---
title: "CLASE: A Hybrid Method for Chinese Legalese — Ma 2026"
description: "LLMs can write Chinese legal text that is factually sound but stylistically off."
authors: ["Yiran Rex Ma", "Yuxiao Ye", "Huiyuan Xie"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.46/"
doi: "10.63317/2xrbbj7oaghv"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/ma-2026-clase-hybrid-method-chinese-legalese/"
---
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# CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation

**Authors:** Yiran Rex Ma, Yuxiao Ye, Huiyuan Xie · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.46/) · [PDF](https://aclanthology.org/2026.lrec-1.46.pdf) · [DOI 10.63317/2xrbbj7oaghv](https://doi.org/10.63317/2xrbbj7oaghv) · [OpenAlex](https://openalex.org/W7160029202)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · 1 in our library, of the 140673 papers whose bibliographies we read

## Summary
LLMs can write Chinese legal text that is factually sound but stylistically off. It may be too colloquial or use non-standard terms, or it may overcorrect with verbose, archaic phrasing and even invented legal concepts. Expert rubrics for these tacit norms are hard to write. Reference-based metrics blur meaning with style, and plain LLM judges are opaque and inconsistent. The authors propose CLASE, a reference-free metric for the stylistic quality of Chinese legal writing.

They start from the reasoning sections of real judgment documents. An LLM first strips away the professional style, then reconstructs it, producing contrastive pairs of authentic versus LLM-restored text with no human scoring. These pairs are used to learn weights for linguistic features and to build a pool of scoring "experiences" for an LLM judge. The final score combines both signals.

On 200 Chinese legal documents, CLASE matches human judgments considerably better than traditional metrics and pure LLM-as-a-judge baselines. It also returns interpretable score breakdowns and concrete revision suggestions, which makes it practical for legal text generation pipelines.

## Key points
- **Three-stage pipeline:**
  - Contrastive pair synthesis: t_reverse = π1(t_gold), then t_restored = π2(t_reverse).
  - Training-free contrastive learning that fills experience pools.
  - Hybrid scoring.
- **Two components:**
  - CLASE-Obj uses linguistic features weighted by logistic regression. It works best with k = 25 features, and adding more features introduces noise.
  - CLASE-Subj is an experience-guided LLM judge that gives natural-language feedback.
- **Why hybrid:** feature-only scoring used as a reinforcement learning reward risks Goodhart's Law, since models could game surface markers. The LLM component adds semantic grounding and actionable advice.
- **Limitations:**
  - The method is tested only on Chinese civil judgments, and the objective features are language-specific.
  - The synthetic pairs were not manually audited at scale.
  - It is costlier to run than BLEU.
- **Code and data:** github.com/rexera/CLASE (LREC 2026, pp. 642–653).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Law](https://md.research.elamur.ai/free/shelves/law/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ma2026clase,
  title = {{CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation}},
  author = {Ma, Yiran Rex and Ye, Yuxiao and Xie, Huiyuan},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2xrbbj7oaghv},
  url = {https://doi.org/10.63317/2xrbbj7oaghv}
}
```

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