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    <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.</abstract>
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%0 Conference Proceedings
%T NitiBench: Benchmarking LLM Frameworks on Thai Legal Question Answering Capabilities
%A Akarajaradwong, Pawitsapak
%A Pothavorn, Pirat
%A Chaksangchaichot, Chompakorn
%A Tasawong, Panuthep
%A Nopparatbundit, Thitiwat
%A Pratai, Keerakiat
%A Nutanong, Sarana
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F akarajaradwong-etal-2025-nitibench
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1739/
%P 34292-34315

Markdown (Informal)

[NitiBench: Benchmarking LLM Frameworks on Thai Legal Question Answering Capabilities](https://aclanthology.org/2025.emnlp-main.1739/) (Akarajaradwong et al., EMNLP 2025)

• NitiBench: Benchmarking LLM Frameworks on Thai Legal Question Answering Capabilities (Akarajaradwong et al., EMNLP 2025)

ACL

• Pawitsapak Akarajaradwong, Pirat Pothavorn, Chompakorn Chaksangchaichot, Panuthep Tasawong, Thitiwat Nopparatbundit, Keerakiat Pratai, and Sarana Nutanong. 2025. NitiBench: Benchmarking LLM Frameworks on Thai Legal Question Answering Capabilities. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 34292–34315, Suzhou, China. Association for Computational Linguistics.