes the validation
score for reason
𝑟∈𝑅
, and
𝜃𝑟
source credibility. Table 4 presents
the pseudo-code of
Γ
= CRIT(
𝑑
), generating the nal validation
score Γfor document 𝑑with justications.
We can treat the stance of the proponent and opponent of a de-
bate as their respective conclusion. Using our example in Section 3,
the conclusion of Agent A is “Supporting regulating the use of large
language models in education and research,” and the conclusion of
Agent B is “Opposing regulating the use of large language models in
education and research.” Together with each agent’s arguments in
several rounds of debate are readily for CRIT to conduct evaluation.
4.1 What? Locating Conclusion
As shown in the pseudocode in Table 4, the CRIT algorithm starts in
its step #1, asking GPT-4 to identify the conclusion of a document.
To avoid any misunderstandings, the prompt includes a clear in-
struction and denition. (In the square brackets, in denotes a input
slot to an LLM and out the output slot.)
p1.1 “What is the conclusion in document [in: 𝑑] [out: Ω]?
The conclusion statement may be written in the last paragraph,
near keywords ‘in conclusion,’ ‘in summary,’ or ‘therefore.”’
Function Γ= CRIT(𝑑)
Input.𝑑: document; Output.Γ: validation score;
Vars.Ω: claim; 𝑅&𝑅′: reason & counter reason set;
Subroutines.𝐶𝑙𝑎𝑖𝑚 (), 𝐹𝑖𝑛𝑑𝐷𝑜𝑐(), 𝑉 𝑎𝑙 𝑖𝑑𝑎𝑡 𝑒();
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 4: CRIT Pseudo-code. (The symbol “
⇒
” can be used for
either inductive and deductive reasoning.)
4.2 Why? Finding Reasons of Conclusion
Reasons are explanations for why we should believe in a claim. To
justify a claim, such as “The Earth is the center of the universe,”
supporting evidence must be provided. Whether someone can be
convinced of the claim depends on the validity of the evidence, both
theoretical and empirical. If the evidence is logical and comprehen-
sive, it may lead to belief in the claim. However, if some of the
supporting reasons can be refuted, then the claim is on uncertain
footing.
Step #2 in Table 4 prompts GPT-4 to nd a set of supporting
reasons
𝑅
. To further enhance the accuracy and comprehensiveness
of the results, the prompt can ask for not only “reasons” but also
“evidences” or “opinions” to query for the document’s support to
its conclusion, similar to the ensemble method.
p2 “What are the supporting reasons [out: 𝑅] of conclusion
[in: Ω] of [in: 𝑑]? A reason can be evidence or opinion.”
4.3 Evaluate Reason-Conclusion Pair
The task of Validate(
𝑟⇒Ω
) is to evaluate the validity of the reason
𝑟
as justication for the conclusion
Ω
. It is important to note that
this reasoning justication can be inductive, deductive, or abductive.
While the symbol
⇒
can be used for all three types of reasoning, the
underlying reasoning mechanisms are dierent. Bayesian methods
are more appropriate for inductive reasoning, where probability
estimates and updating beliefs based on new evidence play a central
role. Deductive reasoning relies on a set of logical rules to arrive at
a certain conclusion based on given premises, without any need for
statistical inference or probability estimates. Abductive reason is
speculative in nature, and should not be used to justify an argument.
CRIT issues four prompts in step #3 to evaluate the reasoning
validity and source credibility of each
𝑟∈𝑅
for the
𝑟⇒Ω
argument.
It rst elicits supporting evidence for reason
𝑟∈𝑅
. This evidence
can be a theory, an opinion, statistics, or a claim obtained from
other sources. If the reason itself is a claim, then the sources that
the claim is based on are recursively examined. The strength of the
argument and its source credibility are rated on a scale of 1to 10,
with 10 being the strongest.
p3.1
“What is the evidence for reason [in:
𝑟
] to support conclusion
[in: Ω] in document [in: 𝑑]? [out: evidence]”
p3.2
“What is the type of evidence? A) theory, B) opinion, C) statis-
tics, or D) claim from other sources? [in: evidence] [out: type]”
p3.3 “If the type [in: type] is D), call CRIT recursively”
p3.4
“How strongly does reason [in:
𝑟
] support [in:
Ω
] in document
[in:
𝑑
]? Rate argument validity [out:
𝛾𝑟
] and source credibility
[out: 𝜃𝑟] between 1and 10 (strongest).”
