Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering

The effectiveness of open-domain question answering (ODQA), particularly those employing a retriever-reader architecture, depends on the ability to recall relevant documents -a critical step that enables the reader to accurately extract answers. To enhance this retrieval phase, current query expansion (QE) techniques leverage pre-trained language models (PLM) to mitigate word mismatches and improve the recall of relevant documents. Despite their advancements, these techniques often treat all expanded terms uniformly, which can lead to lessthan-optimal retrieval outcomes. In response, we propose a novel Recall-oriented Adaptive Learning (ReAL) method, which iteratively adjusts the importance weights of QE terms based on their relevance, thereby refining term distinction and enhancing the separation of relevant terms. Specifically, ReAL employs a similaritybased model to classify documents into pseudorelevant and pseudo-irrelevant sets, and then optimizes term weights via two tailored loss functions to maximize the scoring gap between them. Experiments on four ODQA datasets and five QE methods show that ReAL consistently enhances retrieval accuracy and overall end-toend QA performance, providing a robust and efficient solution for improving QE strategies in ODQA scenarios.