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- W4384773911 abstract "Many recent QA models retrieve answers from passages, rather than whole documents, due to the limitations of deep learning models with limited context size. However, this approach ignores important document-level cues that can be crucial in answering questions. This paper reviews three open-domain QA benchmarks from a document-level perspective and finds that they are biased towards passage-level information. Out of 17,000 assessed questions, 82 were identified as requiring document-level reasoning and could not be answered by passage-based models. Document-level retrieval (BM25) outperformed both dense and sparse passage-level retrieval on these questions, highlighting the need for more evaluation of models' ability to understand documents, an often-overlooked challenge in open-domain QA." @default.
- W4384773911 created "2023-07-20" @default.
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- W4384773911 date "2023-07-18" @default.
- W4384773911 modified "2023-09-26" @default.
- W4384773911 title "Limitations of Open-Domain Question Answering Benchmarks for Document-level Reasoning" @default.
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- W4384773911 doi "https://doi.org/10.1145/3539618.3592011" @default.
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