Enhancing Software Related Information Extraction with Generative
  Language Models through Single-Choice Question Answering
Wolfgang Otto Sharmila Upadhyaya Stefan Dietze
This paper describes our participation in the Shared Task on Software
Mentions Disambiguation (SOMD), with a focus on improving relation extraction
in scholarly texts through Generative Language Models (GLMs) using
single-choice question-answering. The methodology prioritises the use of
in-context learning capabilities of GLMs to extract software-related entities
and their descriptive attributes, such as distributive information. Our
approach uses Retrieval-Augmented Generation (RAG) techniques and GLMs for
Named Entity Recognition (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 GLMs 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's 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.