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nitibench

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Sentence Similarity
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RAG
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metadata
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] | [👨‍💻 Github Repository]

This dataset provides the test data for evaluating LLM frameworks, such as RAG or LCLM. The benchmark consists of two datasets:

🏛️ NitiBench-CCL

Derived from the WangchanX-Legal-ThaiCCL-RAG Dataset, 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 Corporate and Commercial Law (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.

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

💸 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. 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

Data Format

This split uses the same format as described in the NitiBench-CCL split.

Contact

For any inquiries or concerns, please reach out to us via email: Chompakorn Chaksangchaichot.

Citation 

@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.


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