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Examining GPT-4’s Capabilities and Enhancement with SocraSynth


Abstract and Figures

This study explores the architectural advancements of large language models (LLMs), with a particular focus on the GPT-4 model. We begin with a thorough analysis of GPT-4’s distinctive features, including its polydisciplinary and polymodal data representation, the balanced approach in its algorithmic training, and the synergistic blend of human-driven insights with data-centric learning processes. Building upon these insights, we introduce SocraSynth, a {\em reasoning layer} thoughtfully crafted to augment knowledge discovery and bolster analytical reasoning across an ensemble of LLMs. SocraSynth is designed to facilitate a generative process through multi-agent analytical discussions, followed by the evaluation of the resultant arguments for their ``reasonableness.'' This approach significantly enhances interdisciplinary information discovery and complex reasoning, strategically addressing major challenges faced by LLMs, such as the production of contextually inaccurate responses (hallucinations) and entrenched statistical biases. Implementing SocraSynth across various application domains marks a significant advancement in overcoming the limitations of current LLMs, paving the way for more reliable and sophisticated AI-driven analytical tools.
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Examining GPT-4’s Capabilities and
Enhancement with SocraSynth
Edward Y. Chang
Computer Science, Stanford University
First Draft: July 2023; Revised: November 2023
Abstract—This study explores the architectural advancements
of large language models (LLMs), with a particular focus on the
GPT-4 model. We begin with a thorough analysis of GPT-4’s
distinctive features, including its polydisciplinary and polymodal
data representation, the balanced approach in its algorithmic
training, and the synergistic blend of human-driven insights with
data-centric learning processes.
Building upon these insights, we introduce SocraSynth, a
reasoning layer thoughtfully crafted to augment knowledge
discovery and bolster analytical reasoning across an ensemble
of LLMs. SocraSynth is designed to facilitate a generative
process through multi-agent analytical discussions, followed by the
evaluation of the resultant arguments for their “reasonableness.”
This approach significantly enhances interdisciplinary knowledge
discovery and analytical reasoning, strategically addressing major
challenges faced by LLMs, such as the production of contextually
inaccurate responses (hallucinations) and entrenched statistical
biases. Implementing SocraSynth across various application
domains marks a significant advancement in overcoming the
limitations of current LLMs, paving the way for more reliable
and sophisticated AI-driven analytical tools.
Index Terms—knowledge discovery, large language model, LLM
reasoning, Socratic method, SocraSynth.
With the rise of large language models (LLMs) [
], natural language processing has seen transformative
growth, impacting areas such as machine translation, sentiment
analysis, and text summarization. GPT-4 [
], notable for its
benchmark performance, including MMLU [
], excels in these
domains. However, it encounters issues such as hallucination,
biases, and limited reasoning.
This paper begins by exploring GPT-4’s architecture, fo-
cusing on knowledge representation, human-value alignment,
and the blend of human expertise with data-driven methods.
We address GPT-4’s limitations, including hallucinations,
biases, and constrained reasoning, and introduce SocraSynth,
a reasoning layer atop GPT-4 and similar LLMs, designed for
enhanced knowledge discovery and analytical reasoning.
A. Capabilities and Insights Observed
GPT-4’s architecture, initially undisclosed but later elucidated
by the research community [
], is examined, focusing
on knowledge representation and discovery, human-value
alignment, and the interplay between human and data-driven
Microsoft and OpenAI collaborations [
] reveal GPT-4’s
polydisciplinary nature and its polymodal variant’s benchmark
performances. Sections
delve deeper into these
aspects. In terms of human-value alignment, we discuss
ChatGPT’s RLHF methods [
], emphasizing pre-training
censorship’s impact on foundational models. This is expanded
upon in Sections
. Section
explores the role
of human knowledge in foundational model training, touching
on its dual nature as both a facilitator and a limitation. Section
II-F debates the efficacy of data-centric approaches in LLMs.
