LLM Answer Verification

LLM: Large Language Model

The field studies how to check an LLM’s answers, reasoning steps, citations, and evidence before accepting, revising, or abstaining from them. Approaches include retrieval and natural-language inference for evidence checking, symbolic tools and proof assistants for formal tasks, learned reward models for ranking candidate answers, and self- or cross-model review. These methods are tested in settings such as mathematical reasoning, multi-hop question answering, long-document and medical QA, and code or SQL generation, using benchmarks including MATH, GPQA, HotpotQA, FEVER, and BIRD. A central challenge is that verifiers can share a generator’s blind spots or reject valid answers, so accuracy must be balanced against calibration, evidence coverage, cost, and the risk of harmful revisions.

Latest papers

151 papers11 papers in the last four weeks, down 63% on the four weeks before. 0.1% of all new papers.

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 PalLanguage Model Self-Verification LLM Answer Verification
IIT Kharagpur · Adobe Research
Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality
Apr 6, 2026Xiaoyuan Zhu, Kimberly Le Truong, Riccardo Fogliato +8LLM Evaluation LLM Answer Verification
University of Southern California · Carnegie Mellon University · Microsoft Core AI +1
Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification
Apr 4, 2026Aisvarya Adeseye, Jouni Isoaho, Adeyemi AdeseyeLLM Quantization Low-Bit Quantization
University of Turku, Turku, Finland · Brilloconnetz Partners avoin yhtiö, Turku, Finland
SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning
Apr 2, 2026Daeyong Kwon, Soyoung Yoon, Seung-won HwangMulti-Hop QA LLM Answer Verification
Seoul National University, South Korea
ThinknCheck: Grounded Claim Verification with Compact, Reasoning-Driven, and Interpretable Models
Apr 2, 2026Delip Rao, Feijiang Han, Chris Callison-BurchClaim Verification LLM Answer Verification
University of Pennsylvania, Philadelphia, PA, USA
Atomic and Holistic LLM Judges for Reference-Grounded Support Labels: A Prompt-Controlled Comparison
Mar 30, 2026Xinran ZhangLLM-as-a-Judge Language Model Generation Evaluation
University of California, Berkeley
StepCache: Step-Level Reuse with Lightweight Verification and Selective Patching for LLM Serving
Mar 24, 2026Azam NouriLLM Serving LLM Inference Efficiency
Department of Science, Technology & Mathematics, Lincoln University, USA
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
Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval
Mar 18, 2026Md. Asraful Haque, Aasar Mehdi, Maaz Mahboob +1LLM Hallucination Mitigation LLM Grounding
Computational Unit, Z.H. College of Engineering & Technology, Aligarh Muslim University, India · Interdisciplinary Center for Artificial Intelligence, Aligarh Muslim University, India · Department of Computer Engineering, Aligarh Muslim University, India
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
A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification
Mar 11, 2026Yichi Zhu, Kan Ling, Xu Liu +3Knowledge Graphs Biomedical KGs
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
Reinforcement Learning with Conditional Expectation Reward
Mar 11, 2026Changyi Xiao, Caijun Xu, Yixin CaoRL for Language Model Reasoning LLM Answer Verification
Fudan University
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
Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems
Mar 4, 2026Xunzhuo Liu, Bowei He, Xue Liu +2LLM Answer Verification Retrieval-Augmented Generation
Red Hat · MBZUAI, McGill University
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
Parallel Test-Time Scaling with Multi-Sequence Verifiers
Mar 3, 2026Yegon Kim, Seungyoo Lee, Chaeyun Jang +2LLM Answer Verification Test-Time Scaling
Graduate School of AI, KAIST, Seoul, South Korea
RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm
Mar 2, 2026Jiahe Liu, Qinkai Yu, Jingcheng Niu +5Retrieval-Augmented Generation LLM Routing
Technical University of Denmark · University of Exeter · University of Toronto +4
PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A
Feb 24, 2026Anna Martin-Boyle, Cara A. C. Leckey, Martha C. Brown +1LLM Answer Verification LLM Reliability
University of Minnesota Minneapolis, Minnesota, USA · NASA Langley Research Center Hampton, Virginia, USA
Pipeline for Verifying LLM-Generated Mathematical Solutions
Feb 24, 2026Varvara Sazonova, Dmitri Shmelkin, Stanislav Kikot +1LLM Answer Verification LLM Evaluation
Moscow State University · Huawei Technologies Co., Ltd
Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction
Feb 24, 2026Hui Wen Goh, Jonas MuellerLLM Answer Verification LLM Uncertainty Estimation
Cleanlab
When to Trust the Cheap Check: Weak and Strong Verification for Reasoning
Feb 19, 2026Shayan Kiyani, Sima Noorani, George Pappas +1LLM Answer Verification Reasoning Verification
University of Pennsylvania
Escaping the Cognitive Well: Efficient Competition Math with Off-the-Shelf Models
Feb 18, 2026Xingyu Dang, Rohit Agarwal, Rodrigo Porto +3Language Model Self-Verification Mathematical Reasoning
Princeton University · Princeton Language and Intelligence
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 +1
VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning
Jan 27, 2026Vikash Singh, Darion Cassel, Nathaniel Weir +2LLM Answer Verification Reasoning Consistency in Language Models
Case Western Reserve University · Amazon Web Services
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

Apr 21, 2026J/K move · Enter open · S save

Beyond Semantic Similarity: A Component-Wise Evaluation Framework for Medical Question Answering Systems with Health Equity Implications

Abu Noman Md Sakib, Md. Main Oddin Chisty, Zijie Zhang

University of Texas at San Antonio, USA · Khulna University of Engineering and Technology, Bangladesh

LLM Answer Verification Healthcare Algorithmic Fairness Medical QA Language Model Bias Evaluation Medical LLMs

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The use of Large Language Models (LLMs) to support patients in addressing medical questions is becoming increasingly prevalent. However, most of the measures currently used to evaluate the performance of these models in this context only measure how closely a model's answers match semantically, and therefore do not provide a true indication of the model's medical accuracy or of the health equity risks associated with it. To address these shortcomings, we present a new evaluation framework for medical question answering called VB-Score (Verification-Based Score) that provides a separate evaluation of the four components of entity recognition, semantic similarity, factual consistency, and structured information completeness for medical question-answering models. We perform rigorous reviews of the performance of three well-known and widely used LLMs on 48 public health-related topics taken from high-quality, authoritative information sources. Based on our analyses, we discover a major discrepancy between the models' semantic and entity accuracy. Our assessments of the performance of all three models show that each of them has almost uniformly severe performance failures when evaluated against our criteria. Our findings indicate alarming performance disparities across various public health topics, with most of the models exhibiting 13.8% lower performance (compared to an overall average) for all the public health topics that relate to chronic conditions that occur in older and minority populations, which indicates the existence of what's known as condition-based algorithmic discrimination. Our findings also demonstrate that prompt engineering alone does not compensate for basic architectural limitations on how these models perform in extracting medical entities and raise the question of whether semantic evaluation alone is a sufficient measure of medical AI safety.