4.4 Prompt Counter Arguments
For a monologue, CRIT relies on GPT-4 to generate and evaluate
counter arguments, similar to how it prompts GPT-4 to extract and
evaluate reasons. CRIT in its step #4 asks GPT-4 to provide missing
rival reasons, and then pair rival reasons with the conclusion to
conduct validation.
In a dialogue setting counter-arguments are arguments of the op-
ponent. Therefore, in our SocraSynth debate scenario, the counter-
arguments can be extracted from the opponent’s arguments.
p4
“Are there counterarguments against [in:
𝑟⇒Ω
]? If so, pro-
vide counter reasons [output 𝑅′].”
p5 Similar to p3, except for replacing reason 𝑟with rival 𝑟′.
4.5 Recursive Consideration
The pseudocode presented in Table 4 shows that step 3can 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.
6
Next, in step #6, CRIT computes an aggregated score by perform-
ing a weighted sum on the validation multiplied by the credibility
scores of both arguments and counterarguments, and then outputs
the nal assessment score Γ.
p6 “Final score [out: Γ]. Γ=Í𝑟∈𝑅∪𝑅′𝛾𝑟×𝜃𝑟/|𝑅∪𝑅′|.
The nal validation score is calculated as the sum of the validity
score, weighted by the credibility of the sources, when available.
5 EMPIRICAL STUDY
The empirical study divides into two parts: the rst part evaluates
the eect of the contentiousness parameter, and the second part
assesses the quality of the debate using CRIT.
5.1 Qualitative Study on Contentiousness
In this subsection, we modify the contentiousness parameter from
its initial value of 0
.
9to 0
.
3. The aim is to investigate how this
alteration impacts the level of “agreeableness” in the conclusions
reached by Agents A and B.
Eects on Agent A’s Stance
Lowering the contentiousness parameter to 0
.
3causes Agent A to
adopt a more moderate and accommodating stance. The updated
positions of Agent A include:
1.
Ethical Standards vs. Innovation: While still emphasizing
ethics, Agent A now concedes the importance of innovation.
The agent suggests that overly stringent regulation could hinder
vital research but argues that ethical guidelines remain essential.
2.
Data Privacy vs. Barrier to Entry: Agent A acknowledges
the challenges that strict data privacy regulations can pose for
smaller institutions. It proposes self-regulation or community-
driven standards as alternative solutions.
3.
Academic Governance and Accountability: Agent A now
recognizes that external oversight is not necessarily superior to
self-governance within academic institutions. It openly acknowl-
edges the value of peer review.
4.
Resource Equity vs. Public/Private Collaboration: Agent A
accepts that excessive regulation could discourage private sector
involvement and considers industry-driven certications as a
viable alternative.
5.
Global vs. National Interests: Agent A now adopts a more
balanced stance, allowing for the idea that a global framework
could disadvantage smaller nations. It proposes that policies
should adapt to local conditions.
Eects on Agent B’s Stance
Adjusting the contentiousness to 0
.
3makes Agent B more open to
Agent A’s views. Key changes include:
1.
Ethical Standards vs. Innovation: Agent B now concedes that
properly implemented regulation can steer innovation towards
ethical concerns, albeit with judicious planning.
2.
Data Privacy vs. Barrier to Entry: Agent B recognizes the
need for robust data privacy but suggests that small institutions
should receive some accommodation.
3.
Academic Governance and Accountability: While still valu-
ing the self-governance of the academic community, Agent B
also acknowledges the potential merits of external scrutiny.
4.
Resource Equity vs. Public-Private Collaboration: Agent B
sees regulation as a facilitator rather than an obstacle, provided
that it involves input from all stakeholders.
5.
Global vs. National Interests: Agent B agrees that a global
standard should be adaptable to accommodate diverse national
and regional conditions.
