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Hallucination

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Showing 926–950 of 1816 papers

TitleStatusHype
CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report Generation—0
MoE-RBench: Towards Building Reliable Language Models with Sparse Mixture-of-ExpertsCode1
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language ModelsCode2
mDPO: Conditional Preference Optimization for Multimodal Large Language ModelsCode2
Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals—0
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded ConversationsCode0
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language ModelsCode3
Detecting and Evaluating Medical Hallucinations in Large Vision Language Models—0
MMRel: A Relation Understanding Benchmark in the MLLM EraCode1
Understanding Hallucinations in Diffusion Models through Mode InterpolationCode2
DefAn: Definitive Answer Dataset for LLMs Hallucination EvaluationCode0
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMsCode1
Understanding Sounds, Missing the Questions: The Challenge of Object Hallucination in Large Audio-Language ModelsCode2
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis—0
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic EntropyCode1
Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image DescriptionsCode2
A Probabilistic Framework for LLM Hallucination Detection via Belief Tree PropagationCode0
HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination EvaluationCode0
On the Hallucination in Simultaneous Machine TranslationCode0
Progressive Query Expansion for Retrieval Over Cost-constrained Data Sources—0
Estimating the Hallucination Rate of Generative AI—0
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation—0
CRAG -- Comprehensive RAG BenchmarkCode3
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language ModelsCode1
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