Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization
<|reference_start|>Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization: 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 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 encoder-decoder models with the direct preference optimization (DPO) method to improve over prior state of the art for the RadQA radiology question answering task by 12-15 F1 points. 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.<|reference_end|>
@article{nahian2024clinical,
    title={Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by
  Direct Preference Optimization},
    author={Md Sultan Al Nahian, Ramakanth Kavuluru},
    journal={arXiv preprint arXiv:2407.14000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14000},
    primaryClass={cs.IR cs.CL cs.LG}
}