Is Relevance Propagated from Retriever to Generator in RAG?
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913701259378688 |
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| author | Tian, Fangzheng Ganguly, Debasis Macdonald, Craig |
| author_facet | Tian, Fangzheng Ganguly, Debasis Macdonald, Craig |
| contents | Retrieval Augmented Generation (RAG) is a framework for incorporating external knowledge, usually in the form of a set of documents retrieved from a collection, as a part of a prompt to a large language model (LLM) to potentially improve the performance of a downstream task, such as question answering. Different from a standard retrieval task's objective of maximising the relevance of a set of top-ranked documents, a RAG system's objective is rather to maximise their total utility, where the utility of a document indicates whether including it as a part of the additional contextual information in an LLM prompt improves a downstream task. Existing studies investigate the role of the relevance of a RAG context for knowledge-intensive language tasks (KILT), where relevance essentially takes the form of answer containment. In contrast, in our work, relevance corresponds to that of topical overlap between a query and a document for an information seeking task. Specifically, we make use of an IR test collection to empirically investigate whether a RAG context comprised of topically relevant documents leads to improved downstream performance. Our experiments lead to the following findings: (a) there is a small positive correlation between relevance and utility; (b) this correlation decreases with increasing context sizes (higher values of k in k-shot); and (c) a more effective retrieval model generally leads to better downstream RAG performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Is Relevance Propagated from Retriever to Generator in RAG? Tian, Fangzheng Ganguly, Debasis Macdonald, Craig Information Retrieval Retrieval Augmented Generation (RAG) is a framework for incorporating external knowledge, usually in the form of a set of documents retrieved from a collection, as a part of a prompt to a large language model (LLM) to potentially improve the performance of a downstream task, such as question answering. Different from a standard retrieval task's objective of maximising the relevance of a set of top-ranked documents, a RAG system's objective is rather to maximise their total utility, where the utility of a document indicates whether including it as a part of the additional contextual information in an LLM prompt improves a downstream task. Existing studies investigate the role of the relevance of a RAG context for knowledge-intensive language tasks (KILT), where relevance essentially takes the form of answer containment. In contrast, in our work, relevance corresponds to that of topical overlap between a query and a document for an information seeking task. Specifically, we make use of an IR test collection to empirically investigate whether a RAG context comprised of topically relevant documents leads to improved downstream performance. Our experiments lead to the following findings: (a) there is a small positive correlation between relevance and utility; (b) this correlation decreases with increasing context sizes (higher values of k in k-shot); and (c) a more effective retrieval model generally leads to better downstream RAG performance. |
| title | Is Relevance Propagated from Retriever to Generator in RAG? |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2502.15025 |