More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG
This addresses a specific bottleneck in RAG systems for AI researchers and practitioners, showing that multi-document processing is a distinct challenge from long-context handling.
The study isolated how the number of documents affects Retrieval-Augmented Generation (RAG) performance while controlling for context length, finding that increasing document count reduces performance by up to 20% for most LLMs, though Qwen2.5 maintained consistent results.
Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Model (LLM) responses by leveraging relevant external documents during generation. Although previous studies noted that retrieving many documents can degrade performance, they did not isolate how the quantity of documents affects performance while controlling for context length. We evaluate various language models on custom datasets derived from a multi-hop QA task. We keep the context length and position of relevant information constant while varying the number of documents, and find that increasing the document count in RAG settings poses significant challenges for most LLMs, reducing performance by up to 20%. However, Qwen2.5 maintained consistent results across increasing document counts, indicating better multi-document handling capability. Finally, our results indicate that processing multiple documents is a separate challenge from handling long contexts. We also make the datasets and code available: https://github.com/shaharl6000/MoreDocsSameLen .