Related Insight Generation Goes Beyond Factual QA to Synthesize Open-Ended Document-Grounded Answers

arXiv·medium signal

Sharma, Ramu, and Garimella address a limitation of current QA systems: they return a single answer when open-ended questions require synthesis, judgment, and exploration. Their system generates related insights alongside answers, modeling the iterative refinement process human researchers use. Directly applicable to RAG systems and document QA products where users need exploratory answers, not just factual retrieval.