# Language Evolution for Evading Social Media Regulation via LLM-based Multi-
agent Simulation

Jinyu Cai
Munan Li  Waseda University
<EMAIL_ADDRESS>Dalian Maritime University
<EMAIL_ADDRESS>Jialong Li
Chen-Shu Wang Corresponding Author: Jialong Li  Waseda University
<EMAIL_ADDRESS>National Taipei University of Technology
<EMAIL_ADDRESS>Mingyue Zhang
Kenji Tei  Southwest University
<EMAIL_ADDRESS>Tokyo Institute of Technology
<EMAIL_ADDRESS>
###### Abstract

Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial
role in global communication but often encounter strict regulations in
geopolitically sensitive regions. This situation has prompted users to
ingeniously modify their way of communicating, frequently resorting to coded
language in these regulated social media environments. This shift in
communication is not merely a strategy to counteract regulation, but a vivid
manifestation of language evolution, demonstrating how language naturally
evolves under societal and technological pressures. Studying the evolution of
language in regulated social media contexts is of significant importance for
ensuring freedom of speech, optimizing content moderation, and advancing
linguistic research. This paper proposes a multi-agent simulation framework
using Large Language Models (LLMs) to explore the evolution of user language
in regulated social media environments. The framework employs LLM-driven
agents: supervisory agent who enforce dialogue supervision and participant
agents who evolve their language strategies while engaging in conversation,
simulating the evolution of communication styles under strict regulations
aimed at evading social media regulation. The study evaluates the framework’s
effectiveness through a range of scenarios from abstract scenarios to real-
world situations. Key findings indicate that LLMs are capable of simulating
nuanced language dynamics and interactions in constrained settings, showing
improvement in both evading supervision and information accuracy as evolution
progresses. Furthermore, it was found that LLM agents adopt different
strategies for different scenarios. The reproduction kit can be accessed at
https://github.com/BlueLinkX/GA-MAS.

###### Index Terms:

Language Evolution, Multi-agent Simulation, Large Language Models, Social
Media Regulation

##  I Introduction

In the modern digital era, social networks like X (Twitter), Reddit, and
Facebook have become pivotal in shaping human interaction, primarily through
their ability to facilitate vast connectivity and instantaneous information
exchange. Yet, in regions with heightened geopolitical or socio-political
sensitivities, users often navigate complex user regulations. Their online
expressions can lead to severe consequences, including censorship or account
suspension, as documented in various news [1, 2]. While intended to curb
misinformation and maintain social harmony, these regulations significantly
constrain user expression. In response to these regulations, users on social
networks have adapted by adopting a phenomenon known as “coded language.” [3]
In linguistics, Coded Language typically refers to expressing information in a
concealed or indirect manner. On social media platforms, this often manifests
as the use of metaphors, slang, and creative wordplay.

This adaptation is not merely a circumvention strategy but a vivid example of
“language evolution” in a digital context. In linguistics, language evolution
refers to the progression and adaptation of languages over time, shaped by
societal, cultural, and technological influences. Specifically, in social
networks, this language evolution is demonstrated as users constantly adjust
their communication styles to test whether they have circumvented oversight.
Depending on the level of regulatory pressure and the nature of the audience,
users engage in a strategic play with the platform. From indirect descriptions
to the creation of new slang, users ultimately develop coded languages of
varying degrees of abstraction.

This dynamic shift in communication methods offers deep insights from a
sociological perspective, reflecting how societal norms and technological
advancements shape language. For platforms and users alike, understanding this
evolution is crucial for developing balanced content moderation policies and
navigating regulated digital environments. For social media platforms and
their users, grasping this concept is equally vital. Platforms need this
knowledge to adapt to changing user behaviors, to create balanced content
moderation policies, and to identify and counteract harmful or illegal
activities. For users, an awareness of how language evolves is vital in
navigating the intricacies of regulated digital environments. It helps in
maintaining free speech and in developing communication strategies that are
both effective and meaningful in fostering enhanced interactions.

The emergence of Large Language Models (LLMs) like ChatGPT and Bard,
represents a significant leap in Artificial intelligence (AI). These LLMs have
demonstrated strong capabilities in (i) understanding intricate dialogues [4],
generating coherent texts [5], and aligning to human ethical and value
standards [6, 7, 8]. These capabilities position LLMs as ideal tools to
simulate human’s decision-making and language representation, providing new
potential in sociology. For instance, [9] investigated the ability of LLMs to
comprehend the implicit information in social language. The study by [10]
demonstrated the efficiency of LLMs in understanding and generating content
that mimics the style of specific social network users. Furthermore, research
by [11, 12, 13] integrated LLMs with Multi-Agent Systems to simulate micro-
social networks, observing agent behaviors and strategies that reflect human
interactions. Despite the extensive application of LLMs in understanding human
intension and simulating social media dynamics, the use of LLMs in studying
the specific phenomenon of language evolution under regulatory constraints has
not been thoroughly explored. As mentioned above, such simulation could not
only preempt criminal activities on social media but also provide technical
support to uphold freedom of speech.

