TY - JOUR
T1 - NCU-IISR
T2 - 25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024
AU - Chih, Bing Chen
AU - Han, Jen Chieh
AU - Tsai, Richard Tzong Han
N1 - Publisher Copyright:
© 2024 Copyright for this paper by its authors.
PY - 2024
Y1 - 2024
N2 - In this paper, we introduce our system and submissions in BioASQ 12b phase b [1], highlighting a significant improvement with GPT-4 and the integration of Retrieval Augmented Generation (RAG) techniques. We describe our prompt engineering methods and the experimental procedures followed. Because GPT-4 has proven effectiveness in generating answers and its ability in the biological domain, our system utilizes GPT-4 to address biomedical question-answering (QA). Leveraging OpenAI's ChatCompletions API, we refined previous prompt engineering approaches [2] for BioASQ 11b phase b. This year, the addition of RAG techniques significantly improved the information retrieval capabilities of our system. Consequently, our latest submission employed what we experimented to be the most effective prompts and techniques, achieving excellent performance across multiple metrics in the fourth batch.
AB - In this paper, we introduce our system and submissions in BioASQ 12b phase b [1], highlighting a significant improvement with GPT-4 and the integration of Retrieval Augmented Generation (RAG) techniques. We describe our prompt engineering methods and the experimental procedures followed. Because GPT-4 has proven effectiveness in generating answers and its ability in the biological domain, our system utilizes GPT-4 to address biomedical question-answering (QA). Leveraging OpenAI's ChatCompletions API, we refined previous prompt engineering approaches [2] for BioASQ 11b phase b. This year, the addition of RAG techniques significantly improved the information retrieval capabilities of our system. Consequently, our latest submission employed what we experimented to be the most effective prompts and techniques, achieving excellent performance across multiple metrics in the fourth batch.
KW - Biomedical Question Answer
KW - Generative Pre-trained Transformer
KW - Large Language Models (LLMs)
KW - Retrieval Augmented Generation
UR - http://www.scopus.com/inward/record.url?scp=85201608224&partnerID=8YFLogxK
M3 - 會議論文
AN - SCOPUS:85201608224
SN - 1613-0073
VL - 3740
SP - 99
EP - 105
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 9 September 2024 through 12 September 2024
ER -