CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models
Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lü, Libo Qin · 2025
Investigating hallucination issues in large language models (LLMs) within cross-lingual and cross-modal scenarios can greatly advance the large-scale deployment in real-world applications.Nevertheless, the current studies are limited to a single scenario, either cross-lingual or cross-modal, leaving a gap in the exploration of hallucinations in the joint cross-lingual and cross-modal scenarios.Motivated by this, we introduce a novel joint Cross-lingual and Crossmodal Hallucinations benchmark (CCHall) to fill this gap.Specifically, CCHall simultaneously incorporates both cross-lingual and cross-modal hallucination scenarios, which can be used to assess the cross-lingual and crossmodal capabilities of LLMs.Furthermore, we conduct a comprehensive evaluation on CCHall, exploring both mainstream opensource and closed-source LLMs.The experimental results highlight that current LLMs still struggle with CCHall.We hope CCHall can serve as a valuable resource to assess LLMs in joint cross-lingual and cross-modal scenarios.