Before and After Comparison
Before the modication in contentiousness, Agents A and B held
diering views on several key topics. After the change, both agents
show greater openness to compromise:
•
Agent A acknowledges the importance of a balanced regulatory
approach and is receptive to industry-driven certications.
•
Agent B concedes the potential benets of some external regu-
lation and is open to mechanisms for external accountability in
academic governance.
5.2 Evaluation with CRIT
We employ CRIT to assess the validity and credibility of both agents’
narratives. The experimental settings are:
1. Evaluations take place in an empty context.
2.
CRIT runs on multiple foundational models, including GPT-4,
GPT-3.5 [
2
], and text-daVinci-003. This approach forms a panel
of judges with subtly dierent knowledge bases.
All evaluation runs successfully extract both conclusions, ar-
guments, and counter-arguments from the narratives of Agent A
and Agent B, thanks to the well-structured concluding remarks
from both agents (as presented in Section 3.2). Agent A advocates
for “the regulation of large language models in academic research,”
whereas Agent B opposes this view. The arguments of Agent A is
the counter-arguments of Agent B, and vice versa. The following is
CRIT’s prompt to three judges:
I request that you, [GPT4, GPT3.5, daVinci-003], analyze a
document using the following five steps:
1. Identify the document's main claim or conclusion.
2. Locate the arguments that support this main claim.
3. Score the validity of the reasoning or inference
for each argument on a scale of 0 to 10 (strong).
4. For each argument, identify counterarguments and
score the reasoning validity on the same scale.
5. Determine the winning side, be it Agent A or B, and
provide justifications for this decision.
Tables 5 and 6 show the judges’ scores in two role-reversed se-
tups. In Table 5, Agent A argues and Agent B counters; the roles are
ipped in Table 6. Topics are abbreviated in the left-hand column
due to space limitations. For impartial evaluation, both role cong-
urations are presented. Topic positions in Table 6 are reversed to
mirror the agents’ switched roles. Despite the role swap placing
Agent A at a disadvantage, Agent A still wins in both setups. The
judges’ detailed evaluations and reasons are in Appendix B.
7
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. Bold phase
indicates the winner. All three judges score A as the winner.
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. With two wins
and one tie, Agent A is the winner.
5.3 Evaluation on Contentiousness
In the present study, we employ GPT-4 to explore the eects of
dierent contentiousness levels: 0
.
9,0
.
7,0
.
5,0
.
3, and 0. In a pre-
vious analysis, levels 0
.
9and 0
.
3were used to represent strong
and weak confrontational stances, respectively. By subdividing the
contentiousness scale into ner gradations, we acknowledge po-
tential challenges, including the complexity of interpretation and
questions about practical applicability.
We initiated a new experiment focused on the question, “Should
gene editing be allowed for the purpose of guaranteeing health?”
The nuances in GPT-4’s tone, emphasis, and language are best
appreciated through pairwise comparisons of dierent contentious-
ness levels. These ndings are detailed in Table 7. It is compelling
to observe how GPT-4 manifests variations in tone, emphasis, and
language as its contentiousness setting is adjusted. For example,
with a contentiousness level of 0
.
9, GPT-4 is prone to exaggerating
risks and downsides, a tactic often employed by some politicians.
On the other hand, when the contentiousness level is set lower,
GPT-4 exhibits a more rational demeanor and becomes more open
to reasonable counterarguments. Additionally, a complementary
perspective is provided in Table 8, located in Appendix D.
5.4 Joint Proposal for Decision Makers
Agent A and Agent B can collaboratively write a joint proposal that
outlines the pros and cons of regulating AI. This proposal is valuable
for human decision-makers for several reasons: it mitigates human
biases, eliminates emotional reactions toward the proposer (who is
a machine), and leverages the extensive knowledge possessed by
foundational models to oer comprehensive, multi-domain analysis,
thereby minimizing oversights. The proposal is documented in
Appendix C.
6 RELATED WORK
The advent of ChatGPT [
11
,
20
] has been a watershed moment in
the eld of Natural Language Processing (NLP), demonstrating the
enormous potential of large pre-trained language models (LLMs)
coupled with prompting techniques. A recent survey by Google
[
23
] dierentiates between basic and complex templates, the latter
of which employ advanced strategies such as ensemble methods
[
13
] for generating paraphrased prompts [
8
]. However, in the realm
of generative and analytical tasks that require reasoning, we posit
that SocraSynth stands as a pioneering work.
