Asai, A., Wu, Z., Wang, Y., Sil, A., Hajishirzi, H. (2024). Self-RAG: Learning to retrieve, generate, and critique through self-reflection, Proceedings of the International Conference on Learning Representations (ICLR).

Blecher, L., Cucurull, G., Scialom, T., Stojnic, R. (2024). Nougat: Neural optical understanding for academic documents, Proceedings of the International Conference on Learning Representations (ICLR).

Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., Liu, Z. (2024). M3-Embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, Findings of the Association for Computational Linguistics: ACL 2024, 2318-2335.

Cormack, G. V., Clarke, C. L. A., Büttcher, S. (2009). Reciprocal rank fusion outperforms Condorcet and individual rank learning methods, Proceedings of the 32nd International ACM SIGIR Conference, 758-759.

Es, S., James, J., Espinosa-Anke, L., Schockaert, S. (2024). RAGAS: Automated evaluation of retrieval augmented generation, Proceedings of EACL System Demonstrations, 150-158.

Han, E. S., Kim, H. S., Kim, K. J., Lee, K. J. (2024). A study on the classification system of review items for digitalization of Korea construction standards, KSCE 2024 Convention, 326-327 (in Korean).

Han, E. S., Kim, K. J., Lee, K. J., Kim, H. S. (2023). A study on the classification system of construction standards library for digitalization of Korea construction standards, KSCE 2023 Convention, Session J3-002, 305-306 (in Korean).

Kim, G. (2025). Hangul document file format: Exploring the structure of HWPX, Hancom Tech Available at: https://tech.hancom.com/hwpxformat/ (Accessed: August 5, 2026) (in Korean).

Lee, S. H., Choi, B. H. (2022). Digitalization of Korean construction standards: Current status, KSCE 2022 Convention, 76-77 (in Korean).

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W. T., Rocktäschel, T., Riedel, S., Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks, Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.

Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., Ho, D. E. (2025). Hallucination-free? Assessing the reliability of leading AI legal research tools, Journal of Empirical Legal Studies, 22(2), 216-242.

Ministry of Land, Infrastructure and Transport MOLIT (2025). The era of digital construction standards recognizable by artificial intelligence (AI) is approaching, Ministry of Land, Infrastructure and Transport Available at: https://www.molit.go.kr/USR/NEWS/dtl.jsp?id=95091497&lcmspage=1 (Accessed: August 5, 2026) (in Korean).

Robertson, S., Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond, Foundations and Trends in Information Retrieval, 3(4), 333-389.

Smock, B., Pesala, R., Abraham, R. (2022). PubTables-1M: Towards comprehensive table extraction from unstructured documents, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4634-4642.

Strich, J., Isgorur, E. K., Trescher, M., Biemann, C., Semmann, M. (2026). T2-RAGBench: Text-and-table benchmark for evaluating retrieval-augmented generation, Proceedings of the 2026 Conference of the European Chapter of the Association for Computational Linguistics (EACL), Long Papers, 165-191.

Yim, J. H. (2020). Selection strategy for the file format of official documents in a cloud environment, The Korean Journal of Archival Studies(66), 5-35 (in Korean).