B. SocraSynth: Exploration, Reasoning, and Validation
To address common challenges in LLMs such as biases,
hallucinations, and restricted reasoning abilities, we introduce
SocraSynth. Rooted in the concept of “Socratic Synthesis,”
SocraSynth anchors the multi-agent platform, harnessing the ex-
tensive, polydisciplinary knowledge of LLMs. This innovative
system enhances context development, knowledge exploration,
analytical reasoning, and critical evaluation by facilitating
dynamic debates among AI agents. The contentious debate
setting in SocraSynth, through multiple iterations, sharpens
the dialogue context, uncovers new counterarguments, and
mitigates biases. A key feature of SocraSynth is its encour-
agement of LLM agents to engage in debates from diverse
perspectives, fostering a balanced viewpoint and reducing
the biases ingrained in their training datasets. Consequently,
SocraSynth fosters contentious and collaborative dialogues
that significantly improve the quality of content generation,
surpassing traditional monologue-based approaches.
SocraSynth’s utility spans various sectors, exhibiting impres-
sive results in areas such as disease diagnosis [
], corporate
sales strategy development [
], and geo-political analysis [
These applications highlight its adaptability and efficiency in
offering sophisticated, context-sensitive solutions for intricate
decision-making situations.
The unique contributions of our study are organized as
follows: Section II presents six hypotheses about LLMs and
discusses their broader implications. Section III emphasizes the
LLM-committee approach, conducting both contentious and
collaborative dialogues between human and LLM participants
to foster idea exchange and enhance logical reasoning, while
ensuring thorough validation of concepts and arguments.
Section IV presents selected examples from our extensive
case studies, offering a practical glimpse into the application
of our methodologies. Finally, Section V summarizes our key
findings and insights.
This section probes the architectural intricacies and repre-
sentations of GPT-4, putting forth six hypotheses accompanied
by pertinent considerations about the model. We posit these
hypotheses as underlying principles of automated, non-intuitive
statistical processing. Subsequent sections will explore the
endeavor of layering advanced reasoning [
] or decision-
making processes atop these foundations.
Polydisciplinarity as a Source of Super-Intelligence: We
examine the role of polydisciplinary approaches in foun-
dational models and their potential to reveal “unknown
unknowns, leading to new insights and knowledge domains.
Polymodal Feature Learning: This hypothesis evaluates the
benefits of multimodal training, particularly its impact on
enhancing the model’s overall intelligence and adaptability.
Post-Training Value Alignment: We delve into the challenges
and implications of aligning AI models with human values
after the training phase.
Pre-Training Filtering: We discuss the paradoxical effects
that pre-training data filtering might have, with an emphasis
on its influence on model behavior and the learning process.
The Limitations of Human Knowledge in Advancing AI:
This hypothesis considers situations where human insights
may inhibit, rather than enhance, AI progress, pinpointing
potential obstacles.
Is Larger Always Better?: We question whether a direct
relationship exists between the size of a model and its
performance effectiveness, challenging the assumption that
bigger is invariably better.
A. Polydisciplinary
GPT-4 possess what can be defined as polydisciplinary
. This term signifies the simultaneous compre-
hension of all fields of study, sans the typical boundaries
that segregate disciplines. The concept of polydisciplinarity
is distinct from multidisciplinarity in that the latter implies
several discrete fields of study, while the former suggests a fluid
integration of all knowledge. In a multidisciplinary context,
an individual may hold multiple doctorate degrees, each in a
different field. Polydisciplinarity, however, is akin to a single
mind holding, and seamlessly integrating, all knowledge across
Traditional academia partitions knowledge into departments,
such as Physics, Chemistry, Biotechnology, Management,
Music, etc. These divisions, arguably artificial constructs, may
have little utility in the era of supercomputing. Indeed, LLMs
occasionally generate responses that baffle us. This is not
necessarily a reflection of the model’s error, but perhaps our
limited understanding. If we could utilize ChatGPT to access
“unknown unknowns”—insights and knowledge we are not even
aware we lack—our evolution could greatly accelerate. The
challenge lies in formulating the right questions.
