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Hallucination

Papers

Showing 17511775 of 1816 papers

TitleStatusHype
Editing Factual Knowledge and Explanatory Ability of Medical Large Language ModelsCode0
Visually Dehallucinative Instruction Generation: Know What You Don't KnowCode0
HALOS: Hallucination-free Organ Segmentation after Organ Resection SurgeryCode0
An Investigation of Evaluation Metrics for Automated Medical Note GenerationCode0
Teacher-Student Adversarial Depth Hallucination to Improve Face RecognitionCode0
HaloScope: Harnessing Unlabeled LLM Generations for Hallucination DetectionCode0
Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language ModelsCode0
HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision MakingCode0
An Inflectional Database for GitksanCode0
HalluShift: Measuring Distribution Shifts towards Hallucination Detection in LLMsCode0
HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination EvaluationCode0
A Comparative Study on Language Models for Task-Oriented Dialogue SystemsCode0
Characterizing Context Influence and Hallucination in SummarizationCode0
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction WrappingCode0
Zero-Resource Hallucination Prevention for Large Language ModelsCode0
Tensor feature hallucination for few-shot learningCode0
CHAIR -- Classifier of Hallucination as ImproverCode0
Chainpoll: A high efficacy method for LLM hallucination detectionCode0
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable QuestionsCode0
HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language ModelsCode0
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological ImagesCode0
Reducing Quantity Hallucinations in Abstractive SummarizationCode0
ANHALTEN: Cross-Lingual Transfer for German Token-Level Reference-Free Hallucination DetectionCode0
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNNCode0
Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM ResponsesCode0
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