Work

RadQA-DPO: A Radiology Question Answering System with Encoder-Decoder Models Enhanced by Direct Preference Optimization

Year: 2025

Type: conference-paper

Abstract: Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals.While encoder models (e.g., BERT) have been popular for this reading comprehension-style question answering task, recently encoder-decoder models (e.g., T5) are on the rise.There is also the emergence of preference optimization techniques to align decoder-only LLMs with human preferences.In this paper, we combine encoderdecoder models with the direct preference optimization (DPO) method for the RadQA radiology question answering task.Our approach achieves a 12-15 F1 point improvement over previous state-of-the-art models.To the best of our knowledge, this effort is the first to show that DPO method also works for reading comprehension via novel heuristics to generate preference data without human inputs.

Source: Workshop on Biomedical Language Processing (BioNLP)

DOI: 10.18653/v1/2025.bionlp-1.10

Authors: Md Sultan Al Nahian, Ramakanth Kavuluru

Institution: University of Kentucky

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Institutes of Health, U.S. National Library of Medicine

Awards: G2851928988, G9000021486