Papers › Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Kaixin Ma, Hao Cheng, Yu Zhang, Xiaodong Liu, Eric Nyberg, Jianfeng Gao
The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.
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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 | Chain-of-Skills | ANS-EM | 0.674 | #4 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Chain-of-Skills | ANS-F1 | 0.801 | #4 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Chain-of-Skills | JOINT-EM | 0.457 | #4 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Chain-of-Skills | JOINT-F1 | 0.717 | #4 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Chain-of-Skills | SUP-EM | 0.613 | #4 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Chain-of-Skills | SUP-F1 | 0.853 | #4 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.
Methods
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