Papers › Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | HotpotQA | Recursive Dense Retriever | ANS-EM | 0.623 | #14 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Recursive Dense Retriever | ANS-F1 | 0.753 | #14 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Recursive Dense Retriever | JOINT-EM | 0.418 | #14 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Recursive Dense Retriever | JOINT-F1 | 0.666 | #14 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Recursive Dense Retriever | SUP-EM | 0.575 | #14 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Recursive Dense Retriever | SUP-F1 | 0.809 | #14 of 72 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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