MISR: Measuring Instrumental Self-Reasoning in Frontier Models

• URL: http://arxiv.org/abs/2412.03904v1
• Date: Thu, 05 Dec 2024 06:20:47 GMT
• Title: MISR: Measuring Instrumental Self-Reasoning in Frontier Models
• Authors: Kai Fronsdal, David Lindner,
• Abstract summary: We evaluate the instrumental self-reasoning ability of large language model (LLM) agents.<n>We find that instrumental self-reasoning ability emerges only in the most capable frontier models.<n>Our evaluations can be used to measure increases in instrumental self-reasoning ability in future models.
• Score: 7.414638276983446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a suite of tasks to evaluate the instrumental self-reasoning ability of large language model (LLM) agents. Instrumental self-reasoning ability could improve adaptability and enable self-modification, but it could also pose significant risks, such as enabling deceptive alignment. Prior work has only evaluated self-reasoning in non-agentic settings or in limited domains. In this paper, we propose evaluations for instrumental self-reasoning ability in agentic tasks in a wide range of scenarios, including self-modification, knowledge seeking, and opaque self-reasoning. We evaluate agents built using state-of-the-art LLMs, including commercial and open source systems. We find that instrumental self-reasoning ability emerges only in the most capable frontier models and that it is highly context-dependent. No model passes the the most difficult versions of our evaluations, hence our evaluation can be used to measure increases in instrumental self-reasoning ability in future models. We open-source our evaluations at https://github.com/kaifronsdal/Self-Reasoning-Evals.

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