Answerability in Retrieval-Augmented Open-Domain Question Answering

arXiv:2403.01461v1 Announce Type: new
Abstract: The performance of Open-Domain Question Answering (ODQA) retrieval systems can exhibit sub-optimal behavior, providing text excerpts with varying degrees of irrelevance. Unfortunately, many existing ODQA datasets lack examples specifically targeting the identification of irrelevant text excerpts. Previous attempts to address this gap have relied on a simplistic approach of pairing questions with random text excerpts. This paper aims to investigate the effectiveness of models trained using this randomized strategy, uncovering an important limitation in their …

abstract arxiv behavior cs.cl datasets domain examples gap identification performance question question answering retrieval retrieval-augmented systems targeting text type