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Hallucination

Papers

Showing 301325 of 1816 papers

TitleStatusHype
Circuit Transformer: A Transformer That Preserves Logical EquivalenceCode1
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?Code1
Federated Recommendation via Hybrid Retrieval Augmented GenerationCode1
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated AnswersCode1
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail KnowledgeCode1
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language ModelsCode1
All in an Aggregated Image for In-Image LearningCode1
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean DiscrepancyCode1
Citation-Enhanced Generation for LLM-based ChatbotsCode1
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language ModelsCode1
Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided DecodingCode1
Visual Hallucinations of Multi-modal Large Language ModelsCode1
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue SummarizationCode1
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language ModelsCode1
EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language ModelsCode1
Uncertainty Quantification for In-Context Learning of Large Language ModelsCode1
Into the Unknown: Self-Learning Large Language ModelsCode1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & HallucinationsCode1
Introspective Planning: Aligning Robots' Uncertainty with Inherent Task AmbiguityCode1
Training Language Models to Generate Text with Citations via Fine-grained RewardsCode1
INSIDE: LLMs' Internal States Retain the Power of Hallucination DetectionCode1
Unified Hallucination Detection for Multimodal Large Language ModelsCode1
Skip : A Simple Method to Reduce Hallucination in Large Vision-Language ModelsCode1
K-QA: A Real-World Medical Q&A BenchmarkCode1
How well can a large language model explain business processes as perceived by users?Code1
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