An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA

2026-04-21

AI Summary

Existing open-domain question answering systems struggle to support users in iteratively refining and deeply exploring initial answers, primarily due to the absence of mechanisms that generate relevant insights to enrich the interactive experience. This work introduces, for the first time, a document-level insight generation task tailored to open-ended questions, accompanied by the SCOpE-QA dataset. The authors propose InsightGen, a two-stage framework that first constructs a document topic graph via clustering and then selects contextual neighborhoods from this graph to prompt large language models to produce diverse, relevant, and actionable supplementary insights. Experimental results across 3,000 questions demonstrate that the approach effectively extends or reconstructs initial answers, establishing a strong baseline for this novel task.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Answering open-ended questions remains challenging for AI systems because it requires synthesis, judgment, and exploration beyond factual retrieval, and users often refine answers through multiple iterations rather than accepting a single response. Existing QA benchmarks do not explicitly support this refinement process. To address this gap, we introduce a new task, document-grounded related insight generation, where the goal is to generate additional insights from a document collection that help improve, extend, or rethink an initial answer to an open-ended question, ultimately supporting richer user interaction and a better overall question answering experience. We curate and release SCOpE-QA (Scientific Collections for Open-Ended QA), a dataset of 3,000 open-ended questions across 20 research collections. We present InsightGen, a two-stage approach that first constructs a thematic representation of the document collection using clustering, and then selects related context based on neighborhood selection from the thematic graph to generate diverse and relevant insights using LLMs. Extensive evaluation on 3,000 questions using two generation models and two evaluation settings shows that InsightGen consistently produces useful, relevant, and actionable insights, establishing a strong baseline for this new task.

Problem

• open-ended QA
• answer refinement
• related insight generation
• document-grounded QA
• user interaction

Innovation

• related insight generation
• document-grounded QA
• thematic clustering
• neighborhood selection
• open-ended question answering