A review proposes that LLMs can serve as tools for a more integrative and cumulative cognitive science when used under human oversight.
Reply to "Emergent LLM behaviors are observationally equivalent to data leakage"
1 Pith paper cite this work. Polarity classification is still indexing.
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 T\"ornberg [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.
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Addressing Longstanding Challenges in Cognitive Science with Language Models
A review proposes that LLMs can serve as tools for a more integrative and cumulative cognitive science when used under human oversight.