CCHall：面向大语言模型跨语言与跨模态联合幻觉检测的新基准
CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models
AI总结：
针对现有研究仅关注单一跨语言或跨模态幻觉的局限，提出联合基准CCHall，同时涵盖两种场景，评估主流开源与闭源大模型，发现其仍面临挑战。
摘要：
研究大语言模型（LLMs）在跨语言和跨模态场景中的幻觉问题，可以极大地推动其在现实世界应用中的大规模部署。然而，当前的研究仅限于单一场景，即要么是跨语言，要么是跨模态，在跨语言与跨模态联合场景中探索幻觉方面存在空白。受此启发，我们引入了一个新的联合跨语言和跨模态幻觉基准（CCHall）来填补这一空白。具体而言，CCHall同时整合了跨语言和跨模态的幻觉场景，可用于评估LLMs的跨语言和跨模态能力。此外，我们在CCHall上进行了全面评估，探索了主流开源和闭源LLMs。实验结果表明，当前LLMs在CCHall上仍面临挑战。我们希望CCHall能成为评估LLMs在跨语言与跨模态联合场景中表现的重要资源。
英文摘要：
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 Cross-modal 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 cross-modal capabilities of LLMs. Furthermore, we conduct a comprehensive evaluation on CCHall, exploring both mainstream open-source 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
