If a RAG system’s retriever achieves high recall@5 (meaning at least one relevant document is in the top 5 retrieved results) but end-to-end question answering (QA) accuracy remains low, it indicates a disconnect between retrieval quality and the generator’s ability to synthesize answers. High recall@5 suggests the retriever is successfully finding relevant context in most cases, but the generator either fails to extract correct answers from the retrieved documents or struggles to prioritize the most useful information. This mismatch often stems from issues in how the generator processes the retrieved data, limitations in the retriever’s precision, or misalignment between the retrieved content and the QA task’s requirements.
One key factor could be the generator’s inability to handle noisy or redundant information in the top 5 results. For example, even if one relevant document exists, the generator might focus on incorrect or irrelevant parts of other retrieved documents. Imagine a medical QA task where the retriever fetches five research papers, one of which contains the correct answer. If the generator lacks the ability to identify and prioritize that single relevant document—or misinterprets conflicting data in others—it may produce an incorrect answer. Additionally, the retriever might prioritize broad recall (ensuring at least one good result) over precision, leading to low-quality or off-topic documents in the top 5 that confuse the generator. For instance, a question about “Python list sorting” might retrieve five programming articles, but only one explains the specific method in question, while others discuss unrelated topics like data structures or algorithms.
To address this, focus on improving the generator’s robustness to noisy inputs or refining the retriever-generator handoff. For the generator, fine-tuning it to weigh retrieved passages differently (e.g., using attention mechanisms) or adding post-processing steps like answer verification could help. For the retriever, balancing recall and precision by reranking results (e.g., with a cross-encoder) or adjusting the retrieval scope (e.g., narrowing the search to specific document sections) might ensure higher-quality inputs for the generator. Testing the generator with manually curated “perfect” retrieval results can isolate whether the issue lies in retrieval or generation. If the generator performs well in this scenario, the problem is likely in the retriever’s precision or ranking; if not, the generator itself needs improvement.