README.md

---
license: mit
task_categories:
- sentence-similarity
- text-generation
tags:
- legal
- RAG
- LCLM
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: ccl
    path: data/ccl-*
  - split: tax
    path: data/tax-*
dataset_info:
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: relevant_laws
    list:
    - name: law_name
      dtype: string
    - name: section_content
      dtype: string
    - name: section_num
      dtype: string
  - name: reference_answer
    dtype: string
  - name: reference_laws
    list:
    - name: law_name
      dtype: string
    - name: section_content
      dtype: string
    - name: section_num
      dtype: string
  splits:
  - name: ccl
    num_bytes: 18873695
    num_examples: 3729
  - name: tax
    num_bytes: 2227708
    num_examples: 50
  download_size: 4728201
  dataset_size: 21101403
---

# 👩🏻‍⚖️ NitiBench: A Thai Legal Benchmark for RAG

**[[📄 Technical Report](https://arxiv.org/pdf/2502.10868)] | [[👨‍💻 Github Repository](https://github.com/vistec-AI/nitibench/)]**

This dataset provides the **test** data for evaluating LLM frameworks, such as RAG or LCLM. The benchmark consists of two datasets:
- [NitiBench-CCL](#️-nitibench-ccl)
- [NitiBench-Tax](#-nitibench-tax)

## 🏛️ NitiBench-CCL

Derived from the [WangchanX-Legal-ThaiCCL-RAG Dataset](https://huggingface.co/datasets/airesearch/WangchanX-Legal-ThaiCCL-RAG), our version includes an additional preprocessing step in which we separate the reasoning process from the final answer. The dataset contains 35 pieces of legislation related to **C**orporate and **C**ommercial **L**aw (CCL). Information about the 35 pieces of legislation is provided in the table below:

| Legislation | Legal Terminology | Training | Test |
|-------------|-------------------|----------|------|
| Organic Act on Counter Corruption, B.E. 2561 | organic law | ✓ |  |
| Civil and Commercial Code | code | ✓ | ✓ |
| Revenue Code | code | ✓ | ✓ |
| Accounting Act, B.E. 2543 | act | ✓ | ✓ |
| Accounting Profession Act, B.E. 2547 | act | ✓ | ✓ |
| Act on Disciplinary Offenses of Government Officials Performing Duties in Agencies Other than Government Agencies, B.E. 2534 | act | ✓ |  |
| Act on Offences of Officials Working in State Agencies or Organizations, B.E. 2502 | act | ✓ |  |
| Act on Offences Relating to Registered Partnerships, Limited Partnerships, Companies Limited, Associations and Foundations, B.E. 2499 | act | ✓ | ✓ |
| Act on the Establishment of Government Organizations, B.E. 2496 | act | ✓ |  |
| Act on the Management of Shares and Stocks of Ministers, B.E. 2543 | act | ✓ |  |
| Act Repealing the Agricultural Futures Trading Act, B.E. 2542 B.E. 2558 | act | ✓ |  |
| Budget Procedure Act, B.E. 2561 | act | ✓ |  |
| Business Registration Act, B.E. 2499 | act | ✓ | ✓ |
| Chamber of Commerce Act, B.E. 2509 | act | ✓ | ✓ |
| Derivatives Act, B.E. 2546 | act | ✓ | ✓ |
| Energy Conservation Promotion Act, B.E. 2535 | act | ✓ | ✓ |
| Energy Industry Act, B.E. 2550 | act | ✓ | ✓ |
| Financial Institutions Business Act, B.E. 2551 | act | ✓ | ✓ |
| Fiscal Discipline Act, B.E. 2561 | act | ✓ |  |
| Foreign Business Act, B.E. 2542 | act | ✓ | ✓ |
| Government Procurement and Supplies Management Act, B.E. 2560 | act | ✓ |  |
| National Economic and Social Development Act, B.E. 2561 | act | ✓ |  |
| Petroleum Income Tax Act, B.E. 2514 | act | ✓ | ✓ |
| Provident Fund Act, B.E. 2530 | act | ✓ | ✓ |
| Public Limited Companies Act, B.E. 2535 | act | ✓ | ✓ |
| Secured Transactions Act, B.E. 2558 | act | ✓ | ✓ |
| Securities and Exchange Act, B.E. 2535 | act | ✓ | ✓ |
| State Enterprise Capital Act, B.E. 2542 | act | ✓ |  |
| State Enterprise Committee and Personnel Qualifications Standards Act, B.E. 2518 | act | ✓ |  |
| State Enterprise Development and Governance Act, B.E. 2562 | act | ✓ |  |
| State Enterprise Labor Relations Act, B.E. 2543 | act | ✓ |  |
| Trade Association Act, B.E. 2509 | act | ✓ | ✓ |
| Trust for Transactions in Capital Market Act, B.E. 2550 | act | ✓ | ✓ |
| Emergency Decree on Digital Asset Businesses, B.E. 2561 | emergency decree | ✓ |  |
| Emergency Decree on Special Purpose Juristic Person for Securitization, B.E. 2540 | emergency decree | ✓ | ✓ |

The training split of `nitibench-ccl` can be found in the [WangchanX-Legal-ThaiCCL-RAG dataset](https://huggingface.co/datasets/airesearch/WangchanX-Legal-ThaiCCL-RAG).