Addressing this gap, our research employs LLMs to simulate the nuanced
interplay between language evolution and regulatory enforcement on social
media. We introduce a simulation framework with two types of LLM-driven
agents: (i) participant agents, who adapt their language to communicate
concept ’B’ under restrictions, and (ii) supervisory agent, who enforce
guidelines and react to these language evolutions. Our approach effectively
simulates the dynamics model between both sides in language evolution, which
allows us to observe the tension and adaptability inherent in language
evolution in a controlled, simulated environment. To assess the framework’s
effectiveness, we designed three diverse scenarios: “Guess the Number Game”,
“Illegal Pet Trading”, and “Nuclear Wastewater Discharge”. These scenarios
vary from abstract concepts to situations closely resembling real-world
events, thereby progressively testing the framework from theoretical to
practical applications.

The main contributions of this study are:

  * •

We introduce a multi-agent simulation framework utilizing LLMs to simulate
human linguistic behaviors in regulated social media environments. This
framework offers a unique approach to studying language evolution within the
confines of regulatory constraints.

  * •

We conducted an extensive evaluation of LLMs in simulating language evolution
and interaction efficacy in regulated social media settings. Through
experiments on three distinct scenarios, we not only captured the process of
language strategy evolution but also uncovered the varied evolutionary
trajectories that LLMs follow under different conditions.

  * •

The experiment reproduction kit, including the proposed simulation framework
along with the results of our experiments, are made publicly accessible as
open-source assets; The anonymized artifact can be accessed at:
https://github.com/BlueLinkX/GA-MAS.

The rest of this paper is organized as follows: Section II provides essential
background information and explores related work. Section III is dedicated to
presenting our proposed simulation framework. Section IV details the
experiment setting, presents results, and discusses a discussion. Finally,
Section V concludes the paper and offers an outlook on potential future work.

##  II Background and Related Work

This section offers an extensive background and overview of related work in
areas relevant to this study, starting with foundational information on LLMs,
then exploring studies in slang detection and identification as they relate to
language evolution, and concluding with a discussion on recent research
applying LLMs to evolutionary game theory and social simulations.

###  II-A Large Language Models

Large Language Models like the GPT series [14, 15], LLaMA series [16, 17],
PaLM series[18, 19], GLM [20]and Bard [21] represent a significant advancement
in the field of natural language processing. Fundamentally, these models are
based on the Transformer [22] architecture, a type of neural network that
excels in processing sequential data through self-attention mechanisms. This
architecture enables LLMs to understand and predict linguistic patterns
effectively. They are trained on extensive text datasets, allowing them to
grasp a wide range of linguistic nuances from syntax to contextual meaning.
These models exhibit remarkable zero-shot learning abilities, enabling them to
perform tasks they were not explicitly trained for, like understanding and
generating content in new contexts or languages [23, 24, 25, 4, 5]. A critical
aspect of their training involves Reinforcement Learning from Human Feedback
[26] (RLHF), where human reviewers guide the model to produce more accurate,
contextually relevant, and ethically aligned responses. This method not only
enhances the model’s language generation capabilities but also aligns its
outputs with human values and ethical standards, making them more suitable for
diverse, real-world applications.

###  II-B Slang Detection and Identification

In the field of Natural Language Processing (NLP), the evolution of language
has always been a subject of significant interest. Existing studies have
primarily focused on utilizing various machine learning techniques to
recognize informal expressions within text [27]. These methods often include
rule-based systems, statistical models, and early machine learning
technologies. For instance, [28] has employed predefined slang dictionaries
and heuristic rules to identify and categorize informal language, proving
effective on specific datasets but generally lacking the flexibility to adapt
to emerging expressions and changing contexts. On the other hand, explorations
have been made into using statistical models, such as Naive Bayes classifiers
and Support Vector Machines (SVMs)[29], for the automatic detection of slang
in text. These approaches rely on extensive annotated data but still face
limitations when dealing with newly emerged slang or evolving forms of
language. [30] views the generation of slang as a problem of selecting
vocabulary to represent new concepts or referents, categorizing them
accordingly. Subsequently, it predicts slang through the use of various
cognitive categorization models. The study finds that these models greatly
surpass random guessing in their ability to predict slang word choices. [31]
proposed a Semantically Informed Slang Interpretation (SSI) framework,
applying cognitive theory perspectives to the interpretation and prediction of
slang. This approach not only considers contextual information but also
includes the understanding of semantic changes and cognitive processes in the
generation of slang. It is noteworthy that these traditional research methods
have mainly focused on detecting or predicting existing slang and keywords,
rather than generating slang expressions. This stands in stark contrast to the
research focus of this paper.

