From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models

TL;DR

This paper tackles the problem of understanding negation in multilingual multimodal foundation models, where negation meaning must be interpreted across text, image, audio, and video. It proposes a comprehensive taxonomy of negation spanning syntactic, morphological, lexical-semantic, and pragmatic dimensions, with 16 representative subtypes and multilingual examples. The authors present a suite of open research questions (RQ1–RQ11) and advocate for specialized benchmarks, language-specific tokenization, and fine-grained attention alongside multimodal architectures to better capture negation. By advancing targeted evaluation and architectural strategies, the work aims to reduce misinterpretations in tasks like text-to-image generation, retrieval, and cross-lingual understanding, thereby enhancing reliability of multilingual multimodal AI systems.

Abstract

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.

Figures (3)

• Figure 1: Text-to-image generative models face significant challenges in accurately interpreting negations within multilingual prompts. Regardless of the specific prompts, the models consistently produced images of dogs with ears, demonstrating a persistent inability to correctly process negated terms. This example illustrates the limitations of models such as DALL-E 3 and Llama 3.2 in handling various forms of negation across both English and Hindi languages. Furthermore, models like Midjourney and SDXL exhibit even more pronounced deficiencies, as they fail to process or understand the 'Hindi’ language altogether.
• Figure 2: Overview of the proposed taxonomy for negations in multimodal contexts. The accompanying images, generated using multimodal models such as Gemini (v1.5 Flash) and Adobe Firefly, alongside examples retrieved from Google, illustrate how negated concepts are represented across different modalities in response to specific prompts, queries, or descriptions. These visuals aim to provide insight into the diverse ways negations manifest but do not capture the full complexity of the concepts. Additionally, some examples may inadvertently reflect biases present in the data or models.
• Figure 3: Example showcasing how text-to-image models fail while creating images using negative prompts in different languages. The top row contains images generated through English prompts. The bottom row contains images corresponding to similar prompts in Spanish, Bangla, and Japanese. DALL-E 3 and Midjourney models are used for generation, illustrating the limitations of these models in accurately interpreting multilingual prompts and complex negations across languages.