### Data Format

Each data point contains four columns:
- `question: str` — A question relevant to the `relevant_laws`.
- `answer: str` — The original answer generated by an LLM, which has been revised and edited by legal experts to include both the reasoning steps and the final answer.
- `relevant_laws: List[Dict[str, str]]` — A list of relevant law name, section, and contents.
- `reference_answer: str` — The answer to the question based on the `relevant_laws`, provided without the reasoning steps.
- `reference_laws: List[Dict[str, str]]` - A list of referenced law mentioned in `relevant_laws` column.

Formally, given the data triple \((q, T=\{p_1, p_2, \dots, p_K\}, y)\), \(q\) represents the `question`, \(T\) represents `relevant_laws`, and \(y\) represents the `answer`.

### Data Curation

Using the notation described above, the data was curated as follows:
1. Queries (\(q\)) and answers (\(y\)) were manually crafted by legal experts based on a single section sampled from the legal texts of the 35 pieces of legislation.
2. For each data triple \((q, T, y)\), the manually crafted question was carefully quality-assured by a second legal expert.

Thus, for the test data, there is only one positive per query (\(|T|=1\)). The diagram below shows how the test data was collected.

![ccl-test](./assets/CCL_Test_Set.png)

## 💸 NitiBench-Tax

This subset provides a question, relevant laws, and an answer for each data point. Instead of having legal experts manually craft the questions, we scraped the data from a reliable source: the [Revenue Department Website](https://rd.go.th). This subset contains Tax Ruling Cases officially provided by the Revenue Department since 2021. As a result, this subset is considerably more challenging, as it requires extensive legal reasoning both for searching for relevant documents and for generating the answer. The data collection procedure is illustrated in the figure below:

![tax-test](./assets/tax_case_test_set.png)

### Data Format

This split uses the same format as described in the [NitiBench-CCL split](#data-format).

## Contact

For any inquiries or concerns, please reach out to us via email: [Chompakorn Chaksangchaichot](mailto:chompakornc_pro@vistec.ac.th).

## Citation
```bibtex
@inproceedings{akarajaradwong-etal-2025-nitibench,
    title = "{N}iti{B}ench: Benchmarking {LLM} Frameworks on {T}hai Legal Question Answering Capabilities",
    author = "Akarajaradwong, Pawitsapak  and
      Pothavorn, Pirat  and
      Chaksangchaichot, Chompakorn  and
      Tasawong, Panuthep  and
      Nopparatbundit, Thitiwat  and
      Pratai, Keerakiat  and
      Nutanong, Sarana",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1739/",
    doi = "10.18653/v1/2025.emnlp-main.1739",
    pages = "34292--34315",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) show promise in legal question answering (QA), yet Thai legal QA systems face challenges due to limited data and complex legal structures. We introduce NitiBench, a novel benchmark featuring two datasets: (1) NitiBench-CCL, covering Thai financial laws, and (2) NitiBench-Tax, containing Thailand{'}s official tax rulings. Our benchmark also consists of specialized evaluation metrics suited for Thai legal QA. We evaluate retrieval-augmented generation (RAG) and long-context LLM (LCLM) approaches across three key dimensions: (1) the benefits of domain-specific techniques like hierarchy-aware chunking and cross-referencing, (2) comparative performance of RAG components, e.g., retrievers and LLMs, and (3) the potential of long-context LLMs to replace traditional RAG systems. Our results reveal that domain-specific components slightly improve over naive methods. At the same time, existing retrieval models still struggle with complex legal queries, and long-context LLMs have limitations in consistent legal reasoning. Our study highlights current limitations in Thai legal NLP and lays a foundation for future research in this emerging domain."
}

@misc{akarajaradwong2025nitibenchcomprehensivestudiesllm,
      title={NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question Answering}, 
      author={Pawitsapak Akarajaradwong and Pirat Pothavorn and Chompakorn Chaksangchaichot and Panuthep Tasawong and Thitiwat Nopparatbundit and Sarana Nutanong},
      year={2025},
      eprint={2502.10868},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.10868}, 
}
```

## License
This dataset is provided under the **MIT License**.

## Acknowledgment
We sincerely appreciate the generous support from the WangchanX program sponsors—PTT, SCB, and SCBX—whose funding made this project possible. We are also grateful for the invaluable collaboration with VISTEC, which was crucial in bringing this project to fruition.

<br>
<div style="display: flex; align-items: center; gap: 20px;">
  Sponsored by
  <img src="./assets/VISAI_Logo_Horizontal.png" style="height:30px;" alt="VISAI Logo">
  <img src="./assets/Logo_vistec.png" style="height:30px;" alt="VISTEC Logo">
</div>