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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.11116 |
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| _version_ | 1866910993972461568 |
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| author | Huang, Siyuan Ma, Zhiyuan Du, Jintao Meng, Changhua Wang, Weiqiang Leng, Jingwen Guo, Minyi Lin, Zhouhan |
| author_facet | Huang, Siyuan Ma, Zhiyuan Du, Jintao Meng, Changhua Wang, Weiqiang Leng, Jingwen Guo, Minyi Lin, Zhouhan |
| contents | RAG systems rely on rerankers to identify relevant documents. However, fine-tuning these models remains challenging due to the scarcity of annotated query-document pairs. Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. To overcome these limitations, we reframe the reranking process as an attention-mask problem and propose Gumbel Reranking, an end-to-end training framework for rerankers aimed at minimizing the training-inference gap. In our approach, reranker optimization is reformulated as learning a stochastic, document-wise Top-$k$ attention mask using the Gumbel Trick and Relaxed Top-$k$ Sampling. This formulation enables end-to-end optimization by minimizing the overall language loss. Experiments across various settings consistently demonstrate performance gains, including a 10.4\% improvement in recall on HotpotQA for distinguishing indirectly relevant documents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gumbel Reranking: Differentiable End-to-End Reranker Optimization Huang, Siyuan Ma, Zhiyuan Du, Jintao Meng, Changhua Wang, Weiqiang Leng, Jingwen Guo, Minyi Lin, Zhouhan Computation and Language Information Retrieval RAG systems rely on rerankers to identify relevant documents. However, fine-tuning these models remains challenging due to the scarcity of annotated query-document pairs. Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. To overcome these limitations, we reframe the reranking process as an attention-mask problem and propose Gumbel Reranking, an end-to-end training framework for rerankers aimed at minimizing the training-inference gap. In our approach, reranker optimization is reformulated as learning a stochastic, document-wise Top-$k$ attention mask using the Gumbel Trick and Relaxed Top-$k$ Sampling. This formulation enables end-to-end optimization by minimizing the overall language loss. Experiments across various settings consistently demonstrate performance gains, including a 10.4\% improvement in recall on HotpotQA for distinguishing indirectly relevant documents. |
| title | Gumbel Reranking: Differentiable End-to-End Reranker Optimization |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2502.11116 |