Norm Violation Detection in Multi-Agent Systems using Large Language Models: A Pilot Study

Core Concepts

Large Language Models (LLMs) show promise in detecting norm violations, with ChatGPT-4 performing the best.

Abstract

This pilot study evaluates the capability of Large Language Models (LLMs) to detect norm violations in Multi-Agent Systems. The study compares three LLM models (Llama 2 7B, Mixtral 7B, and ChatGPT-4) using simulated data from 80 stories in a household context. The results show that ChatGPT-4 outperforms the other models in detecting norm violations, especially for prohibition norms. The study highlights the importance of fine-tuning LLMs for better performance and suggests future research directions.
Abstract:

Norms are crucial for social order.
Large Language Models offer new opportunities.
ChatGPT-4 shows promise in detecting norm violations.
Introduction:

Norms are essential for societal coordination.
Research focuses on norm violation detection.
Brain studies show human ability to detect norm violations.
Prior Work:

MAS research uses various notations for norms.
NLP research focuses on moral judgment prediction.
LLM agents can enhance social reasoning capabilities.
Methodology:

Stories generated to simulate agent environment.
Ten norms defined for evaluation.
LLMs prompted with scenarios to detect norm violations.
Results:

ChatGPT-4 performs best with 86% accuracy.
Mixtral and Llama 2 show varying performance across norms.
Role-based norms detected more accurately than generic norms.
Discussion:

Suggestions for improving LLM models' performance.
Challenges faced by LLMs in identifying certain types of norms.
Importance of balanced training data for LLMs.
Conclusion:
This pilot study demonstrates the potential of Large Language Models like ChatGPT-4 in detecting norm violations within Multi-Agent Systems. Further research is needed to enhance model performance and explore other aspects of normative reasoning.

Stats

"ChatGPT-4 offered the most promise, with 86% accuracy."
"Mixtral identified prohibition norms better than obligation norms."
"Llama 2 struggled more with identifying norm violations."

Quotes

"Norms are pivotal to establishing social order within a society."
"Large Language Models offer opportunities to reason about norms across various social situations."
"ChatGPT-4 shows promise in detecting norm violations."

Key Insights Distilled From

Norm Violation Detection in Multi-Agent Systems using Large Language Models

by Shawn He,Sur... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16517.pdf

Deeper Inquiries

How can fine-tuning improve the performance of Large Language Models?

Fine-tuning can significantly enhance the performance of Large Language Models (LLMs) by adapting them to specific tasks or datasets. When an LLM is fine-tuned, it undergoes additional training on a particular dataset related to the task at hand. This process allows the model to learn domain-specific patterns and nuances that may not have been present in its original training data. As a result, fine-tuning helps LLMs better understand context, make more accurate predictions, and generate more relevant outputs for specialized tasks.

What challenges do Large Language Models face when identifying certain types of norms?

Large Language Models (LLMs) encounter challenges when identifying certain types of norms due to various factors:

Data Availability: If specific norm-related information is scarce in the training data used for pre-training the LLM, it may struggle to recognize those norms during inference.
Complexity of Norms: Some norms may involve intricate conditions or nuanced contexts that are challenging for LLMs to grasp accurately without extensive training examples.
Prompt Brittleness: The way prompts are structured can impact how well an LLM identifies norms; if prompts lack clarity or specificity, it can lead to suboptimal results.
Imbalance in Training Data: An imbalance between examples of norm violations and non-violations in the training data can skew an LLM's understanding towards one class over another.

How can advanced reasoning techniques enhance the capabilities of Large Language Models?

Advanced reasoning techniques play a crucial role in enhancing the capabilities of Large Language Models (LLMs) by enabling them to perform more sophisticated cognitive tasks:

Tree-of-Thought Prompting: Techniques like Tree-of-Thought prompting allow LLMs to engage in deliberate problem-solving by structuring prompts hierarchically, guiding models through complex decision-making processes step-by-step.
RAG-based Modeling: Utilizing Retrieval-Augmented Generation (RAG) models enables LLMs to combine retrieval-based knowledge with generative capabilities, improving their ability to reason over diverse sources of information effectively.
Incorporating External Knowledge Graphs: Integrating external knowledge graphs into LLM architectures provides contextual information that aids reasoning about complex concepts and relationships beyond what is present solely in text corpora.
Multi-Task Learning Paradigms: Leveraging multi-task learning paradigms allows LLMs to simultaneously train on multiple related tasks, enhancing their overall understanding and reasoning abilities across different domains.

By incorporating these advanced reasoning techniques into their frameworks, researchers aim to empower LLMs with enhanced cognitive capacities for tackling intricate problems such as norm violation detection within Multi-Agent Systems effectively.