aiqbench analysis
This research demonstrates how targeted adaptation can steer large language models to prioritize either the literal text or the underlying intent of legal rules, significantly altering their behavior in real-world scenarios. By mapping these behaviors onto a low-dimensional internal space, the study reveals how legal reasoning within LLMs is structured and offers a method to direct model alignment with specific ethical frameworks.
Directing large language models to follow the letter or spirit of the law
arXiv:2609.23083v1 Announce Type: new Abstract: The distinction between the spirit and letter of the law is a central issue across research and everyday life, and a growing concern for building safe, intelligent machines. What is this distinction based on, and how can we develop machines that follow the intention behind a rule? We used targeted adaptation that made large language models prioritize the spirit or letter of the law. With minimal modifications, our method significantly changed LLM behavior across diverse measures, novel vignettes, real-world scenarios, and influential legal cases. An analysis of model internals revealed a low-dimensional space with three interpretable dimensions matching a formal pre-specified framework for the geometry of legal concepts. These findings show how legal thought in LLMs may be organized and directed.
Abstract:The distinction between the spirit and letter of the law is a central issue across research and everyday life, and a growing concern for building safe, intelligent machines. What is this distinction based on, and how can we develop machines that follow the intention behind a rule? We used targeted adaptation that made large language models prioritize the spirit or letter of the law. With minimal modifications, our method significantly changed LLM behavior across diverse measures, novel vignettes, real-world scenarios, and influential legal cases. An analysis of model internals revealed a low-dimensional space with three interpretable dimensions matching a formal pre-specified framework for the geometry of legal concepts. These findings show how legal thought in LLMs may be organized and directed.
Computation and Language (cs.CL)
arXiv:2609.23083 [cs.CL]
(or arXiv:2609.23083v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.23083 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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