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

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.

Publication Details

Journal
ArXiv.org
Published
2025-06-23
Primary Topic
Language and cultural evolution
Type
article
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Reply to "Emergent LLM behaviors are observationally equivalent to data leakage"

ArXiv.org
Language and cultural evolution
article

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

article en

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.

ArXiv.org
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Language and cultural evolution
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Reply to "Emergent LLM behaviors are observationally equivalent to data leakage" · ArXiv.org (2025) | TGRS Research Map | TGRS