Multi-hop retrieval improves grounding quality by breaking complex queries into smaller steps, each requiring retrieval of intermediate facts. Instead of relying on a single search to answer a question, the system iteratively gathers evidence from multiple sources. This stepwise approach forces the model to explicitly link its reasoning to concrete data points, reducing reliance on assumptions or internal knowledge. For example, answering "What caused the 2023 stock market surge?" might require first retrieving articles about interest rate changes, then finding data linking those changes to sector performance. Each hop acts as a verifiable checkpoint, constraining the model to build conclusions from retrieved facts rather than inventing connections.
A practical example is answering medical questions like "Can drug X treat condition Y if the patient has allergy Z?" A single-step retrieval might miss critical interactions between these three elements. With multi-hop, the system could first retrieve drug X’s mechanism, then check condition Y’s treatment guidelines, and finally cross-reference allergy Z’s contraindications. This ensures the final answer synthesizes all three facts explicitly, avoiding dangerous oversights. Similarly, in software troubleshooting, resolving "Why does feature A fail on browser B?" might require retrieving compatibility tables, version-specific bug reports, and configuration best practices separately, ensuring each dependency is validated.
Single-step retrieval often fails for nuanced questions because relevant information is scattered across documents. For instance, answering "How did Event P influence Policy Q in Country R?" might require combining historical timelines, policy drafts, and expert analyses from different sources. Multi-hop retrieves these pieces sequentially, allowing the model to validate intermediate claims (e.g., confirming Event P’s timeline before linking it to policy changes). This reduces "fabrication domino effects" where one incorrect assumption corrupts the entire answer. By anchoring each logical step to retrieved evidence, multi-hop creates an audit trail that makes errors easier to spot and correct during development.