CCFQA: A Benchmark for Cross-Lingual and Cross-Modal Speech and Text Factuality Evaluation
Yexing Du, Kaiyuan Liu, Youcheng Pan, Zheng Chu, Bo Yang, Xiaocheng Feng, Ming Liu, Yang Xiang
Abstract
As Large Language Models (LLMs) are increasingly popularized in the multilingual world, ensuring hallucination-free factuality becomes markedly crucial. However, existing benchmarks for evaluating the reliability of Multimodal Large Language Models (MLLMs) predominantly focus on textual or visual modalities with a primary emphasis on English, which creates a gap in evaluation when processing multilingual input, especially in speech. To bridge this gap, we propose a novel Cross-lingual and Cross-modal Factuality benchmark (CCFQA). Specifically, the CCFQA benchmark contains parallel speech-text factual questions across 8 languages, designed to systematically evaluate MLLMs
BibTeX
@inproceedings{aaai2026_ccfqaabenchmarkf,
title = {CCFQA: A Benchmark for Cross-Lingual and Cross-Modal Speech and Text Factuality Evaluation},
author = {Yexing Du and Kaiyuan Liu and Youcheng Pan and Zheng Chu and Bo Yang and Xiaocheng Feng and Ming Liu and Yang Xiang},
booktitle = {AAAI 2026},
year = {2026}
}