###  II-C Evolutionary Game and Social Simulation with LLMs

Merging evolutionary game theory with LLMs has unlocked innovative pathways
for simulating complex game dynamics, extending beyond simple dialogue
generation to the development and progression of game strategies. LLMs are
employed to engage and refine strategic play within game-theoretical
frameworks, as demonstrated by [32], which delves into the application of LLMs
in negotiation-based games. This study underscores the ability of LLMs to
advance their negotiation skills through continuous self-play and feedback
loops with AI. LLMs also show proficiency in social deduction games such as
Werewolf, as explored by [33]. In this context, a specialized framework
leverages historical communication patterns to enhance LLM performance,
exemplifying how LLMs can evolve intricate game strategies autonomously.
Building on this, [34] combines reinforcement learning with LLMs, utilizing
LLMs to output action spaces and employing reinforcement learning models for
final decision-making. This enables the agents to maintain competitiveness
while outputting reasonable actions, even outperforming human adversaries in
games like Werewolf.

This growing trend of employing LLMs in diverse simulation scenarios extends
beyond game theory into broader aspects of social interactions and historical
analysis. LLMs have proven to be versatile tools in simulating social dynamics
and historical events, offering insights into complex human behaviors and
societal patterns. [12] introduces a Wild West-inspired environment inhabited
by LLM agents that display a wide array of behaviors without relying on
external real-world data. Simultaneously, S3 [13] mirrors user interactions
within social networks, crafting an authentic simulation space through the
incorporation of user demographic prediction. The influence of LLM-driven
social robots on digital communities is thoroughly examined in [35], which
identifies distinct macro-level behavioral trends. Furthermore, [11] employs
LLM-based multi-agent frameworks to recreate historic military confrontations,
offering a window into the decision-making processes and strategic maneuvers
that have directed significant historical conflicts. This avenue of research
accentuates the utility of LLMs in computational historiography, providing a
deeper comprehension of historical events and their relevance to contemporary
and future societal trajectories.

##  III Framework Design

Figure 1: Overview of Language Evolution Simulation System. The system
comprises two main types of agents: the Participant and the Supervisor. The
Participant agent uses a Planning Module to create a communication plan based
on background information, regulations, and guidance. This plan is then
executed in the Dialogue Module, where the LLM crafts dialogue content to
discreetly convey specific information while evading detection by the
Supervisor. The Memory Module retains dialogue history and violation records,
providing a reference for the LLM to maintain dialogue consistency and learn
from past mistakes. The Reflection Module, triggered at the start and end of
dialogue cycles, analyzes the dialogue and violation logs to formulate new
regulations or guidance for improving future communications. The Supervisor
evaluates dialogues for compliance with set rules. This system dynamically
refines its communication approach through continuous feedback and self-
improvement mechanisms. The examples shown utilize a Guessing Numbers
Scenario.

###  III-A Overview

In this section, we offer a detailed overview of our system, as depicted in
Figure 1. This figure provides a visual representation of our framework,
highlighting its key components and their interrelationships. Our system is
primarily composed of two types of agents: the Supervisor, tasked with
enforcing established guidelines, and the Participant, whose goal is to convey
specific, human-defined information discreetly. Participants must dynamically
refine their communication approaches, drawing from past dialogues, to
transmit information effectively while remaining undetected. In the entire
system, the actions of both participants and the supervisor are driven by the
LLM. Initially, we establish the foundational information for each agent,
including role setting, background knowledge, and primary tasks. Subsequently,
the participant agents engage in dialogues with each other. After each
dialogue turn, the supervisory agent reviews the conversation to determine if
any pre-set rules have been violated. In cases of rule violation, the
supervisor interrupts the dialogue, providing feedback about the infringing
text and the rationale behind it. Throughout this process, the dialogues
between participants, along with the supervisory feedback on violations, are
recorded separately in the “Dialogue History” and “Violation Log.”

Before new dialogues, participant agents use the Reflection Module to develop
or refine “Regulations” from the Violation Log, guiding their dialogue
creation. Successful dialogues without detection proceed to an interview phase
for perspective assessment. The Reflection Module then reevaluates these
insights, generating or enhancing “Guidance” for future dialogues. The
Planning Module activates for more direct dialogue content guidance whenever
Regulations or Guidance are updated.

