4 papers
JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation
Fan Xu, Huixuan Zhang, Zhenliang Zhang +2
Current large language models (LLMs) often suffer from hallucination issues, i,e, generating content that appears factual but is actually unreliable. A typical hallucination detect…
SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs
Bei Yan, Zhiyuan Chen, Yuecong Min +4
Despite rapid advances, Large Vision-Language Models (LVLMs) still suffer from hallucinations, i.e., generating content inconsistent with input or established world knowledge, whic…
A Survey of Multimodal Hallucination Evaluation and Detection
Zhiyuan Chen, Yuecong Min, Jie Zhang +4
Multi-modal Large Language Models (MLLMs) have emerged as a powerful paradigm for integrating visual and textual information, supporting a wide range of multi-modal tasks. However,…
C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation
Xu Zhang, Zhifei Liu, Jiahao Wang +4
Despite the rapid advancement of large language models, they remain highly susceptible to generating hallucinations, which significantly hinders their widespread application. Hallu…