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The methodology prioritises the use of in-context learning capabilities of LLMs to extract software-related entities and their descriptive attributes, such as distributive information. Our approach uses Retrieval-Augmented Generation\u00a0(RAG) techniques and LLMs for Named Entity Recognition\u00a0(NER) and Attributive NER to identify relationships between extracted software entities, providing a structured solution for analysing software citations in academic literature. The paper provides a detailed description of our approach, demonstrating how using LLMs in a single-choice QA paradigm can greatly enhance IE methodologies. Our participation in the SOMD shared task highlights the importance of precise software citation practices and showcases our system\u2019s ability to overcome the challenges of disambiguating and extracting relationships between software mentions. This sets the groundwork for future research and development in this field.<\/jats:p>","DOI":"10.1007\/978-3-031-65794-8_21","type":"book-chapter","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T06:02:44Z","timestamp":1723615364000},"page":"289-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Software-Related Information Extraction via\u00a0Single-Choice Question Answering with\u00a0Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9530-3631","authenticated-orcid":false,"given":"Wolfgang","family":"Otto","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7142-3887","authenticated-orcid":false,"given":"Sharmila","family":"Upadhyaya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4364-9243","authenticated-orcid":false,"given":"Stefan","family":"Dietze","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","unstructured":"Beltagy, I., Lo, K., Cohan, A.: Scibert: a pretrained language model for scientific text. 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