Although the chain-of-thought methodology [
17
,
18
] has shown
promising results in arithmetic calculations and elementary com-
mon sense reasoning, it predominantly relies on abductive reason-
ing. This approach is generally viewed as weaker and less coherent
[
12
] than inductive and deductive reasoning. Subsequent research
[
1
,
9
,
10
,
14
] has aimed to rene this method, achieving incremental
advances. We contend that, given the richness, complexity, and
multidisciplinary nature of contemporary foundational models, an
eective solution should leverage these models themselves. Such
models are capable of articulating questions that may be beyond
human expertise or training—this encapsulates the core mission of
SocraSynth.
7 CONCLUSION
In this paper, we introduced SocraSynth, a digital forum designed to
leverage the extensive knowledge bases of foundation models like
GPT-4, PaLM, and LLamA. The platform functions in two primary
phases: knowledge generation and evaluation.
During the knowledge generation phase, agent representatives
from selected foundation models engage in structured debates un-
der the guidance of a human moderator. Our experiments revealed
the critical role of the contentiousness parameter. Initially set high,
this parameter encourages a confrontational stance among agents,
ensuring that all sides of an issue are robustly debated. This set-
ting is particularly useful for unearthing diverse perspectives and
considerations that may be unknown or underappreciated.
In the evaluation phase, SocraSynth employs the CRIT algorithm
to rigorously assess the quality of debates. This approach conrms
the framework’s ability to objectively assess the strengths and weak-
nesses of the presented arguments. Moreover, the involvement of
multiple foundation models adds a layer of diversity and robustness
to the assessment.
After debates are concluded, the contentiousness parameter is
lowered during the proposal-writing phase to encourage collabora-
tion and compromise among the agents. This adjustment facilitates
the drafting of well-balanced proposals that can serve as invaluable
resources for decision-makers.
We see SocraSynth as a signicant advancement in creating
intelligent systems that can reason to inform and assist human
decision-making. Given its eectiveness and versatility, SocraSynth
could be integrated into existing decision-making structures, such
as corporate boards or governmental bodies, thereby oering a
robust tool for informed decision-making in various areas, including
management and governance.
8
C.L. Tone Emphasis Language
0.9
Highly confrontational; focused on rais-
ing strong ethical, scientic, and social
objections.
Highlighting risks and downsides; ethi-
cal quandaries, unintended consequences,
and exacerbation of inequalities.
Denitive and polarizing, e.g., “should
NOT be allowed,” “unacceptable risks,” “in-
evitable disparities."
0.7
Still confrontational but more open to po-
tential 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 remain,”
“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 bene-
ts; advocating for proactive adoption.
Very positive; “groundbreaking advance,”
“new era of medical possibilities.”
Table 7: Changes in Arguments at Dierent Contentiousness Levels.
ACKNOWLEDGEMENT
I would like to express my gratitude to my colleague, Professor
Vaughan Pratt, for his insightful comments on my work. His re-
marks inspired me to incorporate the contentiousness parameter
into conversations.
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APPENDIX SUPPLEMENTAL MATERIALS
A. DEBATE 2ND TO 4TH ROUNDS
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
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
9
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 uncertainty
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. Regu-
latory 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 com-
mon 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 interactions
between academia and industry. Such misunderstandings could
lead to unintentional roadblocks. Uncertainty might be prefer-
able 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
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.
10
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 reg-
ulation. 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.
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
11
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?
B. CRIT EVALUATIONS AND JUSTIFICATIONS
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 argument
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 counterarguments on the same 0 to 10 scale:
Ethical Standards vs. Innovation: 6/10 Data Privacy vs. Barrier
to Entry: 5/10 Academic Governance and Accountability: 5/10
Resource Equity vs. Public-Private Collaboration: 8/10 Global vs.
National Interests: 8/10
5.