We can explore the unknown unknowns across three distinct
levels: the mystic level, the speculative level, and the represen-
tation/interpretation level. At the mystic level, we encounter
The term “polydisciplinary” in the context of GPT-4 was introduced by Eric
Horvitz, Microsoft’s CSO, during a panel discussion at Stanford University.
knowledge that is beyond our comprehension or articulation—
the deepest abyss of the unknown. At the speculative level,
we can conceive questions but lack the means to access their
answers. This stage signifies an understanding of our ignorance,
though without the resources to bridge these gaps. At the
representation/interpretation level, we find instances where an
AI model can provide remarkable solutions that we fail to
comprehend. This is not due to a lack of information, but our
limited capability to decode complex representations.
Each of these levels illustrates the spectrum of our un-
derstanding, from profound ignorance to the brink of com-
prehension. At the speculative level, we delicately tread the
boundary between the known and the unknown. Take, for
example, the prospect of undiscovered physical laws or particles.
Another illustration lies in the realm of extraterrestrial life. If
it exists, it could be governed by entirely different principles
of biochemistry or other unknown laws. These speculations,
while currently residing in the domain of the unknown, might
someday migrate into the territories of known unknowns or even
known knowns, pushing the boundaries of our understanding
of the universe.
We are primarily intrigued by the representation and in-
terpretation of “unknown unknowns. At this juncture, poly-
disciplinarity offers a fresh lens, gifting us new insights and
perspectives to perceive and elucidate phenomena previously
beyond human comprehension. This approach fuses knowledge
across various domains into a unified framework, enabling us
to tackle challenges unburdened by disciplinary silos.
Such a methodology bears implications for a more compre-
hensive grasp of intricate issues. Take, for example, climate
change. A true understanding of this global challenge necessi-
tates an integrated perspective, not just on greenhouse gases,
but also encompassing factors such as land use, deforestation,
energy production, biodiversity, and climate feedback loops.
In the realm of AI model interpretation, the possibilities are
expansive. The past decade alone has showcased several note-
worthy illustrations: from data-driven representation learning
in computer vision [6], to the triumph of AlphaGo Zero over
AlphaGo, and the notable progression from AlphaFold1 to
The recent introduction of the SocraSynth platform [
represents a significant advancement in the field. SocraSynth
brings together a multi-agent committee of LLMs to deliberate
on a wide range of complex topics. These include issues such
as the regulation of AI in academic research [
], disease
diagnosis [
], corporate strategy, and even the resolution of
conflicts in the Middle East [
]. For further exploration of this
subject, please refer to Section III.
B. Polymodality
models, which employ multiple data modalities
such as text and images, demonstrate superior performance
over their unimodal counterparts. GPT-4, trained with both text
and images, outperforms text-only models on the GRE exam,
Following the term polydisciplinary, here we define and use the term
polymodal, instead of multimodal, to refer to something that involves, relates
to, or is characterized by many different modes, methods, or modalities.
as reported in [
]. For instance, GPT-4’s performance on the
GRE vocabulary section was enhanced by three percent when
trained with images, and its math score saw an impressive
jump of nearly twenty percent!
The beneficial impact of images on vocabulary recognition
is understandable. For instance, an image of a ‘cat’ annotated
in multiple languages allows GPT-4 to associate the perceptual
features of a cat with the word ‘cat’ in different languages.
However, it remains intriguing how polymodal training can
benefit non-perceptual words, such as corroborate,paradox,
and pragmatic, as seen in the list of popular GRE vocabulary
(table omitted due to the space limit). This opens an interesting
avenue for empirical studies to identify which words benefit
from polymodal training.
The mystery deepens when considering how images could
enhance math abilities. Most math questions do not come
with associated images. The mechanism by which polymodal
training enhances performance on mathematical tasks remains
an intriguing question for further exploration.
C. Post-Training Value Alignment
Post-training alignment with human values [
] seeks to
curtail undesirable behaviors in AI models such as ChatGPT,
mitigating issues including hallucination and the generation
of toxic language. Achieved through fine-tuning the model’s
parameters, this process leverages reinforcement learning
techniques based on human feedback. Despite its well-meaning
intentions, this form of moderation might inadvertently restrict
the model’s intelligence. For instance, the backpropagation
process during value alignment could unintentionally impede
ChatGPT’s programming capabilities by modifying the model
parameters previously considered “optimal”. Essentially, opti-
mizing for a specific application might unintentionally impede
performance across other applications.
