作者位次 #2/5ACL 2026Poster· Natural Language Processing
INFACT : A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video- LLM s
Yangjunqi · Yuecong Min · Jie Zhang · Shiguang Shan · Xilin CHEN
摘要
Despite rapid progress, Video Large Language Models (Video-LLMs) remain unreliable due to hallucinations, which are outputs that contradict either video evidence (faithfulness) or verifiable world knowledge (factuality).Existing benchmarks provide limited coverage of factuality hallucinations and...