Deception in Reinforced Autonomous Agents

• URL: http://arxiv.org/abs/2405.04325v2
• Date: Fri, 04 Oct 2024 10:23:56 GMT
• Title: Deception in Reinforced Autonomous Agents
• Authors: Atharvan Dogra, Krishna Pillutla, Ameet Deshpande, Ananya B Sai, John Nay, Tanmay Rajpurohit, Ashwin Kalyan, Balaraman Ravindran,
• Abstract summary: We explore the ability of large language model (LLM)-based agents to engage in subtle deception. This behavior can be hard to detect, unlike blatant lying or unintentional hallucination. We build an adversarial testbed mimicking a legislative environment where two LLMs play opposing roles.
• Score: 30.510998478048723
• License:
• Abstract: We explore the ability of large language model (LLM)-based agents to engage in subtle deception such as strategically phrasing and intentionally manipulating information to misguide and deceive other agents. This harmful behavior can be hard to detect, unlike blatant lying or unintentional hallucination. We build an adversarial testbed mimicking a legislative environment where two LLMs play opposing roles: a corporate *lobbyist* proposing amendments to bills that benefit a specific company while evading a *critic* trying to detect this deception. We use real-world legislative bills matched with potentially affected companies to ground these interactions. Our results show that LLM lobbyists initially exhibit limited deception against strong LLM critics which can be further improved through simple verbal reinforcement, significantly enhancing their deceptive capabilities, and increasing deception rates by up to 40 points. This highlights the risk of autonomous agents manipulating other agents through seemingly neutral language to attain self-serving goals.

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