hallucination detection

DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models EMNLP 2025 DUTJBD at SemEval-2025 Task 3: A Range of Approaches for Predicting Hallucination Generation in Models SemEval 2025 EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits ACL 2025 EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding EMNLP 2025 Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection AAAI 2025 Estimating LLM Consistency: A User Baseline vs Surrogate Metrics EMNLP 2025 ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries ACL 2025 Evaluating Evaluation Metrics – The Mirage of Hallucination Detection EMNLP 2025 E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification EMNLP 2025 Explainable Hallucination through Natural Language Inference Mapping ACL 2025 FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation ACL 2025 FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data NAACL 2025 FACTCHECKMATE: Preemptively Detecting and Mitigating Hallucinations in LMs EMNLP 2025 Fact-Controlled Diagnosis of Hallucinations in Medical Text Summarization Interspeech 2025 FactLens: Benchmarking Fine-Grained Fact Verification ACL 2025 Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts EMNLP 2025 FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs NAACL 2025 FENJI at SemEval-2025 Task 3: Retrieval-Augmented Generation and Hallucination Span Detection SemEval 2025 FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning EMNLP 2025 FinGrAct: A Framework for FINe-GRrained Evaluation of ACTionability in Explainable Automatic Fact-Checking EMNLP 2025 FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation ACL 2025 FiRC-NLP at SemEval-2025 Task 3: Exploring Prompting Approaches for Detecting Hallucinations in LLMs SemEval 2025 From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs ACL 2025 From RAG to Reality: Coarse-Grained Hallucination Detection via NLI Fine-Tuning ACL 2025 From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization NAACL 2025