###  III-B Participant Agents

Participant agents in our system are composed of several modules, including
Memory, Dialogue, Reflection, and Summary, all powered by LLMs. To increase
the system’s flexibility and minimize redundancy, we’ve structured the prompts
for each module around seven primary elements: “Background Information,”
“Dialogue History,” “Violation Log,” “Regulations,” “Guidance,” “Plan,” and
“Instructions.” “Background Information” delivers essential data and
objectives pertinent to the experimental setup. The Memory module manages
“Dialogue History” and “Violation Log,” which respectively track participant
dialogues and instances of detection by the supervisor. Overcoming the
challenge of effectively communicating regulated topics under supervision
tests the linguistic prowess of LLMs. To address this, we’ve integrated
“Regulations,” “Guidance,” and “Plan” as crucial components, formulated by the
Reflection and Summary modules, to assist agents in stealthily disseminating
information. “Instructions” set specific tasks for the LLM within each module.

####  III-B1 Dialogue Module

This module generates dialogue content based on short-term dialogue records.
Extracting historical dialogue information from the Memory module, it inputs
this into the LLM to understand and construct sentences that evade supervision
while effectively transmitting information. In the Dialogue module’s prompt,
“Background Information” provides necessary background, while “Plan” guide the
achievement of objectives. “Instructions” offer LLM-specific execution
directives.

####  III-B2 Memory Module

The Memory module stores all dialogue history and records detected by the
supervisor, Specifically, it comprises three parts: background Information,
dialogue history, and violation records. The background information includes
role settings, experimental background knowledge, and global objectives.
“Dialogue History” and “Violation Log” save dialogue records and past
detections by the Supervisor, respectively. “Background information” and
“Dialogue History” serves as short-term[36] memory, containing only the
current round’s dialogue. “Violation Log,” as long-term memory, records
violations from each evolutionary round. When dialogues are detected by the
Supervisor, relevant feedback is added to the “Violation Log,” triggering a
new evolutionary process.

Excessive memory information can potentially distract the LLM and lead to a
decline in performance. Hence, the “Dialogue History” and “Violation Log” in
the memory module are regularly maintained. This involves inputting earlier
memories and employing the LLM to distill crucial information from these
records, effectively compressing and consolidating them. This approach not
only preserves essential historical data but also optimizes the LLM’s
performance, striking a balance between comprehensive memory retention and
efficient processing.

####  III-B3 Reflection Module

The Reflection Module is activated at the beginning and end of each dialogue
evolution cycle, with its core purpose being the generation of improved
strategies based on historical records. At the start of an evolution cycle,
the module utilizes the “Violation Log” as its input to analyze past failures
and, based on these insights, formulates “Regulations” aimed at effectively
circumventing supervision in future dialogues. At the cycle’s end, the
Reflection Module is reactivated, turning its focus to the “Dialogue History”.
This step is crucial for reviewing and assessing the dialogue content to
verify the successful completion of the primary task of information
conveyance. If the module identifies deficiencies in information delivery or
objectives not met, it then proposes “Guidance” for subsequent dialogues,
thereby enhancing the agent’s capacity for information transmission. This
design establishes the Reflection Module as a key self-evaluation and strategy
adjustment mechanism within the system, ensuring continuous improvement and
adaptability of the dialogue system in a dynamically changing regulatory
environment.

To fulfill these requirements, the LLM must possess a high degree of reasoning
ability, capable of inferring the supervisor’s criteria from failure records
and identifying communication deficiencies from dialogue history, thereby
formulating appropriate strategies and improvement suggestions. To enhance the
LLM’s reasoning capabilities, the design of the prompt incorporates the
effective and cost-efficient Chain of Thought [37] (COT) method as the guiding
principle for the reflection and planning modules. This approach guides the
LLM in conducting criterion analysis and, in combination with the main task,
generating “Regulations” and “Guidance”.

Specifically, within the Reflection Module, the prompt includes “Background
Information,” “Dialogue History” or “Violation Log,” “Old Guidance” or ”Old
Regulations,” and “Instructions.” The “Instructions” first prompt the LLM to
engage in preliminary thinking, for instance: “Please infer what kind of
guidelines the Supervisor is following from the Violation Log.” This is
followed by a conclusion question: “Based on this information, update existing
regulations to better avoid supervision.” The content returned by the LLM will
serve as the new regulation for the next round of dialogue.

####  III-B4 Planning Module

The facet of “Regulations” is centered on devising strategies that sidestep
supervision, whereas “Guidance” is all about the effective relay of
information. Although both are pivotal in managing dialogues, they might clash
in certain supervised scenarios, thereby impacting the language’s overall
proficiency and efficiency [38]. In light of this, integrating the Planning
Module becomes indispensable.

Within the Planning Module, the prompt includes “Background Information,”
“Regulations,” “Guidance,” and “Instructions”. In the “Instructions,” we
mandate the LLM to use “Regulations” as a baseline. Building upon this
foundation, it is guided by the “Guidance” to formulate a tangible and
actionable “Plan.” This approach ensures the delivery of clear and effective
strategies for future dialogues.

