Less LLM, More Documents: Searching for Improved RAG
Retrieval-Augmented Generation (RAG) couples document retrieval with large
language models (LLMs). While scaling generators improves accuracy, it also
raises cost and limits deployability. We explore an orthogonal axis: enlarging
the retriever's corpus to reduce reliance on large LLMs. Experimental results
show that corpus scaling consistently strengthens RAG and can often serve as a
substitute for increasing model size, though with diminishing returns at larger
scales. Small- and mid-sized generators paired with larger corpora often rival
much larger models with smaller corpora; mid-sized models tend to gain the
most, while tiny and large models benefit less. Our analysis shows that
improvements arise primarily from increased coverage of answer-bearing
passages, while utilization efficiency remains largely unchanged. These
findings establish a principled corpus-generator trade-off: investing in larger
corpora offers an effective path to stronger RAG, often comparable to enlarging
the LLM itself.
68e41338e4e093a7044e4c2b
Jingjie Ning
claimed_verified
2025-10-07T12:27:41.555Z
6310a812a23f0327bce68778
https://cdn-avatars.huggingface.co/v1/production/uploads/6310a812a23f0327bce68778/dp9JherZV-RYiSRKnvxsh.jpeg
Ethan Ning
user
ethanning
68e41338e4e093a7044e4c2c
Yibo Kong
68e41338e4e093a7044e4c2d
Yunfan Long
68e41338e4e093a7044e4c2e
Jamie Callan
Less LLM, More Documents: Searching for Improved RAG
Retrieval-Augmented Generation (RAG) couples document retrieval with large
language models (LLMs). While scaling generators improves accuracy, it also
raises cost and limits deployability. We explore an orthogonal axis: enlarging
the retriever's corpus to reduce reliance on large LLMs. Experimental results
show that corpus scaling consistently strengthens RAG and can often serve as a
substitute for increasing model size, though with diminishing returns at larger
scales. Small- and mid-sized generators paired with larger corpora often rival
much larger models with smaller corpora; mid-sized models tend to gain the
most, while tiny and large models benefit less. Our analysis shows that
improvements arise primarily from increased coverage of answer-bearing
passages, while utilization efficiency remains largely unchanged. These
findings establish a principled corpus-generator trade-off: investing in larger
corpora offers an effective path to stronger RAG, often comparable to enlarging
the LLM itself.
Expanding the retriever's corpus in Retrieval-Augmented Generation (RAG) can improve performance and reduce reliance on large language models.
Retrieval-Augmented Generation RAG document retrieval large language models LLMs corpus scaling answer-bearing passages