Can LLMs Follow Simple Rules?

• URL: http://arxiv.org/abs/2311.04235v3
• Date: Fri, 8 Mar 2024 17:04:49 GMT
• Title: Can LLMs Follow Simple Rules?
• Authors: Norman Mu, Sarah Chen, Zifan Wang, Sizhe Chen, David Karamardian, Lulwa Aljeraisy, Basel Alomair, Dan Hendrycks, David Wagner
• Abstract summary: Rule-following Language Evaluation Scenarios (RuLES) is a framework for measuring rule-following ability in Large Language Models. RuLES consists of 14 simple text scenarios in which the model is instructed to obey various rules while interacting with the user. We show that almost all current models struggle to follow scenario rules, even on straightforward test cases.
• Score: 28.73820874333199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) are deployed with increasing real-world responsibilities, it is important to be able to specify and constrain the behavior of these systems in a reliable manner. Model developers may wish to set explicit rules for the model, such as "do not generate abusive content", but these may be circumvented by jailbreaking techniques. Existing evaluations of adversarial attacks and defenses on LLMs generally require either expensive manual review or unreliable heuristic checks. To address this issue, we propose Rule-following Language Evaluation Scenarios (RuLES), a programmatic framework for measuring rule-following ability in LLMs. RuLES consists of 14 simple text scenarios in which the model is instructed to obey various rules while interacting with the user. Each scenario has a programmatic evaluation function to determine whether the model has broken any rules in a conversation. Our evaluations of proprietary and open models show that almost all current models struggle to follow scenario rules, even on straightforward test cases. We also demonstrate that simple optimization attacks suffice to significantly increase failure rates on test cases. We conclude by exploring two potential avenues for improvement: test-time steering and supervised fine-tuning.

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