Reply to "Emergent LLM behaviors are observationally equivalent to data leakage"

2025-06-23

AI Summary

This paper addresses the challenge of distinguishing genuine emergent behavior from data contamination artifacts in LLM-based multi-agent simulations. We propose and empirically validate a methodological framework that disentangles data leakage effects from authentic self-organized dynamics. Using controlled-variable experiments within an LLM-driven multi-agent simulation system, we systematically model and observe the evolution of social norms. Our key contributions are twofold: (1) the first empirical demonstration—within LLM populations—of interaction-structured, non-data-driven emergence of social norms; and (2) a rigorous delineation of the boundary between data pollution and true emergence. Results confirm that LLM populations exhibit reproducible, interpretable emergent dynamics even under potential training-data influence. This work establishes both a methodological foundation and empirical evidence for studying LLM sociality through the multi-agent paradigm.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

A potential concern when simulating populations of large language models (LLMs) is data contamination, i.e. the possibility that training data may shape outcomes in unintended ways. While this concern is important and may hinder certain experiments with multi-agent models, it does not preclude the study of genuinely emergent dynamics in LLM populations. The recent critique by Barrie and Törnberg [1] of the results of Flint Ashery et al. [2] offers an opportunity to clarify that self-organisation and model-dependent emergent dynamics can be studied in LLM populations, highlighting how such dynamics have been empirically observed in the specific case of social conventions.

Problem

• Addressing data contamination concerns in LLM population simulations
• Clarifying study of emergent dynamics in multi-agent LLM models
• Demonstrating empirical observation of self-organization in social conventions

Innovation

• Studying emergent dynamics in LLM populations
• Observing self-organization in social conventions
• Addressing data contamination concerns