README.md

metadata

license: cc
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/nitibench-ccl.parquet
      - split: tax
        path: data/nitibench-tax.parquet

👩🏻‍⚖️ NitiBench: A Thai Legal Benchmark for RAG

[📄 Technical Report]

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

• NitiBench-CCL
• NitiBench-Tax

🏛️ 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 answer to the question based on the relevant_laws, provided without the reasoning steps.
• relevant_laws: List[Dict[str, str]] — A list of relevant laws that should be used as context when answering the question.
• reference_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.

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.

💸 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:

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.

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