破解多模态模型理解否定的难题，提升跨语言文化语义精准度
From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models
- 构建否定表达的三维度分类体系：结构、语义与文化影响
- 揭示现有模型在跨语言否定识别上的显著缺陷
- 适合研究多模态理解与语言鲁棒性的学者参考
否定作为一种表达缺失、否认或矛盾的语言现象，对多语言多模态基础模型构成重大挑战。尽管这些模型在机器翻译、文本引导生成、图像描述、音频交互和视频处理等任务中表现优异，但在不同语言和文化背景下准确理解否定仍存在困难。本文提出一个全面的否定构造分类框架，阐明结构、语义和文化因素如何影响多模态基础模型的表现。我们列出开放性研究问题，强调解决这些问题对实现稳健否定处理的重要性，并倡导建立专用评估基准、语言特异性分词、细粒度注意力机制以及先进多模态架构。这些策略有助于发展更具适应性和语义精确性的多模态基础模型，使其更好地应对多语言、多模态环境中否定表达的复杂性。
原文摘要 · Abstract (English)
Negation, a linguistic construct conveying absence, denial, or contradiction, poses significant challenges for multilingual multimodal foundation models. These models excel in tasks like machine translation, text-guided generation, image captioning, audio interactions, and video processing but often struggle to accurately interpret negation across diverse languages and cultural contexts. In this perspective paper, we propose a comprehensive taxonomy of negation constructs, illustrating how structural, semantic, and cultural factors influence multimodal foundation models. We present open research questions and highlight key challenges, emphasizing the importance of addressing these issues to achieve robust negation handling. Finally, we advocate for specialized benchmarks, language-specific tokenization, fine-grained attention mechanisms, and advanced multimodal architectures. These strategies can foster more adaptable and semantically precise multimodal foundation models, better equipped to navigate and accurately interpret the complexities of negation in multilingual, multimodal environments.
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