LLM Answer Verification

LLM: Large Language Model

Follow this topic Ask about this topic

Momentum

17 papers in the last four weeks, up 113% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 102

All topics

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models
Apr 23, 2026Vipula Rawte, Ryan Rossi, Franck Dernoncourt +1LLM Answer Verification LLM Auditing
Adobe · Adobe Research
Beyond Semantic Similarity: A Component-Wise Evaluation Framework for Medical Question Answering Systems with Health Equity Implications
Apr 21, 2026Abu Noman Md Sakib, Md. Main Oddin Chisty, Zijie ZhangLLM Answer Verification Healthcare
University of Texas at San Antonio, USA · Khulna University of Engineering and Technology, Bangladesh
FUSE: Ensembling Verifiers with Zero Labeled Data
Apr 20, 2026Joonhyuk Lee, Virginia Ma, Sarah Zhao +4Neural Network Verification LLM Evaluation
Stanford University · Google
From Tokens to Steps: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning
Apr 16, 2026Kiran Purohit, Ramasuri Narayanam, Soumyabrata PalLLM Answer Verification Speculative Decoding
IIT Kharagpur · Adobe Research
Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
Mar 5, 2026Qiao Jin, Yin Fang, Lauren He +12LLM Answer Verification Healthcare
Division of Intramural Research, National Library of Medicine, National Institutes of Health · Department of Computer Science, University of Virginia · Center for Cancer Research, National Cancer Institute, National Institutes of Health +3
Verify to Amplify: Improving Reasoning via Learned Chain-of-Thought Verification
Mar 3, 2026Maria-Florina Balcan, Avrim Blum, Kiriaki Fragkia +2LLM Evaluation LLM Answer Verification
Carnegie Mellon University · Toyota Technological Institute at Chicago · Northwestern University
When Is Enough Not Enough? Illusory Completion in Search Agents
Feb 7, 2026Dayoon Ko, Jihyuk Kim, Sohyeon Kim +5AI Agent Reliability LLM Answer Verification
Seoul National University · LG AI Research · University of Illinois Chicago
Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving
Dec 10, 2025Liangliang Chen, Weiyu Sun, Huiru Xie +2LLM Answer Verification Educational Technology
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
The 4/ $δ$ Bound: Designing Predictable LLM-Verifier Systems for Formal Method Guarantee
Nov 30, 2025Pierre Dantas, Lucas Cordeiro, Youcheng Sun +1LLM Answer Verification Formal Verification
Dept. of Computer Science, The University of Manchester, UK. · Dept. of Electrical Engineering, Federal University of Amazonas (UFAM), Brazil.
Pessimistic Verification for Open Ended Math Questions
Nov 26, 2025Yanxing Huang, Zihan Tang, Zejin Lin +2LLM Answer Verification LLM Mathematical Reasoning
Qiuzhen College, Tsinghua University, Beijing, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China · Dept. of Comp. Sci & Tech., Institute for AI, Tsinghua University, Beijing, China.
Training-free LLM Verification via Recycling Few-shot Examples
Jun 8, 2025Dongseok Lee, Jimyung Hong, Dongyoung Kim +1LLM Answer Verification Few-Shot Prompting
Yonsei University · KAIST
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
Mar 3, 2025Tin Nguyen, Logan Bolton, Mohammad Reza Taesiri +2LLM Grounding LLM Answer Verification
Auburn University · University of Alberta

Apr 23, 2026, cs.AIJ/K move · Enter open · S save

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

Vipula Rawte, Ryan Rossi, Franck Dernoncourt, Nedim Lipka

Adobe · Adobe Research

LLM Answer Verification LLM Auditing LLM Interpretability

Open paper AskPDF arXiv

Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual inaccuracies and hallucinations. This limitation poses significant risks in high-stakes domains such as healthcare, law, and scientific communication, where trust and verifiability are paramount. In this paper, we introduce DAVinCI - a Dual Attribution and Verification framework designed to enhance the factual reliability and interpretability of LLM outputs. DAVinCI operates in two stages: (i) it attributes generated claims to internal model components and external sources; (ii) it verifies each claim using entailment-based reasoning and confidence calibration. We evaluate DAVinCI across multiple datasets, including FEVER and CLIMATE-FEVER, and compare its performance against standard verification-only baselines. Our results show that DAVinCI significantly improves classification accuracy, attribution precision, recall, and F1-score by 5-20%. Through an extensive ablation study, we isolate the contributions of evidence span selection, recalibration thresholds, and retrieval quality. We also release a modular DAVinCI implementation that can be integrated into existing LLM pipelines. By bridging attribution and verification, DAVinCI offers a scalable path to auditable, trustworthy AI systems. This work contributes to the growing effort to make LLMs not only powerful but also accountable.

Momentum

Related topics

Latest papers 102