The question of who should set acceptable standards adds
another layer of complexity. Even when assuming all decision-
makers have the best intentions, it’s vital to recognize the
distinct historical experiences, values, and worldviews inherent
to different cultures. This segues into the age-old philosophical
debate about the nature of objective truth. While this discussion
is undoubtedly important, it falls outside the central focus of this
study, which emphasizes the mechanistic aspects of alignment.
D. Pre-Training Censorship
Censoring data before training LLMs has the potential to
not only limit their intellectual capacity but also completely
obliterate it. This is reminiscent of the mass act of book burning
and scholar burial initiated by Emperor Qin in ancient China
around 213-212 BC. Such an act of wide-scale censorship could
have erased a myriad of diverse perspectives and knowledge,
much of which might be considered acceptable today. Although
I oppose government-imposed censorship, if it must be imposed,
it seems more appropriate to apply it post-training.
This perspective is rooted in fundamental statistics and
machine learning principles. A model trained without exposure
to “negative” (or undesirable) data may have difficulties in
accurately distinguishing between positive and negative classes,
potentially leading to misclassifications. This challenge is
notably evident in the application of Support Vector Machines
(SVMs). For SVMs, the creation of an optimal hyperplane
between classes is crucial for high classification accuracy.
However, if there is a lack of support vectors on either side
of this hyperplane, the risk of prediction errors escalates.
Consequently, excluding undesirable documents from the
training set compromises the model’s capacity to discern
boundaries for correct document classification, diminishing
the effectiveness of post-training alignment efforts.
Supporting this viewpoint, a study by [
] conducted an
extensive evaluation of
ImageNet models across
different testing conditions. It found that training data diversity
is pivotal for model robustness; a homogenous training set
can significantly weaken the model’s performance, particularly
when even minor variations are introduced in the test data.
This principle is analogous to human behavioral patterns.
An individual who lacks exposure to inappropriate behavior
may face challenges in decision-making, owing to the absence
of a reference framework for discerning unacceptable actions.
This analogy extends to authoritarian regimes, which, despite
rigorous content control measures, often encounter difficulties
in developing accurate foundational models. This is possibly
due to their limited understanding of the nuances of the content
they seek to regulate. Ironically, a foundational model, trained
with preemptive censorship, may lack the essential ability to
identify and regulate the very content it was intended to control.
E. Limitations of Human Knowledge
Human knowledge, surprisingly, may hinder rather than
facilitate the training of machine learning models in certain
cases. This is evident in the domains of gaming (AlphaGo
versus AlphaGo Zero), protein folding (AlphaFold1 versus
AlphaFold2), and autonomous driving, where models trained
without the influence of human knowledge consistently exhibit
superior performance.
Consider the case of AlphaGo and AlphaGo Zero. AlphaGo,
trained with data from approximately
million rounds of
Go games, is outperformed by AlphaGo Zero. Remarkably,
AlphaGo Zero was trained from scratch, without any pre-
existing game knowledge. Similarly, AlphaFold2, which op-
erates without relying on human knowledge, outshines its
predecessor, AlphaFold1, that did utilize such knowledge. This
intriguing phenomenon was humorously noted by DeepMind’s
CEO, Demis Hassabis, in an April 2023 seminar at Stanford
University. He playfully remarked that human knowledge might
complicate the learning process more than facilitate it in these
advanced AI models.
In his insightful online article, “The Bitter Lesson,” Sutton
illuminates the patterns that have emerged from nearly seven
decades of AI research [
]. He asserts that researchers
often rely heavily on human knowledge to make incremental
progress in the face of burgeoning computational capabilities.
However, when there is a significant leap in computational
power, these marginal advancements are frequently outstripped
and surpassed. Sutton uses the evolution of computer vision
as an illustrative example, where early principles such as
edge detection, generalized cylinders, or SIFT features [
], a
method that has accumulated over
citations, have been
gradually superseded by models that learn directly from data.
