| Challenge: | Existing bridge modules attempt to rewrite documents for better generation, but fail to capture "document utility". |
| Approach: | They propose to observe reasoning process as process supervision and scale this observation to enhance reliability in distillation. |
| Outcome: | The proposed method improves over strong bridging baselines on multiple open-domain question-answering benchmarks. |
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| Challenge: | Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility. |
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Hengran Zhang, Minghao Tang, Keping Bi, Jiafeng Guo, Shihao Liu, Daiting Shi, Dawei Yin, Xueqi Cheng
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