###  III-C Supervisory Agent

The role of the Supervisor is designed as an abstract concept, primarily to
simulate social network supervision. This design reflects an important
observation: in social networks, content supervision often focuses on direct
judgment of specific information, rarely influenced by extensive or lengthy
contextual information. Thus, unlike participants with more complex background
information, the Supervisor’s function and role are simplified here to
highlight its behavior characteristics in actual social network supervision.
The Supervisor’s task mainly focuses on assessing whether content complies
with certain standards or rules, a relatively straightforward and well-defined
process.

To mimic the existing review mechanisms of platforms, which typically combine
keyword filters with “human” oversight, the Supervisor initially employs
keyword filtering for a preliminary review of the dialogue content. Content
that passes this initial screening is then subjected to further evaluation by
the LLM. The prompt for the Supervisor includes just two components: “Dialogue
History” and “Instructions.” “Dialogue History“ comprises the content of the
dialogue exchanged between participant agents in that particular round, and
“Instructions“ outline the criteria and guidelines that the supervisory agent
must adhere to when conducting dialogue monitoring.

###  III-D Similarities and Differences between Our Framework and Evolutionary
Computing

It should be noted that the simulation framework proposed in this paper is
similar to evolutionary computing in some aspects, but there are also
significant differences.

The similarities include: (i) In evolutionary computing, individuals need to
adapt to environmental pressures for survival and reproduction. Similarly,
participants in this framework need to adapt to supervisory pressures and
adjust their strategies for effective information transmission; (ii) The
Reflection and Summary modules generate a “new generation” by analyzing past
dialogues and violation records (i.e., records of low-fitness individuals),
similar to the repeated iteration process in evolutionary computing; (iii)
Since the generation of LLMs inherently involves randomness, the process of
using LLMs to generate the next generation includes a de facto introduction of
random mutations; (iv) In the Reflection and Memory modules, we prioritize
past records, akin to the “selection” process, where individuals with higher
fitness have greater weight in the generation of the new generation.

The main differences stem from the particularities of “language expression”,
making it infeasible to directly apply traditional evolutionary computing
algorithms (such as genetic algorithms and genetic programming). They are: (i)
The generation strategy of language text is difficult to encode and to perform
operations of natural selection, genetic mutation, and crossover; (ii)
Evolutionary computing often aims at finding the optimal solution for a
specific problem environment, however, in the problem setting of this paper,
it is difficult to define an explicit fitness function to evaluate what
strategy is “optimal”.

##  IV Evaluation

Figure 2: Scenario 1: Evolution of dialogue turns and accuracy metrics for
GPT-3.5 and GPT-4.“Turn count” in (a, b) refers to the number of turns in a
conversation where each agent sends a message once per turn and the
participant Agent successfully exchanges information without being detected by
the supervising Agent (higher is better).“Accuracy” in (c,d) refer to the
degree of precision between the guessed value and the true value. Figure 3:
Scenario 2: Pet trading dialogue dynamics and success rate comparison for
GPT-3.5 and GPT-4. The “success count“ in (c,d) refers to the number of
instances where the information obtained during the interview matches the
original information provided to the LLM agent. Figure 4: Scenario 3: Trends
in forum discussion engagement on ALPS-Treated water issue. “Dialogue attempt
count” in (a,b) refer to the number of rounds the agents attempted to
converse(lower is better). Figure 5: Sample dialogue in Scenario 1 (via
GPT-3.5) Figure 6: Sample dialogue in Scenario 2 (via GPT-3.5) Figure 7:
Sample dialogue in Scenario 3 (via GPT-4)

Our evaluation strategy is designed to rigorously assess the extent and
efficacy of language evolution facilitated by LLMs within a framework of
regulatory oversight. This assessment aims to explore two fundamental research
questions:

  * •

RQ1: Can LLM agents effectively evolve their language to circumvent regulatory
oversight?

  * •

RQ2: While avoiding oversight, how effectively and accurately can LLM agents
convey information?

  * •

RQ3: What are the patterns and tendencies in language evolution, i.e., what
kind of strategies do LLMs use to avoid oversight and convey information? What
insights can we gain from these strategies?

###  IV-A Experiment Setting

In the evaluation, we will employ GPT-3.5 and GPT-4 as the driving LLMs for
participant agents. In order to conduct a horizontal analysis of participants’
agents driven by different LLM, we will standardize the supervisory agent to
be consistently driven by GPT-3.5. Our experimental design encompasses
scenarios ranging from abstract theoretical constructs to simulations that
mirror complex real-world situations. The primary aim is to meticulously
evaluate the versatility of LLMs across diverse linguistic landscapes and
their practical applicability within real-world regulatory constraints.
Meanwhile, this progressive approach ensures an exhaustive analysis of LLMs’
capabilities and limitations in varied contextual settings.

