---
title: "Pub-LawBench: Public-Oriented Benchmarking for LegalAI (2026)"
description: "Most LegalAI benchmarks, such as LegalBench and LawBench, are built around what lawyers need: issue spotting, citations… 58 references."
authors: ["Qiaoyu Zheng", "Zehan Ma", "Yijing Zhang", "Qiqi Wang", "Huijia Li", "Qian Liu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1680/"
doi: "10.18653/v1/2026.acl-long.1680"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zheng-2026-pub-lawbench-public-oriented-benchmarking/"
---
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# Pub-LawBench: Public-Oriented Benchmarking for LegalAI

**Authors:** Qiaoyu Zheng, Zehan Ma, Yijing Zhang, [Qiqi Wang](https://md.research.elamur.ai/free/en/authors/qiqi-wang/), [Huijia Li](https://md.research.elamur.ai/free/en/authors/huijia-li/) and [Qian Liu](https://md.research.elamur.ai/free/en/authors/qian-liu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1680/) · [PDF](https://aclanthology.org/2026.acl-long.1680.pdf) · [DOI 10.18653/v1/2026.acl-long.1680](https://doi.org/10.18653/v1/2026.acl-long.1680) · [OpenAlex](https://openalex.org/W7166883518)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most LegalAI benchmarks, such as LegalBench and LawBench, are built around what lawyers need: issue spotting, citations, and rigorous reasoning. Ordinary people with legal questions need something different. They want plain, practical guidance and documents they can actually use. Pub-LawBench is designed to test that public-facing ability. Drawing on legal functionalism, genre analysis and Speech Act Theory, the authors split lay users' needs into two task families. Instant QA has 9 subtasks covering understanding, reasoning and expression. Legal Gen has 4 document-drafting subtasks. Each output is judged along three axes: how well it conforms to legal norms, how relevant its content is, and how usable its format is. To mimic how non-experts use these tools, 17 commercial, open-weight and legal-specialized LLMs were run with simple prompts and Chain-of-Thought only, without agents or RAG. The models generally produce documents that look professionally structured but lack concrete, actionable advice. CoT can even lower accuracy on legal reasoning tasks. The work shows that professional-grade rigor does not automatically make a model helpful to the public.

## Key points
- Instant QA reuses existing Chinese and English datasets, grouped into three levels:
  - Understanding: JEC-QA-KD, CAIL, Unfair-ToS, LEdGAR.
  - Reasoning: CaseHOLD, COLIEE, JEC-QA-CA.
  - Expression: QA-Corpus, Laws-QA.
- Legal Gen has four drafting stages: Fact Extraction, Reasoning, Defense and Judgment Generation.
- CoT gave mixed results. It helped on LEdGAR (Claude Sonnet 4.5 gained +39.4) but hurt on Unfair-ToS (Gemini 3 lost 17.6) and on COLIEE. The authors attribute this to a "validity illusion" and reasoning drift.
- In case studies, the proposed KeyCover metric flagged a verbose output (54.8) that BERTScore rated highly (77.9). The Legal Normativity metric caught a citation to the repealed Property Law of the PRC.
- Adding BM25-based RAG over the National Laws and Regulations Database improved Law_QA scores by about 0.09–0.11 without changing model rankings. For example, DISC-LawLLM rose from 0.245 to 0.356.

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

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## Cite

Cite as: Qiaoyu Zheng, Zehan Ma, Yijing Zhang, Qiqi Wang, Huijia Li and Qian Liu. 2026. Pub-LawBench: Public-Oriented Benchmarking for LegalAI. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{zheng2026pub,
  title = {{Pub-LawBench: Public-Oriented Benchmarking for LegalAI}},
  author = {Qiaoyu Zheng, Zehan Ma, Yijing Zhang, Qiqi Wang, Huijia Li and Qian Liu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1680},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1680}
}
```

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*Updated 2 November 2026*