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Hallucination Evaluation

Evaluate the ability of LLM to generate non-hallucination text or assess the capability of LLM to recognize hallucinations.

Papers

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Showing 41–49 of 49 papers

TitleStatusHype
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework—0
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models—0
TextSquare: Scaling up Text-Centric Visual Instruction Tuning—0
HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation—0
Can We Catch the Elephant? A Survey of the Evolvement of Hallucination Evaluation on Natural Language Generation—0
ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks—0
Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs—0
TLDR: Token-Level Detective Reward Model for Large Vision Language Models—0
Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs—0
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