Tsinghua NLP's LexReward: taxonomy-driven reward modeling for legal LLMs
TsinghuaNLP · hf · 2026-10-05
Tsinghua NLP introduces LexReward, a taxonomy-driven reward modeling framework for legal language models, addressing the coarse-grained, low-interpretability nature of holistic reward judgments.
- Three complementary quality dimensions: Style (lexical/syntactic quality), Element (legal subjects, facts, statutes, decisions), and Chain (ordering, completeness, correctness, non-redundancy of legal reasoning)
- Per-dimension rubrics specify evaluation criteria and quality levels
- Rubric-based rewards construct pairwise preference data for DPO and reward-model training, improving performance across all three dimensions
- Dimension-specific reward models (LexRM) enable downstream RL optimization without reference answers at reward time
Experiments show the rewards reliably distinguish legal response quality and validate the taxonomy design.
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