Understanding Retrieval Augmentation for Long-Form Question Answering
Published in Conference On Language Modeling 2024, 2024
This paper provides a comprehensive analysis of retrieval augmentation for long-form question answering. We examine how retrieval augmentation affects model performance across different types of questions and identify key factors that determine its effectiveness.
Recommended citation: Hung-Ting Chen, Fangyuan Xu*, Shane A. Arora*, Eunsol Choi. (2024). "Understanding Retrieval Augmentation for Long-Form Question Answering." Conference On Language Modeling 2024.
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