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

Paper · 2026 · Saransh Sharma, Pritika Ramu, Aparna Garimella et al.

Summary

Open-ended questions over document collections, such as drafting a report or planning a strategy, rarely have one correct answer, and users usually revise a first draft several times. Existing QA benchmarks and follow-up-question systems assume the first answer is already acceptable. This paper defines a new task, document-grounded related insight generation. Given a question, an initial answer and a set of documents, a system must produce grounded statements that add something new: risks, trade-offs, missing aspects or alternative ideas that help improve the current answer.

The authors release SCOpE-QA, a benchmark built from scientific paper collections. They also propose INSIGHTGEN, a two-stage method. First, a K-Means variant clusters document chunks into a thematic graph. Second, neighboring clusters supply context that is related to the answer without repeating it, and an LLM turns this context into insights using chain-of-thought prompting.

INSIGHTGEN beats naïve prompting and several RAG pipelines, including agentic ones, under two independent LLM judges. The results suggest that modeling structure between documents matters more than pure similarity retrieval for this kind of exploratory support.

Key points

  • SCOpE-QA dataset: 3,000 open-ended questions over 20 research collections, totaling 405 papers with 10–35 per collection. Papers were gathered via ChatGPT, Perplexity Deep Research and Connected Papers. o3-mini was used only to compress papers and generate questions, typically 200 per collection and 250 for Quantization.
  • Models: GPT-4o and Claude-3.5 generate the insights. Gemini 2.5 Flash and Claude 4 Sonnet act as judges.
  • Baselines: INSIGHTGEN outperforms Iterative RAG, Multi-Query RAG and Agentic RAG, the latter combining HyDE, query expansion, decomposition and re-ranking. Its advantage grows as collections become larger.
  • Judge agreement: The two judges agree on 87% of pairwise orderings, with a median Spearman ρ of 0.85. All 32 Wilcoxon tests stay significant after Bonferroni correction.
  • Limitations: The method caps output at five insights per question and covers only English text.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Question answering · 487 works

Cite

Cite as Saransh Sharma, Pritika Ramu, Aparna Garimella and Koyel Mukherjee. 2026. An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.2005

@inproceedings{sharma2026answer,
  title = {{An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA}},
  author = {Saransh Sharma, Pritika Ramu, Aparna Garimella and Koyel Mukherjee},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2005},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2005}
}

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