Can a model's partial response guide what to retrieve next?

Standard RAG asks: "what documents are relevant to this query?" before any generation has occurred. The query is the only signal available. For complex tasks, the query is often an inadequate signal — it expresses what was asked but not what is needed to answer it fully.

ITER-RETGEN (Iterative Retrieval-Generation Synergy) demonstrates an alternative: use the model's current response to the task as the retrieval query. The model's response "shows what might be needed to finish the task" — it contains implicit signals about the gaps between what has been answered and what remains unaddressed.

The synergy is iterative: generate a response → use response as retrieval query → retrieve more relevant documents → regenerate with new context → repeat. Each generation round surfaces new implicit information needs that the original query did not express. Performance on multi-hop question answering, fact verification, and commonsense reasoning improves substantially over single-pass RAG.

This reframes what generation is for in RAG pipelines. Generation is not only the terminal output step — it is also a diagnostic step that identifies what retrieval should target next. The generator functions as both an answer producer and an information-need clarifier.

The connection to human information seeking: humans working on complex research do not submit all their queries upfront. They read, understand what they know and don't know, then query for the specific gaps that reading revealed. ITER-RETGEN operationalizes this workflow.

Source: RAG