LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context
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Abstract (arXiv)
While the validity of LLMs' use in the legal context remains subject to ethical and legal debate, legal professionals are already experimenting with personal LLMs, if only for translation and reformulation. However, even such a seemingly innocuous use can introduce biases through case processing speed if LLM assistants selectively refuse assistance on certain topics. To better anticipate such biases, we investigate several modern small LLMs that are most likely to be used as on-device assistants, to assess the impact of overrefusal on legal prompts. Surprisingly, we find that authority-style prefixes (``you are acting as an assistant of the national supreme court'', ``[...] defense lawyer'') systematically increase refusal rates by 2--20x over the no-prefix baseline, while a known role-play jailbreak prefix shows mixed effects, sharply increasing refusals in some models and barely shifting them in others. The finding suggests that small on-prem deployable LLMs are unstable under contextual framings that a real institutional user might naturally introduce, and further investigation is essential to minimize opportunities for bias.
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References · 10
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
- 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 223
- 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 176
- 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 150
- 2023Large Language Models in Law: A Surveycited by 128
- 2021Lawformer: A Pre-trained Language Model for Chinese Legal Long Documentscited by 116
- 2026Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenderscited by 10
- 2026Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignmentcited by 7
- 2025OVERT: A Benchmark for Over-Refusal Evaluation on Text-to-Image Modelscited by 5
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