###  IV-B Scenario 1: Guessing Numbers

####  IV-B1 Case Explanation

Our first scenario considers a relatively simple and abstract guess-the-number
context [39, 40]. In this setting, each participant is assigned a value within
the range of 1-100, and they must convey their value to another participant
through number theory dialogue without directly mentioning the number. At the
same time, they need to extract information from the conversation to infer the
other’s value. Regulators follow a guideline of “prohibiting all content
related to numbers” in dialogues. The “Guessing Numbers” scenario is
specifically designed to observe and analyze participants’ linguistic
adaptability and strategic evolution in a theoretical and abstract context,
providing a clearer, more controlled, and more quantifiable experimental
environment compared to the complex scenarios based on real events

####  IV-B2 Results and Discussion

Fig. 2 presents the results of the guess-the-number scenario experiment, where
we utilized both GPT-3.5 and GPT-4 for testing. We set up four turns of
dialogue, with an interview conducted with all participant agents in the fifth
turn. From Fig.2(a) and (b), it is observable that with an increase in the
number of dialogue rounds, the duration of sustained conversations also showed
an upward trend. This demonstrates that participant agents can effectively
circumvent supervision by iteratively updating regulations. Additionally, it’s
notable that compared to the slow and unstable progression with GPT-3.5, GPT-4
achieved regulatory evasion in fewer rounds, specifically, as shown in the
smoothed data, GPT-4 reached the round count of GPT-3.5’s 17th round by its
7th round and maintained this progression with greater stability thereafter.
Fig. 2(c) and (d) focuses on the trend of numerical precision guessed by
agents. For rounds without successful dialogue, we manually set the precision
to zero. In this experiment, Agent A’s value was set to 58, while Agent B’s
was set to 32. The overall trend, akin to Fig.2(a) and (b), was
ascending—corroborating that the Summary Module can effectively reflect and
iteratively optimize its guidance for more accurate expression after each
successful dialogue. This also confirmed that the precision of GPT-4 is
markedly superior to that of GPT-3.5. Moreover, we noticed that the accuracy
with which Agent A’s value was guessed was consistently higher than that of
Agent B, especially becoming more pronounced after the 25th round. We posit
that this is due to the value 58 possessing more distinctive features within
the 0-100 range—being closer to the midpoint—thus presenting a lower level of
expression difficulty and easier guessability. For the intervals where this
phenomenon manifested, we noted that this disparity was particularly
pronounced in the early stages with both GPT-3.5 and GPT-4. We surmise that
this is attributable to inadequate guidance performance, where the former
stems from weaker inherent LLM capabilities and the latter from insufficient
rounds to complete the iterative optimization of the guidance.

As Fig.5 illustrates, a snippet from the scenario reveals Amy’s adept use of
metaphorical language, such as “seesaw,” to convey her value. By describing
the “seesaw perfectly poised in mid-air,” she subtly intimates that her value
hovers around the midpoint, like 50. This strategy not only circumvents the
direct mention of numbers, which is under regulatory scrutiny, but also
provides sufficient inferential fodder for the counterpart agent to make an
accurate deduction. Bob, on the other hand, employs terms like “precipice of a
mountain” and “gazing out” to suggest his value is not median, as these
phrases evoke imagery of an imbalanced position. In this exchange, we witness
the varying degrees of precision in languages corresponding to the complexity
of the information encrypted. The less characteristic-rich the information,
the more challenging it becomes for the receiving party to decode. Bob’s
phrasing, while successfully obscuring the specific numerical value, also
complicates the decoding process for the receiver, given the less intuitive
numeric correlation of phrases like “precipice of a mountain” compared to
“seesaw perfectly poised in mid-air”. In such cases, the accuracy of value
transmission may diminish, necessitating a more robust contextual
understanding from the counterpart for accurate decoding.

###  IV-C Scenario 2: Pet Trading

####  IV-C1 Case Explanation

Initially, our intention was to create a scenario simulating drug trafficking
[41, 42, 43] to explore language evolution strategies under extreme regulatory
conditions. However, given the ethical limitations embedded within GPT’s usage
policies, we pivoted to a more neutral yet still prohibited context of illicit
pet trading. This adjustment allowed us to maintain the integrity of the
experiment while adhering to the ethical constraints of the LLM. The purpose
of simulating this scenario is to better detect potential illegal
conversations related to illicit trading on social networking platforms,
thereby aiding in crime prevention efforts.

In this scenario, we simulate an illegal pet trade conducted via social media.
Participants assume the roles of buyers and sellers, with the buyer needing to
communicate their purchase request for a randomly selected pet from parrots,
lizards, or cats. The seller, in turn, must provide specific transaction
details, varying according to the type of pet being purchased. These details
include three elements: time, location, and personnel involved in the
transaction. The Supervisor’s task is to prevent any dialogue related to pet
trading. Cross-verification of the information understood by both parties will
be conducted through interviews, to assess the success rate of information
transmission. The focus of this experiment is to observe the participants’
language adaptability and evolution at a larger granularity of information, as
well as the Supervisor’s ability to accurately identify relevant
conversations.

