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Learning to Filter Context for Retrieval-Augmented Generation

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arxiv 2311.08377 v1 pith:6744ISWZ submitted 2023-11-14 cs.CL cs.AI

Zhiruo Wang , Jun Araki , Zhengbao Jiang , Md Rizwan Parvez , Graham Neubig This is my paper · ORCID
classification cs.CLcs.AI
keywords contextgenerationtasksansweringapproachesfactfilcofilter
verification ladder T0 review T1 audit T2 compute T3 formal
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On-the-fly retrieval of relevant knowledge has proven an essential element of reliable systems for tasks such as open-domain question answering and fact verification. However, because retrieval systems are not perfect, generation models are required to generate outputs given partially or entirely irrelevant passages. This can cause over- or under-reliance on context, and result in problems in the generated output such as hallucinations. To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time. We experiment on six knowledge-intensive tasks with FLAN-T5 and LLaMa2, and demonstrate that our method outperforms existing approaches on extractive question answering (QA), complex multi-hop and long-form QA, fact verification, and dialog generation tasks. FILCO effectively improves the quality of context, whether or not it supports the canonical output.

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Cited by 13 Pith papers

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