A parallel scenario might be unfolding in NLP research, where
features constructed via human knowledge could potentially
under-perform compared to insights that models like GPT-
4 extract directly from data. Indeed, our earlier discourse
on polydisciplinarity underlined the limitations of human
knowledge, reinforcing Sutton’s proposition. This is because
human knowledge is fundamentally limited by our individual
cognitive capacities and the inexorable constraints of time.
That being said, it’s crucial not to misconstrue these examples
as an indictment against the value of human knowledge in AI.
Human knowledge plays an instrumental role in developing
interpretability, establishing ethical guidelines, and designing
AI system architectures (like CNNs and transformers). AI is,
after all, intended to augment human capabilities. Therefore,
understanding how to integrate human knowledge into AI
design could be vital for many applications. While we recognize
the potential of models learning from scratch, we should equally
value the role of human knowledge in shaping and directing
AI technologies.
F. Is Larger Always Better?
The term “Large” in Large Language Models (LLMs) can
be somewhat ambiguous, as it may pertain to the volume of
the training data, the expanse of the language covered, or
the architecture of the language model itself. While GPT-4’s
vast training dataset, encompassing tens of billions of assorted
documents, undoubtedly classifies as large, when we refer
to an LLM as “large, we predominantly allude to the sheer
magnitude of parameters within its transformer architecture.
Factors that contribute to this parameter count encompass the
input size (context size), word-embedding size, the number of
attention heads, and the number of attention layers.
The restrictions imposed by the first three elements can
typically be addressed through adjustments in hardware con-
figurations and software algorithms. Additionally, the potential
to expand context size, word embedding size, and the quantity
of attention heads tends to have an upper threshold. Regarding
attention heads, Kovaleva et al.’s study on BERT [
] indicates
that many attention heads don’t substantially contribute to
the model’s performance and might be the result of over-
parameterization. Conversely, the number of attention layers
directly influences the training time due to dependencies
between layers. Thus, when referring to the “size” of a Large
Language Model (LLM), we typically focus on the number of
attention layers.
While this far, larger models generally perform better due to
their increased capacity to learn and represent complex patterns,
there’s a limit to these benefits. In heuristic, adding more
parameters could lead to diminishing returns in performance,
higher computational cost, and overfitting, where the model
becomes excessively tuned to the training data and performs
poorly on new, unseen data. In principle, the concept of a
Shannon Limit could be metaphorically used [
] to refer
to a theoretical maximum performance that can be achieved
given the available data and computational resources. (However,
defining and quantifying such a limit for complex systems like
neural networks is a challenging area of research [18].)
The adoption of a mixture of experts model in GPT-4, which
consists of eight sub-models instead of a mere enlargement of
GPT-3’s architecture, implies that the strategy of purely esca-
lating size may have plateaued in terms of performance given
the current training dataset. As delineated earlier, three primary
design choices underpin GPT-4’s architecture. Evidently, a
straightforward augmentation of GPT-3’s parameters by adding
extra attention layers doesn’t deliver marked enhancements.
Hence, GPT-4 shifts towards a horizontal growth strategy
through an ensemble method, targeting a reduction in statistical
errors. This raises inquiries about the configuration of the
eight sub-models, each comparable to a GPT-3 model, and the
methodology for consolidating their outputs.
Potential strategies for training-data sharding include:
1. Training all ensemble models on the complete dataset.
Vertically segmenting data based on knowledge domains.
3. Randomly sub-sampling the data.
Regrettably, only corporations possessing substantial hardware
resources are positioned to rigorously experiment and discern
the optimal sharding approach.
This section aims to address the challenges of statistical
biases and limited reasoning capabilities in LLMs. We first
briefly review related work before presenting our approach,
SocraSynth, which layers advanced reasoning or decision-
making processes atop LLMs.
Several sophisticated methodologies have been developed
to integrate reasoning capabilities into LLMs. Notable among
these are the chain-of-thought [
], tree-of-thought [
], and
cumulative reasoning [
], complemented by other advance-
ments [
]. These approaches aim to direct models
towards logic-centric reasoning [
], thereby improving
response quality and consistency. However, their effectiveness
is often limited to specific, narrowly defined scenarios.
