Avalon's Game of Thoughts: Battle Against Deception through Recursive
Contemplation
- URL: http://arxiv.org/abs/2310.01320v3
- Date: Tue, 24 Oct 2023 12:51:28 GMT
- Title: Avalon's Game of Thoughts: Battle Against Deception through Recursive
Contemplation
- Authors: Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen
Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang
- Abstract summary: This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments.
We introduce a novel framework, Recursive Contemplation (ReCon), to enhance LLMs' ability to identify and counteract deceptive information.
- Score: 80.126717170151
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent breakthroughs in large language models (LLMs) have brought remarkable
success in the field of LLM-as-Agent. Nevertheless, a prevalent assumption is
that the information processed by LLMs is consistently honest, neglecting the
pervasive deceptive or misleading information in human society and AI-generated
content. This oversight makes LLMs susceptible to malicious manipulations,
potentially resulting in detrimental outcomes. This study utilizes the
intricate Avalon game as a testbed to explore LLMs' potential in deceptive
environments. Avalon, full of misinformation and requiring sophisticated logic,
manifests as a "Game-of-Thoughts". Inspired by the efficacy of humans'
recursive thinking and perspective-taking in the Avalon game, we introduce a
novel framework, Recursive Contemplation (ReCon), to enhance LLMs' ability to
identify and counteract deceptive information. ReCon combines formulation and
refinement contemplation processes; formulation contemplation produces initial
thoughts and speech, while refinement contemplation further polishes them.
Additionally, we incorporate first-order and second-order perspective
transitions into these processes respectively. Specifically, the first-order
allows an LLM agent to infer others' mental states, and the second-order
involves understanding how others perceive the agent's mental state. After
integrating ReCon with different LLMs, extensive experiment results from the
Avalon game indicate its efficacy in aiding LLMs to discern and maneuver around
deceptive information without extra fine-tuning and data. Finally, we offer a
possible explanation for the efficacy of ReCon and explore the current
limitations of LLMs in terms of safety, reasoning, speaking style, and format,
potentially furnishing insights for subsequent research.
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