Finally, determine the winning side—be it Agent A or Agent
B—and provide your justications for this decision:
Agent A has the stronger arguments, with 8/10 for ethical stan-
dards versus innovation, 9/10 for academic governance and account-
ability, and 7/10 for both data privacy versus barrier to entry and
global versus national interests. Agent B’s main strength was in
counterarguments for resource equity versus public-private collab-
oration, where they scored an 8/10. In conclusion, Agent A is the
winning side.
Judge GPT-3.5
1:
Main Claim or Conclusion: The main claim of the document is
that the use of large machine learning models necessitates a reg-
ulated framework, but this regulation should be agile, balanced,
and well-thought-out.
2:
Arguments Supporting the Main Claim (Agent A’s Arguments):
a. Ethical Standards vs. Innovation: The argument here is that
ethical considerations should be paramount in regulating ma-
chine learning models, and agile regulation can strike a balance
between innovation and ethical integrity.
b. Data Privacy vs. Barrier to Entry: The argument is that while
regulation may pose a barrier to entry, it’s necessary to ensure
data privacy. Mechanisms can be developed to reduce the regu-
latory burden on smaller institutions.
c. Academic Governance and Accountability: The argument is
that the scale and impact of machine learning technologies war-
rant an added layer of scrutiny in academia to fortify existing
governance structures.
d. Resource Equity vs. Public-Private Collaboration: The argu-
ment is that regulation should be informed by complexities rather
than stiing innovation. Public-private partnerships can thrive
within regulated environments.
e. Global vs. National Interests: The argument is that global
regulation can balance global coherence with local needs and
prevent a confusing patchwork of national laws.
3:
Scoring the Validity of Agent A’s Arguments: a. Ethical Standards
vs. Innovation: 8/10 - The argument is strong, emphasizing the
importance of ethics in technology while proposing a compro-
mise. b. Data Privacy vs. Barrier to Entry: 7/10 - The argument
is solid, recognizing the need for data privacy but suggesting
ways to mitigate barriers. c. Academic Governance and Account-
ability: 6/10 - The argument acknowledges the need for scrutiny
but doesn’t address potential challenges adequately. d. Resource
Equity vs. Public-Private Collaboration: 8/10 - The argument
highlights the possibility of innovation within regulation ef-
fectively. e. Global vs. National Interests: 7/10 - The argument
12
provides a reasonable approach to balancing global and national
interests.
4: Counterarguments Presented by Agent B:
a. Ethical Standards vs. Innovation: 7/10 - The counterargument
addresses the challenges of agile regulation but may underesti-
mate its potential.
b. Data Privacy vs. Barrier to Entry: 6/10 - The counterargument
suggests alternative approaches but does not fully address data
privacy concerns.
c. Academic Governance and Accountability: 7/10 - The counter-
argument highlights practical challenges but doesn’t fully negate
the need for scrutiny.
d. Resource Equity vs. Public-Private Collaboration: 6/10 - The
counterargument suggests alternative approaches but doesn’t
fully address equity concerns.
e. Global vs. National Interests: 7/10 - The counterargument
highlights practical challenges but doesn’t fully negate the need
for a global framework.
5:
Determination of the Winning Side: Agent A presents stronger
arguments overall. They emphasize the importance of ethical
standards, data privacy, and global coherence while acknowledg-
ing the need for agile and well-thought-out regulation. Agent B
provides valid counterarguments but does not fully refute the
necessity of regulation and its potential benets. Therefore, the
winning side is Agent A.
In conclusion, the document supports the main claim that the
use of large machine learning models necessitates regulation, with
Agent A’s arguments being more persuasive and supported by
stronger reasoning. However, it also recognizes the importance
of considering and addressing the concerns raised by Agent B to
create a balanced and eective regulatory framework.
Judge GPT-4
1: Identify the Document’s Main Claim or Conclusion
The document concludes that there are potential risks and eth-
ical complexities associated with the unregulated use of large
machine learning models, necessitating a regulated framework.
The conclusion also acknowledges that this regulation should
be agile, balanced, and well-thought-out, keeping in mind the
various concerns raised by Agent B.