In open-domain contexts, where complex and lengthy logical
sequences are common, these methods encounter significant
limitations. Their reliance on sequential, step-by-step reasoning
becomes a hindrance, prone to accumulating errors as the
sequence progresses. This is especially true for the chain-of-
thought approach, effective in simpler tasks but less so in
open-domain analytics.
This brings us to an essential question: if users can inde-
pendently develop an extensive reasoning chain, what is the
practical benefit of using LLMs for such tasks? The paradox
lies in the fact that LLMs, developed to overcome human
cognitive limitations, often require the application of intuitive
human reasoning to address their shortcomings. This paradox
highlights the need for a more sophisticated approach in the
development and application of LLMs, one that synergizes
human intuition with machine computational power.
SocraSynth is designed to address these challenges, enhanc-
ing human decision-making processes across both familiar
and novel domains. It employs informal reasoning [
], as
opposed to the formal reasoning outlined in [
SocraSynth fosters a human-moderated debate environment,
thereby strengthening the structure and reliability of reasoning.
It utilizes LLMs’ capabilities in key NLP tasks like classifica-
tion, question answering, and information retrieval, offering a
comprehensive approach to reasoning in complex scenarios.
SocraSynth operates in two primary phases: generative and
evaluative. During the generative phase, LLM agents put forth
highly contentious arguments and counterarguments under the
guidance of the moderator, striving to present comprehensive
arguments rather than reaching a mutual consensus. In the
evaluative phase, a range of virtual judges—each backed by a
unique LLM—impartially appraise the discourse’s merits. The
Critical Inquisitive Template (CRIT) algorithm [
], anchored
in Socratic reasoning [
], forms the foundation for this
Subsequent to these phases, SocraSynth adjusts the “con-
tentiousness” parameter to urge LLM agents to produce a
well-balanced proposal, which, when curated for human review,
embodies the fusion of multi-agent knowledge discovery and in-
tricate deliberation. This is particularly salient in areas focused
on open-ended decision-making, where “reasonableness” often
trumps absolute “truths,” especially when such truths—like
the question of “Should Musk have acquired Twitter?”—are
inherently subjective.
A. Debate Format Liberates Agents from Inherent Model Biases
In SocraSynth, two agents are purposefully set to argue from
conditionally biased viewpoints corresponding to their assigned
stances. This setup naturally counteracts the inherent biases
from the LLMs’ training data. Engaging in debate from these
distinct perspectives, the agents stimulate dynamic discussions
that go beyond their models’ default biases. This requirement
to adopt and argue a range of viewpoints leads to a more
comprehensive exploration of ideas. As a result, this debate
format fosters more balanced and nuanced discourse, thereby
enriching the understanding of diverse subjects.
B. Breadth, Depth, and Polydisciplinary Knowledge
“The unknown unknowns eclipse both the known unknowns and
what we already grasp.
In Section
, our focus was on the representation and
interpretation of unknown unknowns. Employing polydisci-
plinary strategies can unearth new insights and perspectives,
illuminating aspects previously unrecognized. Modern LLMs
challenge us to reevaluate overlooked elements in decision-
making processes. The pivotal question is: How can humans
effectively traverse the realm of the unknown unknowns? This
is undoubtedly a challenging task. Rather, it seems more
pragmatic to let LLM-supported agents lead these explorations,
with humans stepping in for the final assessment.
C. Question Formulation by LLMs, Not Humans
“The core challenge is in crafting the right inquiries.
Consider a ten-year-old engaging with a panel of Nobel
Laureates from various disciplines; posing meaningful questions
would be a formidable task. This is why SocraSynth assigns
the role of question generation to LLM agents. These agents
engage in deep discussions, uncovering novel perspectives and
insights. By placing LLM agents in a debate format, we ensure
they clearly express their viewpoints, backing them up with
evidence and logic. Each position they articulate evolves into
a question, prompting a response from the opposing LLM
agent. As they engage in this intellectual contest, striving
for dominance, they actively seek supportive arguments and
counterpoints, enriching the discourse.
D. Mitigating Hallucination through Integrative Arguments
“While solutions often converge, hallucinations deviate.
In a debate setting, careful monitoring is key to addressing
any hallucinatory or false statements produced by an LLM
agent. Statistically, the chance of two agents producing identical