Refined Medical Search via Dense Retrieval and User Interaction

Abstract

Users formulate search queries that reflect an information need. Those queries are then submitted to a search service in the expectation that the retrieved results will allow the user to complete an external task, and align with their broader information context. In this study, we reimplement and reproduce the log-augmented dense retrieval approach introduced by Jin, Shin, and Lu in 2023. As part of our study we extend the experimentation by: (1) using all of the available training data rather than a subset; (2) exploring a second dense retrieval model; and (3) enhancing the approach by incorporating user reformulation behavior into the dense retrieval computation so as to improve ranking ef..