  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.
