Journal of Construction Automation and Robotics ISSN:2800-0552(Print) 2951-116X(Online) 건설자동화·로보틱스 논문집

Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. (2024). Seven failure points when engineering a retrieval augmented generation system. Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI. https://doi.org/10.1145/3644815.3644945

Cho, S.-K., and Kim, S.-S. (2024). A study on the efficient response to architectural civil complaints using large language models(LLM) [A Study on the Efficient Response to Architectural Civil Complaints Using Large Language Models(LLM)]. Journal of the Architectural Institute of Korea, 40(9), pp. 81-90.

Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., Metropolitansky, D., Ness, R. O., and Larson, J. (2024). From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130.

Jin, C., Zhang, Z., Jiang, X., Liu, F., Liu, S., Liu, X., and Jin, X. (2025). Ragcache: Efficient knowledge caching for retrieval-augmented generation. ACM Transactions on Computer Systems, 44(1), pp. 1-27. https://doi.org/10.1145/3768628

Lee, J., and Lee, G. (2025). Long context window-based zero-shot legal interpretation of building codes and regulations. Automation in Construction, 179, 106450. https://doi.org/10.1016/j.autcon.2025.106450

Levy, S., Mazor, N., Shalmon, L., Hassid, M., and Stanovsky, G. (2025). More documents, same length: Isolating the challenge of multiple documents in RAG. arXiv preprint arXiv:2503.04388. https://doi.org/10.18653/v1/2025.findings-emnlp.1064

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

Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P. (2024). Lost in the middle: how language models use long contexts. Transactions of the Association for Computational Linguistics, 12, pp. 157-173. https://doi.org/10.1162/tacl_a_00638

Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E. H., Schärli, N., and Zhou, D. (2023). Large language models can be easily distracted by irrelevant context. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, pp. 31210-31227.

Shin, E., So, H., Yoon, Y., Jung, C., Kim, D., and Jung, H. (2025). Modular RAG-based Legal Question Answering System Reflecting the Hierarchical Structure of Civil Law. Journal of Korean Institute of Information Technology, 23(11), pp. 27-38. https://doi.org/10.14801/jkiit.2025.23.11.27

Tang, Y., and Yang, Y. (2024). Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries. arXiv preprint arXiv:2401.15391.

Xi, Y., Bai, Y., Luo, H., Wen, W., Liu, H., and Li, H. (2025). Hybrid retrieval-augmented generation agent for trustworthy legal question answering in judicial forensics. arXiv preprint arXiv:2511.01668.

Yoran, O., Wolfson, T., Ram, O., and Berant, J. (2024). Making retrieval-augmented language models robust to irrelevant context. International Conference on Learning Representations (ICLR 2024).