A survey on cutting-edge relation extraction techniques based on language models Díaz García, José Ángel Díaz López, Julio Amador Relation extraction NLP modelos de lenguaje The research reported in this paper was supported by the DesinfoScan project: Grant TED2021-129402B-C21 funded by MCIN /AEI/ 10.13039/ 501100011033 and, by the European Union NextGenerationEU/PRTR, and FederaMed project: Grant PID2021-123960OB-I00 funded by MCIN/ AEI/ 10.13039/501100011033 and by ERDF A way of making Europe. Finally, the research reported in this paper is also funded by the European Union (BAG-INTEL project, grant agreement no. 101121309). Funding for open access publishing: Universidad de Granada/CBUA. This comprehensive survey examines the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences from 2020 to 2023, focusing on models that leverage language models. Our findings underscore the dominance of BERT-based methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging Large Language Models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations. 2025-10-29T11:05:18Z 2025-10-29T11:05:18Z 2025-07-01 journal article Diaz-Garcia, J. A., & Lopez, J. A. D. (2025). A survey on cutting-edge relation extraction techniques based on language models. Artificial Intelligence Review, 58(9), 287. https://doi.org/10.1007/s10462-025-11280-0 https://hdl.handle.net/10481/107566 10.1007/s10462-025-11280-0 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Attribution-NonCommercial-NoDerivatives 4.0 Internacional Springer Nature