Exploring Approaches to Answer Biomedical Questions: From Pre-processing to GPT-4 Notebook for the BioASQ Lab at CLEF 2023

Hyunjae Kim, Hyeon Hwang, Chaeeun Lee, Minju Seo, Wonjin Yoon, Jaewoo Kang

Research output: Contribution to journalConference articlepeer-review

Abstract

Biomedical question answering (QA) plays a crucial role in assisting researchers, healthcare professionals, and even patients in accessing and retrieving accurate and up-to-date information from the vast amount of biomedical knowledge available in literature. To enhance the efficiency of knowledge discovery and information retrieval, we investigate the efficacy of various pre-processing, model training, data augmentation, and ensemble methods and evaluate a range of advanced pre-trained models such as BioLinkBERT and GPT-4. Additionally, we explore data augmentation and ensemble methods to further improve system performance. In our participation in BioASQ Task 11b-Phase B, our systems achieved a top ranking in all four batches for the yes/no type of questions, in one out of four batches for factoid questions, and in two out of four batches for list-type questions.

Original languageEnglish
Pages (from-to)132-144
Number of pages13
JournalCEUR Workshop Proceedings
Volume3497
Publication statusPublished - 2023
Event24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023 - Thessaloniki, Greece
Duration: 2023 Sept 182023 Sept 21

Bibliographical note

Publisher Copyright:
© 2023 Copyright for this paper by its authors.

Keywords

  • BioASQ 11b
  • BioLinkBERT
  • Data Augmentation
  • Ensemble
  • GPT-4

ASJC Scopus subject areas

  • General Computer Science

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