Joint Inference of Retrieval and Generation for Passage Re-ranking (2024.findings-eacl)
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| Challenge: | Existing methods for re-ranking documents are sparse and do not require training. |
| Approach: | They propose a method that optimizes mutual information between query and passage distributions by integrating cross-encoders and generative models in the re-ranking process. |
| Outcome: | The proposed method outperforms conventional re-rankers and language model scorers in open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings. |
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