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Hallucination

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TitleStatusHype
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word ProblemCode0
German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth DatasetCode0
KnowAgent: Knowledge-Augmented Planning for LLM-Based AgentsCode3
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated AnswersCode1
The Claude 3 Model Family: Opus, Sonnet, Haiku—0
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering—0
Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models—0
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail KnowledgeCode1
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination MitigationCode2
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection—0
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language ModelsCode1
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast DecodingCode2
Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models—0
Self-Consistent Decoding for More Factual Open ResponsesCode0
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models—0
The All-Seeing Project V2: Towards General Relation Comprehension of the Open WorldCode4
Navigating Hallucinations for Reasoning of Unintentional Activities—0
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScoreCode0
Collaborative decoding of critical tokens for boosting factuality of large language models—0
All in an Aggregated Image for In-Image LearningCode1
Editing Factual Knowledge and Explanatory Ability of Medical Large Language ModelsCode0
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models—0
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful SpaceCode2
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