Evidence-Grounded QA

QA: Question Answering

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39 papers7 papers in the last four weeks, down 22% on the four weeks before. 0.0% of all new papers.

ACL-Verbatim: hallucination-free question answering for research
May 20, 2026Gábor Recski, Szilveszter Tóth, Nadia Verdha +2Hallucination in Language Models Retrieval-Augmented Generation
TU Wien · KR Labs
CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
May 13, 2026Dongsheng Ma, Jiayu Li, Zhengren Wang +9VLM Evaluation Citation Verification
Peking University · Shanghai Artificial Intelligence Laboratory
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
May 7, 2026Susheel Suresh, Hazel Mak, Shangpo Chou +2Tool-Augmented Language Model Agents Agentic Search
Microsoft Corporation
EigentSearch-Q+: Enhancing Deep Research Agents with Structured Reasoning Tools
Apr 9, 2026Boer Zhang, Mingyan Wu, Dongzhuoran Zhou +6Deep Research Agents Agentic Search
Meta · Northeastern University, China · University of Oslo +5
PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs
Mar 21, 2026Tianyi Huang, Caden Yang, Emily Yin +2Evidence-Grounded QA LLM Answer Verification
Ryquo · App-In Club
CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
Mar 17, 2026Tianyi Huang, Ying Kai DengFactual QA Question Answering
Ryquo · App-In Club
iAgentBench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics
Mar 4, 2026Preetam Prabhu Srikar Dammu, Arnav Palkhiwala, Tanya Roosta +1AI Agent Benchmarks LLM Agent Evaluation
University of Washington Seattle, Washington, USA · UC Berkeley Berkeley, California, USA
ACE-TA: An Agentic Teaching Assistant for Grounded Q&A, Quiz Generation, and Code Tutoring
Feb 20, 2026Himanshu Tripathi, Charlottee Crowell, Kaley Newlin +3Evidence-Grounded QA Intelligent Tutoring Systems
University of Alabama, Tuscaloosa, Alabama, USA · Brown University, Providence, Rhode Island, USA · Meharry Medical College, Nashville, Tennessee, USA +1
AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports
Jan 6, 2026Edward Ajayi, Mustapha Alaba, David Stephen +1Question Answering Document QA
Carnegie Mellon University Africa Kigali, Rwanda

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ACL-Verbatim: hallucination-free question answering for research

Gábor Recski, Szilveszter Tóth, Nadia Verdha, István Boros+1

TU Wien · KR Labs

Hallucination in Language Models Retrieval-Augmented Generation LLM Hallucination Mitigation Question Answering Document QA Factual QA+1

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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 annotation is performed by NLP researchers and is based on synthetic user queries generated using a custom pipeline based on the ScIRGen methodology, paired with chunks of research papers retrieved by VerbatimRAG. On this benchmark, a 150M-parameter ModernBERT token classifier trained on silver supervision from our pipeline achieves the best word-level F1 (53.6), ahead of the strongest evaluated LLM extractor (48.7).