InferAct: Inferring Safe Actions for LLM-Based Agents Through Preemptive Evaluation and Human Feedback

• URL: http://arxiv.org/abs/2407.11843v2
• Date: Thu, 17 Oct 2024 11:26:10 GMT
• Title: InferAct: Inferring Safe Actions for LLM-Based Agents Through Preemptive Evaluation and Human Feedback
• Authors: Haishuo Fang, Xiaodan Zhu, Iryna Gurevych,
• Abstract summary: This paper introduces InferAct, a novel approach to proactively detect potential errors before risky actions are executed. InferAct acts as a human proxy, detecting unsafe actions and alerting users for intervention. Experiments on three widely-used tasks demonstrate the effectiveness of InferAct.
• Score: 70.54226917774933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A crucial requirement for deploying LLM-based agents in real-life applications is the robustness against risky or even irreversible mistakes. However, the existing research lacks a focus on preemptive evaluation of reasoning trajectories performed by LLM agents, leading to a gap in ensuring safe and reliable operations. To explore better solutions, this paper introduces InferAct, a novel approach that leverages the belief reasoning ability of LLMs, grounded in Theory-of-Mind, to proactively detect potential errors before risky actions are executed (e.g., `buy-now' in automatic online trading or web shopping). InferAct acts as a human proxy, detecting unsafe actions and alerting users for intervention, which helps prevent irreversible risks in time and enhances the actor agent's decision-making process. Experiments on three widely-used tasks demonstrate the effectiveness of InferAct, presenting a novel solution for safely developing LLM agents in environments involving critical decision-making.

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