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

• URL: http://arxiv.org/abs/2502.09645v1
• Date: Mon, 10 Feb 2025 16:55:13 GMT
• Title: From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models
• Authors: Mayank Vatsa, Aparna Bharati, Surbhi Mittal, Richa Singh,
• Abstract summary: Negation, a linguistic construct conveying absence, denial, or contradiction, poses significant challenges for multilingual multimodal foundation models. We propose a comprehensive taxonomy of negation constructs, illustrating how structural, semantic, and cultural factors influence multimodal foundation models. We advocate for specialized benchmarks, language-specific tokenization, fine-grained attention mechanisms, and advanced multimodal architectures.
• Score: 48.68342037881584
• License:
• 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.

Related papers

• Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming [3.591342811819669]
  Large Language Models (LLMs) demonstrate remarkable proficiency in generating accurate and fluent text. They often struggle with diversity and novelty, leading to repetitive or overly deterministic responses. We introduce inference-time multi-view brainstorming method, a novel approach that enriches input prompts with diverse perspectives.
  arXiv  Detail & Related papers  (2025-02-18T10:04:20Z)
• IOLBENCH: Benchmarking LLMs on Linguistic Reasoning [8.20398036986024]
  We introduce IOLBENCH, a novel benchmark derived from International Linguistics Olympiad (IOL) problems. This dataset encompasses diverse problems testing syntax, morphology, phonology, and semantics. We find that even the most advanced models struggle to handle the intricacies of linguistic complexity.
  arXiv  Detail & Related papers  (2025-01-08T03:15:10Z)
• From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models [17.04716417556556]
  This review visits foundational concepts such as the distributional hypothesis and contextual similarity. We examine both static and contextualized embeddings, underscoring advancements in models such as ELMo, BERT, and GPT. The discussion extends to sentence and document embeddings, covering aggregation methods and generative topic models. Advanced topics such as model compression, interpretability, numerical encoding, and bias mitigation are analyzed, addressing both technical challenges and ethical implications.
  arXiv  Detail & Related papers  (2024-11-06T15:40:02Z)
• A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [51.8203871494146]
  The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient. This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
  arXiv  Detail & Related papers  (2024-05-17T17:47:39Z)
• Multi-modal Latent Space Learning for Chain-of-Thought Reasoning in Language Models [25.058162782167503]
  Chain-of-thought (CoT) reasoning has exhibited impressive performance in language models for solving complex tasks and answering questions. We introduce a novel approach for multi-modal CoT reasoning that utilizes latent space learning via diffusion processes to generate effective image features that align with language thoughts. Our method fuses image features and text representations at a deep level and improves the complex reasoning ability of multi-modal CoT.
  arXiv  Detail & Related papers  (2023-12-14T09:13:09Z)
• TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild [102.93338424976959]
  We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models.
  arXiv  Detail & Related papers  (2023-09-14T15:34:01Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities [10.721189858694396]
  We study the unification of multilingual and multimodal (MultiX) streams. We review the languages studied, gold or silver data with parallel annotations, and understand how these modalities and languages interact in modeling. We present an account of the modeling approaches along with their strengths and weaknesses to better understand what scenarios they can be used reliably.
  arXiv  Detail & Related papers  (2022-10-30T21:46:01Z)
• Analyzing the Limits of Self-Supervision in Handling Bias in Language [52.26068057260399]
  We evaluate how well language models capture the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. Our analyses indicate that language models are capable of performing these tasks to widely varying degrees across different bias dimensions, such as gender and political affiliation.
  arXiv  Detail & Related papers  (2021-12-16T05:36:08Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.