LAURA: Knowledge Distillation for Interpretable Ambiguous Clause Identification in Legal Contracts
Legal contracts contain ambiguities that expose enterprises to financial and legal risks. Some ambiguities allow flexible interpretation without triggering disputes, while others lead to significant legal conflicts. This makes identification alone insufficient, and interpretable rationale analysis essential. We propose LAURA, a post-training framework for interpretable ambiguous clause identification. LAURA leverages knowledge distillation with an IRAC-Unlearning prompting technique to transfer knowledge from a teacher LLM to an open-weight student model (<=1B parameters), which is then trained using a joint objective combining classification and rationale generation losses. The framework...
| Authors | Amrita Singh, Aditya Joshi, Jiaojiao Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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