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Hallucination

Papers

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Showing 126–150 of 1816 papers

TitleStatusHype
Less is More: Mitigating Multimodal Hallucination from an EOS Decision PerspectiveCode2
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination MitigationCode2
Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image DescriptionsCode2
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference AlignmentCode2
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language ModelsCode2
A Survey on Hallucination in Large Vision-Language ModelsCode2
ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language ModelsCode2
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step QuestionsCode2
VHM: Versatile and Honest Vision Language Model for Remote Sensing Image AnalysisCode2
Granite GuardianCode2
Calibrated Self-Rewarding Vision Language ModelsCode2
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast DecodingCode2
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question AnsweringCode2
MLAgentBench: Evaluating Language Agents on Machine Learning ExperimentationCode2
Benchmarking Large Language Models in Retrieval-Augmented GenerationCode2
Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge EnhancementCode2
GPT-NER: Named Entity Recognition via Large Language ModelsCode2
FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning EvaluationCode2
FreshLLMs: Refreshing Large Language Models with Search Engine AugmentationCode2
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine TranslationCode2
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge GraphCode2
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsCode2
FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"Code2
CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMsCode2
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language ModelsCode2
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