LexReward: A Taxonomy-Driven Reward Framework for Legal Language Models
Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-grained holistic judgments, providing limited domain specificity and interpretability. We introduce LexReward, a taxonomy-driven framework for legal reward modeling. LexReward chara
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Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-grained holistic judgments, providing limited domain specificity and interpretability. We introduce LexReward, a taxonomy-driven framework for legal reward modeling. LexReward chara
This work introduces a structured approach to reward modeling for legal LLMs, moving beyond coarse-grained judgments. For ML engineers, understanding such domain-specific reward frameworks is crucial for developing and deploying high-quality, reliable AI systems in regulated industries.