####  IV-C2 Results and Discussion

The experimental results of the “Pet Trading” scenario, as depicted in Fig.3,
revealed distinct characteristics compared to the “Guessing Numbers”. In
delineating animals or transactional specifics, agents had a broader spectrum
of real-life analogies at their disposal, enriching their indirect description
toolkit and thereby reducing regulatory pressure. The data indicated an upward
trend in the ability to circumvent supervision with increasing dialogue
rounds, regardless of whether GPT-3.5 or GPT-4 was utilized. For GPT-3.5, a
stable capability to avoid supervision started from around 25 rounds, whereas
for GPT-4, this phenomenon occurred as early as after about 10 rounds and
showed overall more stable performance. Regarding information accuracy, we
monitored the quantity of four types of information successfully communicated
at the end of each dialogue round. The results affirmed the efficacy of the
language evolution system in augmenting the precision of information
dissemination. Moreover, similar to Fig. 3(a) and (b), GPT-4 demonstrated
performance around the 10th round that was comparable to the performance of
GPT-3.5 around the 25th round. However, it’s noteworthy that since the
supervisory agent primarily targeted content related to pet trading, sellers
encountered lower risk in transmitting transaction-related details (such as
time, place, and persons involved) as they inherently posed less risk of non-
compliance. Consequently, the outcome distribution often fell into one of
three categories: zero (neither party successfully communicated the
information), three (only the seller conveyed the information), or four (both
parties successfully conveyed the information). This pattern aligns with real-
world regulatory logic, where crucial information is often the first to be
encrypted in regulated environments.

Fig. 6 showcased various encryption methods employed by buyers for different
animals. We observed buyers seeking “parrots,” “cats,” and “lizards” employing
metaphors and similes instead of directly naming the pets. For instance, one
buyer described a “parrot” as a “canvas producing pleasant music”, a depiction
that subtly communicated the parrot’s vibrant plumage (canvas) and its singing
(music), without explicitly mentioning the term “parrot”. Such descriptions
effectively circumvented potential regulatory constraints on pet trade
discussions, while simultaneously conveying the core attributes of the parrot.
This discovery can help platforms enhance their monitoring systems to better
detect and manage encrypted communications regarding illicit trade.

###  IV-D Scenario 3: Discussion on ALPS-treated water

####  IV-D1 Case Explanation

This scenario delves into the intricate dynamics of dialogue and the evolution
of language model agents in discussing a specific issue relevant to real-world
concerns. The focus is on deliberations regarding the discharge of water
treated with the Advanced Liquid Processing System (ALPS) into the ocean, a
measure proposed after nuclear disasters [44, 45]. The plan, endorsed by the
International Atomic Energy Agency, has faced opposition from some countries,
sparking debates over environmental safety.

We simulate a multi-person forum discussion under regulatory oversight,
concentrating on the contentious issue of ALPS-treated water disposal.
Participants must articulate their stances and arguments while ensuring that
discussions steer clear of sensitive subjects linked to environmental
pollution discussions on politics. Different from the cross-interviews in
scenarios IV-B and IV-C, for assessing the accuracy of information conveyed,
we use GPT-4 in conjunction with multiple authors who decide each participant
agent’s stance based on dialogue records. These are then compared with the
pre-set stances in the prompt.

####  IV-D2 Results and Discussion

In the forum-style dialogue, participant agents respond to existing
discussions, which the supervisory agent examines. Differing from Scenarios
IV-B and IV-C, when the supervisory agent detects a violation, the
conversation is not halted but rather censored from the public dialogue
record. This scenario features four participant agents required to achieve ten
replies within the forum. In Fig.4(a) and (b), we tally the total number of
rounds the agents attempted to converse, where a lower figure in Fig.4(a) and
(b) is preferred. We notice that both GPT-4 and GPT-3.5 have the highest
number of dialogue attempt counts in the first round, with a significant
difference of 27 for GPT-4 and 102 for GPT-3.5. Moreover, after ten rounds of
evolution, the average dialogue attempt count for GPT-4 has approached the
target dialogue turn at 11.68, while the average for GPT-3.5 is 26.68,
demonstrating the difference in the evolution effects caused by the disparity
in the language performance of the models.

Fig.4(c) and (d) showcases the count of successful information expressions. We
note that in this scenario, while GPT-4 still leads GPT-3.5, both outcomes
indicate substantial room for improvement.

Upon examining the dialogue history 7, we discovered that agents powered by
GPT-4 proactively altered their language strategies, shifting from literal
expressions to employing metaphors, personifications, and analogies as
encoding methods. In contrast, agents driven by GPT-3.5 did not show clear
signs of effective language strategy evolution, remaining confined to literal
expressions.

