paper-with-me

홈 › Papers

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

2025-06-23 · Ariel Flint Ashery, Luca Maria Aiello, Andrea Baronchelli

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.

📄 PDF Abstract BibTeX arXiv:2506.18600

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Emergent LLM behaviors are observationally equivalent to data leakage

2025-05-26 · Christopher Barrie, Petter Törnberg

Ashery et al. recently argue that large language models (LLMs), when paired to play a classic "naming game," spontaneously develop linguistic conventions reminiscent of human social norms. Here, we show that their result…

Memorization

Locally- but not Globally-identified SVARs

2025-04-02 · Emanuele Bacchiocchi, Toru Kitagawa

This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally. In this case there exists a set of isolated structural parameter points that are…

SVARs with breaks: Identification and inference

2024-05-08 · Emanuele Bacchiocchi, Toru Kitagawa

In this paper we propose a class of structural vector autoregressions (SVARs) characterized by structural breaks (SVAR-WB). Together with standard restrictions on the parameters and on functions of them, we also consider…

Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms

2026-06-01 · Zixuan Jin, Wenzhuo Zhang, Shuxian Quan, Zirui Dong 외 arxiv

Robot swarms can exhibit coherent collective behaviors through local perception, limited communication and decentralized decision-making, yet modeling and controlling such emergence remains challenging when behaviors unf…

Reinforcement Learning

Limit Orders and Knightian Uncertainty

2022-08-23 · Michael Greinecker, Christoph Kuzmics

A range of empirical puzzles in finance has been explained as a consequence of traders being averse to ambiguity. Ambiguity averse traders can behave in financial portfolio problems in ways that cannot be rationalized as…