Papers › Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Ikuya Yamada, Akari Asai, Hannaneh Hajishirzi
Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. However, such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) to represent the passage index using compact binary codes rather than continuous vectors. BPR is trained with a multi-task objective over two tasks: efficient candidate generation based on binary codes and accurate reranking based on continuous vectors. Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA. Our code and trained models are available at https://github.com/studio-ousia/bpr.
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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 |
|---|---|---|---|---|---|---|---|
| Open-Domain Question Answering | Natural Questions | BPR (linear scan; l=1000) | Exact Match | 41.6 | #5 of 5 | Archive leaderboard | report |
| Open-Domain Question Answering | TQA | BPR (linear scan; l=1000) | Exact Match | 56.8 | #2 of 2 | Archive leaderboard | report |
| Question Answering | Natural Questions (long) | BPR (linear scan; l=1000) | EM | 41.6 | #13 of 13 | 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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