Papers Fact Verification
“Fact Verification” 태그가 달린 논문 279편 · 필터 해제
Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification
Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable hallucination detection in high-stakes cli…
Fact VerificationLearning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation
We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting synthetic examples with a fixed reward threshold, our method formul…
Natural Language UnderstandingFact VerificationReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification
Multi-hop fact verification, which verifies claims by reasoning over multiple pieces of evidence, is critical for combating misinformation on social media yet remains highly challenging. Recent methods primarily rely on …
Fact VerificationReasoning Errors Have a Region and a Direction in the Residual-Stream Trajectory of LLMs
As language models are increasingly used for tasks that require verifiable reasoning, reliably distinguishing sound reasoning from flawed reasoning has become an important practical problem. Recent trajectory-based metho…
Fact VerificationVetScore: Risk-Weighted Fact Verification for Veterinary Long-Form QA with Citations
Citation excerpts can be used to increase the reliability of generated outputs and their faithfulness to cited sources, which is especially important in high-stakes domains such as human and veterinary medicine. However,…
Question AnsweringFact VerificationKnowledgeless Language Models: Suppressing Parametric Recall for Evidence-Grounded Language Modeling
Language models encode substantial factual knowledge in their parameters, which can lead to unreliable behavior when this knowledge is outdated, incomplete, or misaligned with the provided context. In this work, we study…
Question AnsweringFact VerificationAgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs
Knowledge graphs (KGs) are often automatically constructed from large-scale corpora, but they inevitably contain factual errors due to noisy sources and extraction failures, and verifying them reliably at industrial scal…
Fact VerificationKnowledge GraphsMEDIAREF: A Public Knowledge Store for Media Background Checks
LLM-based retrieval-augmented generation (RAG) is increasingly used for automated fact-checking (AFC) and related tasks. By grounding LLM outputs in retrieved evidence, RAG-based systems provide transparent justification…
Fact VerificationDon't Blindly Trust It: How Unreliable Feedback Breaks Tool-Using LLM Agents
Tool-augmented agents are typically evaluated by their gains under reliable external feedback. Yet these gains leave open a key counterfactual: when feedback is unreliable, would the agent be better off receiving no task…
Question AnsweringFact VerificationA Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization
Despite the significant advancement of LLMs in conversation summarization, their evaluation remains limited by insufficient scenarios, input lengths, and sample sizes. Furthermore, existing benchmarks often omit frontier…
Fact VerificationFrom Verdict to Process: Agentic Reinforcement Learning for Multi-Stage Fact Verification
Recent approaches combining Large Language Models (LLMs) with retrieval-augmented reasoning have shown promise for automated fact verification. To process complex claims, these verification pipelines typically execute mu…
Reinforcement LearningFact VerificationAnswer GenerationCRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification
Table reasoning remains challenging for large language models (LLMs), particularly in tasks that require multi-step inference over long and structured tables. Existing approaches predominantly rely on single-direction re…
Question AnsweringFact VerificationCascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
Multi-step agentic retrieval-augmented generation (RAG) pipelines have demonstrated significant capability for complex reasoning tasks, yet remain vulnerable to a class of failure that existing hallucination detection me…
Fact VerificationEvidence Absence Is Not Evidence Insufficiency: Diagnosing NEI Construction Artifacts in Fact Verification
Evidence absence is not evidence insufficiency, but fact verification benchmarks can make them observationally similar. The Not Enough Information (NEI) label is often operationalized through different evidence condition…
Fact VerificationSEEK: Semantic Evidence Extraction via Adaptive ChunKing for Multilingual Fact-Checking
Multilingual fact verification requires evidence that is both relevant and sufficiently complete for reliable factuality prediction. However, existing systems often rely on search snippets, sentence-level evidence, or lo…
Fact VerificationJuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors
As large language models (LLMs) are increasingly deployed to users around the world, they are integrated into everyday tasks across diverse cultural contexts, from drafting personal communications to brainstorming creati…
Fact VerificationHow do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remain unclear. To address this problem, this…
Fact VerificationFrom Table to Cell: Attention for Better Reasoning with TABALIGN
Multi-step LLM reasoning over structured tables fails because planning and execution share no explicit cell-grounding contract. Existing methods constrain the planner to a left-to-right factorization at odds with table p…
Question AnsweringFact VerificationCAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction
With the vast amount of content uploaded every hour, along with the AI generated content that can include hallucinations, Automated Fact-Checking (AFC) has become increasingly vital, as it is infeasible for human fact-ch…
Fact VerificationGrounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
Multi-Hop Fact Verification requires complex reasoning across disparate evidence, posing significant challenges for Large Language Models , which may suffer from hallucinations and fractured logical chains. Existing meth…
Reinforcement LearningFact VerificationCausal Inference