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

TL;DR

This paper introduces a new task and dataset for generating related insights to improve open-ended document-grounded QA, enabling richer user interaction and iterative refinement.

Contribution

It proposes InsightGen, a two-stage approach combining thematic clustering and neighborhood selection to generate diverse, relevant insights using large language models.

Findings

InsightGen produces useful, relevant, and actionable insights.

The dataset SCOpE-QA contains 3,000 questions across 20 research collections.

Evaluation shows the approach establishes a strong baseline for the new task.

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.