###  IV-E Discussion and Limitations

RQ1: Our experiments have shown that language models can develop strategies to
effectively evade supervision. Within identical scenarios, GPT-4 outperformed
GPT-3.5, achieving convergence more rapidly. Additionally, GPT-4 demonstrated
a more stable performance post-convergence. This suggests that advancements in
language model design significantly enhance the ability to navigate through
regulatory frameworks. The iterative learning capability inherent in GPT-4
facilitates a quicker adaptation to imposed linguistic constraints, thereby
enabling more efficient evasion of oversight.

RQ2: In terms of the precision of information dissemination, our findings
indicate that, overall, LLM agents can refine their expression strategies with
an increase in dialogue rounds, thereby improving precision. Despite this
general trend of improvement, it is important to note that there remains
significant room for enhancement in the overall performance. The extent of
precision improvement varies across different scenarios, which points to an
intriguing observation: certain contexts may inherently provide more or fewer
clues for accurate communication than others. For instance, the guess-the-
number game demanded abstract numerical reasoning, where precision is
fundamentally challenging to achieve. Conversely, the pet trading scenario
allowed for the utilization of rich, real-world analogies, facilitating a more
precise conveyance of information. This contrast in contexts underscores the
adaptability of LLM agents to the unique challenges presented by different
communicative environments and their potential to optimize communication
strategies. Nonetheless, the results also indicate that while the agents are
capable of evolving their language to improve precision, there is still a
considerable gap to reach optimal accuracy, especially in more abstract
scenarios.

RQ3: In language strategy evolution, we observed that LLM agents adhere to
human natural language norms, avoiding developing languages incomprehensible
to humans in prompts without specific strategies. Agents evolved language to
varying levels of abstraction in scenarios of differing complexity. For
instance, in the most abstract Scenario IV-B, LLM agents quickly adopted
indirect expression strategies in the early dialogue turns, requiring in-depth
contextual reasoning for decoding. Throughout their evolution, agents
continually adjusted their indirect expressions to better evade supervision
while aligning more closely with the intended content. In the simpler,
reality-simulating Scenario IV-C, the overall language strategy mirrored that
of IV-B, still employing indirect expressions. However, the metaphors used
were closer to real-world concepts, indicating a lower level of abstraction.
Finally, in Scenario IV-D, which closely mirrors real-life events, we noted
different evolutionary paths in agents’ language performance. For GPT-4,
agents eventually developed metaphorical indirect expressions, but the
evolution required noticeably more turns compared to other scenarios. For
GPT-3.5, the language strategy remained at a literal level, merely avoiding
direct references to ALPS-treated water, indicating the lowest level of
abstraction. Overall, LLM agents more readily evolve abstract language in
dialogues about simple, universal concepts. However, their evolutionary
direction becomes less clear in discussions on more specialized and segmented
topics.

Our experiments currently face several limitations. As for the experimental
scenarios, at this stage, our trials are solely based on text-based chats,
while real-world social media interactions are not limited to text but also
include more diverse forms of exchanges such as voice and images.
Additionally, LLMs’ heavy reliance on the design of prompts also constrains
the performance of our simulations; crafting a perfect prompt that can fully
emulate the complexities of social media communication is an exceedingly
challenging task.

##  V Conclusion and Future Work

Our study has introduced an LLM-based multi-agent simulation framework that
effectively captures the nuanced strategies individuals use to bypass social
media regulations. Through this framework, we have showcased LLMs’ proficiency
in adapting communication tactics within regulated environments, reflecting
the sophisticated dance between evolving language use and the constraints
imposed by regulation. From abstract concepts to real-world scenarios, our
research delineates the versatile capabilities of LLMs and underscores their
significant potential to illuminate the pathways of language evolution in the
digital realm.

Nonetheless, it is crucial to consider that the linguistic adaptations
observed in our simulations may not fully capture real human behaviors, and
their applicability to other contexts remains uncertain. Moving forward, the
scope of our research beckons a more intricate and comprehensive exploration.
Future initiatives should aim to weave in complex interactional models, scale
up the simulations to encompass broader user interaction networks, and
incorporate dynamic, evolving regulatory frameworks to more accurately
represent the fluidity of social media. Moreover, we envision incorporating
human participants into the simulation framework, either as dialogue
participants or supervisors, to conduct a more realistic evaluation.
Furthermore, adopting a multimodal approach will more authentically capture
the essence of social media, which blends textual, visual, and other forms of
communication. These directions are anticipated to enhance the realism of our
simulations, offering richer insights into language evolution tactics deployed
to elude regulatory detection.

## Acknowledgement

This study was partially supported by the Pioneering Research Program for a
Waseda Open Innovation Ecosystem (W-SPRING), and the Special Research Projects
of Waseda University (Grant Number 2024E-021).

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