Datasets:

TsinghuaNLP
/

LexReward

High-quality legal responses require more than a correct final answer. They should use precise and objective language, identify relevant legal subjects and facts, apply appropriate statutes, and present complete and coherent reasoning. Coarse-grained holistic rewards can obscure these distinct requirements.

LexReward is a taxonomy-driven reward framework that decomposes legal response quality into three complementary dimensions:

Dimension	Evaluation criteria
Style	Word specificity, subjective-word control, sentence coherence, sentence structure, and collocation correctness.
Element	Legal subjects, case facts, statutory grounds, and judicial decisions.
Chain	Reasoning order, completeness, correctness, and non-redundancy.

The framework operationalizes these criteria with interpretable rule-based evaluators and LLM judges. Rubric scores guide preference construction from diverse model-generated responses. We then train LexRM, a family of dimension-specific reward models, with a Bradley-Terry pairwise objective.

The paper evaluates rubric rewards and LexRM through preference selection and test-time scaling, and studies downstream policy optimization with DPO and GRPO. Its experiments show that rubric-guided preference data and learned reward models improve legal response quality across the three dimensions.

Dataset Statistics

The following statistics describe the paper's experiments, not dataset files distributed in this code package.

Dimension	Source data	Training or construction data	Evaluation data
Style	CLASE	4,000 training queries used for preference construction	1,000 official test instances
Element	JEC-QA and LexChain	2,736 source instances yielding 2,244 preference pairs	3,000 LegalDelta in-domain test instances
Chain	LexChain	9,550 training instances, including 4,000 queries allocated to preference training	1,000 official test instances

Dataset Format

Both constructors output chat-format RM preference pairs:

{
  "chosen": [
    {"role": "user", "content": "Case facts and task instructions..."},
    {"role": "assistant", "content": "Preferred model-generated response..."}
  ],
  "rejected": [
    {"role": "user", "content": "Case facts and task instructions..."},
    {"role": "assistant", "content": "Dispreferred model-generated response..."}
  ],
  "meta": {
    "source_row_id": 0,
    "pair_group": "A",
    "tier_ratio": "5:3:2",
    "chosen_index": 0,
    "rejected_index": 1,
    "chosen_score": -0.2,
    "rejected_score": -0.8
  }
}

Field Descriptions

Field	Description
`chosen`	Chat messages containing the prompt and preferred generated response.
`rejected`	Chat messages containing the same prompt and dispreferred generated response.
`meta.source_row_id`	Identifier of the source query in the candidate pool.
`meta.pair_group` / `meta.tier`	Pair category A, B, or C. CLASE uses `pair_group`; LexChain uses `tier`.
`meta.tier_ratio`	Construction ratio, `5:3:2`.
`meta.chosen_index` / `meta.rejected_index`	Selected candidate indices in the original five-response pool.
Scores	CLASE records selected scores in `meta`; LexChain also provides top-level `chosen_score` and `rejected_score`. Higher scores are preferred.

LexChain outputs additionally preserve chosen_model, rejected_model, all five scores in meta.row_scores, and meta.judge_model. Both training scripts consume the shared chosen and rejected fields.

Dataset Example

The following pair is synthetic and shown in English for readability. The experiments use Chinese legal text.

{
  "chosen": [
    {
      "role": "user",
      "content": "Rewrite the following case summary as a formal judicial explanation without adding facts: the loan is due and remains unpaid."
    },
    {
      "role": "assistant",
      "content": "The loan has matured, and the borrower has not fulfilled the repayment obligation. The outstanding loan therefore remains payable."
    }
  ],
  "rejected": [
    {
      "role": "user",
      "content": "Rewrite the following case summary as a formal judicial explanation without adding facts: the loan is due and remains unpaid."
    },
    {
      "role": "assistant",
      "content": "The money is due and they have not paid it back, so they still need to pay."
    }
  ],
  "meta": {
    "source_row_id": 0,
    "pair_group": "A",
    "tier_ratio": "5:3:2",
    "chosen_index": 0,
    "rejected_index": 1,
    "chosen_score": -0.2,
    "rejected_score": -0.8
  }
}

Citation

If you use this work, please cite our arXiv preprint:

@article{cai2026lexreward,
  title = {{LexReward}: A Taxonomy-Driven Reward Framework for Legal Language Models},
  author = {Yida Cai and Xin Dai and Bingxiang He and Huiyuan Xie and Yuxiao Ye and Zhenghao Liu and Yang Bai and Zhiyuan Liu},
  year = {2026},
  journal = {arXiv preprint arXiv:2609.39071}
}