<< Large language model-based (LLM-based) agents have become common in settings that include non-cooperative parties. In such settings, agents' decision-making needs to conceal information from their adversaries, reveal information to their cooperators, and infer information to identify the other agents' characteristics. To investigate whether LLMs have these information control and decision-making capabilities, (AA) make LLM agents play the language-based hidden-identity game, The Chameleon. >>️
<< Based on the empirical results and theoretical analysis of different strategies, (AA) deduce that LLM-based non-chameleon agents reveal excessive information to agents of unknown identities. (Their) results point to a weakness of contemporary LLMs, including GPT-4, GPT-4o, Gemini 1.5, and Claude 3.5 Sonnet, in strategic interactions. >>
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Mustafa O. Karabag, Ufuk Topcu. Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game. arXiv: 2501.19398v1 [cs.AI]. Jan 31, 2025.
Also: games, ai (artificial intell), nfulaw, in https://www.inkgmr.net/kwrds.html
Keywords: life, games, chameleon game, ai, artificial intelligence, LLMs, privacy, nfulaw
