ACL-Verbatim: hallucination-free question answering for research
Academic researchers need efficient and reliable methods for collecting high-quality information from trusted sources, but modern tools for AI-assisted research still suffer from the tendency of Large Language Models (LLMs) to produce factually inaccurate or nonsensical output, commonly referred to as hallucinations. We apply the extractive question answering system VerbatimRAG to research papers in the ACL Anthology, directly mapping user queries to verbatim text spans in retrieved documents. We contribute a novel ground truth dataset for the task of mapping user queries to relevant text spans in research papers, and use it to train and evaluate a variety of extractive models. Human anno...
| Authors | Gábor Recski, Szilveszter Tóth, Nadia Verdha et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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- KRLabsOrg/verbatim-rag-modern-bert-v2Model · cited by
- KRLabsOrg/acl-anthology-mdDataset · cited by
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