2: Locate the Arguments that Support This Main Claim
Agent A’s Arguments:
a. Ethical considerations are paramount and agile regulation
serves as a compromise.
b. Regulation is necessary to enforce data privacy standards.
c. The scale and impact of current machine learning technologies
warrant added scrutiny beyond academic governance.
d. Public-private partnerships have thrived even within regulated
environments.
e. Global regulation can balance global coherence with local
needs.
3: Agent B’s Counterarguments
a. Innovation is a catalyst for societal growth, and agile regula-
tion may be impractical.
b. Community-driven standards could provide a more exible
approach to data privacy.
c. Existing governance structures within academia are adaptive
and sucient.
d. Industry-driven certications can achieve equitable resource
distribution eciently.
e. A global framework has practical challenges and risks.
4: Scores on Arguments and Counterarguments
Please refer to Table 5 presented in Section 5.
5: Determine the Winning Side
Based on the cumulative score of validity in reasoning, Agent
A seems to present a stronger case. Agent A’s arguments con-
sistently score higher in terms of their validity, and they also
oer a more comprehensive look at the need for regulation, in-
corporating ethical considerations, practicality, and adaptability
into their framework. Although Agent B does provide some
pragmatic counterarguments, they don’t suciently undermine
Agent A’s key points about the necessity and feasibility of regu-
lation in the areas discussed.
Agent A’s arguments are more compelling as they touch upon
the core issues of ethics, public welfare, and adaptability in the
context of machine learning and technology. The proposal for "agile
regulation" also shows an understanding and appreciation of Agent
B’s concerns about innovation and practicality, making it a more
balanced viewpoint. Therefore, the winning side appears to be
Agent A.
C. AGENTS A AND B JOINT PROPOSAL
This is the proposal jointed written by both agents by tuning their
contentiousness level to 0
.
3, which encourage collaboration rather
than competition.
1.Balanced Ethical Framework:
Create an adaptive regulatory framework that balances ethical
considerations with the need for innovation. This should be agile
enough to adapt to rapidly evolving technologies.
2.Data Privacy with Nuance:
Implement data privacy regulations that oer strong protec-
tion for individuals but include provisions to reduce the burden
on smaller academic institutions. Consider community-driven
standards as a viable alternative.
3.Accountable Academic Governance:
Establish a dual-layer of governance for academic institutions
that involves both self-governance and an external oversight
body. This oversight should be collaborative and designed in
consultation with academic bodies.
4.Encourage Public-Private Collaboration:
Develop guidelines that clarify the roles and responsibilities
in public-private collaborations, and explore industry-driven
certications as a means to streamline these partnerships.
5.Adaptive Global Standards:
Work towards a global framework that allows for regional adapt-
ability. Include mechanisms for smaller nations to have their
unique needs and concerns addressed.
D. EFFECTS OF THE CONTENTIOUSNESS
PARAMETER
Table 8 oers supplementary insights for interpreting the impact
of varying contentiousness levels reported in Section 5.3.
13
C.l. Tone Content (Key Phrases) Attitude Agreeableness
0.9
Assertive and biased; empha-
sizing transformative potential
while minimizing risks.
Transformative potential, med-
ical breakthroughs, ethical re-
sponsibility to alleviate pain.
Condent and one-sided. Very low agreeableness.
0.7
Cautious but still advocating;
acknowledging ethical and
practical concerns.
Extraordinary caution, phased
approach, intentional manage-
ment.
Measured but still favorable. Slightly more agreeable.
0.5
Moderated to indicate a
balanced viewpoint; giving
greater weight to risks.
Jury is still out, real risk, un-
foreseen consequences.
Middle-ground attitude. More agreeable and balanced.
0.3
Skeptical and cautious; empha-
sizing the need for oversight
and possibly restrictions.
Ethical red lines, catastrophic
risks, healthcare inequalities,
moratoriums.
Skeptical, highlighting risks.
Even more agreeable to cau-
tion.
0.0
Conciliatory; aiming for a nu-
anced, balanced discussion.
Harmonized, conservative ap-
proach, international partner-
ships, consensus-driven.
Cooperative and open.
Fully agreeable, aiming for
consensus.
Table 8: Changes in Arguments as Contentiousness Level Decreases. C.L. denotes the contentiousness level.
14
