mlsanityData to 14 Oct 2026

A Grounded Typology of Word Classes

arXiv 2412.10369 ↗First posted 13 Dec 2024cs.CL cs.CVcited by 2

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Abstract (arXiv)

We propose a grounded approach to meaning in language typology. We treat data from perceptual modalities, such as images, as a language-agnostic representation of meaning. Hence, we can quantify the function--form relationship between images and captions across languages. Inspired by information theory, we define "groundedness", an empirical measure of contextual semantic contentfulness (formulated as a difference in surprisal) which can be computed with multilingual multimodal language models. As a proof of concept, we apply this measure to the typology of word classes. Our measure captures the contentfulness asymmetry between functional (grammatical) and lexical (content) classes across languages, but contradicts the view that functional classes do not convey content. Moreover, we find universal trends in the hierarchy of groundedness (e.g., nouns > adjectives > verbs), and show that our measure partly correlates with psycholinguistic concreteness norms in English. We release a dataset of groundedness scores for 30 languages. Our results suggest that the grounded typology approach can provide quantitative evidence about semantic function in language.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 61 references that are in the map. First posted on arXiv 13 Dec 2024.

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6 of 61 references.

References · 6

  1. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  2. 2024PaliGemma: A versatile 3B VLM for transfercited by 696
  3. 2024How to Compute the Probability of a Wordcited by 36
  4. 2024Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilitiescited by 30
  5. 2023Semantic and Expressive Variation in Image Captions Across Languagescited by 8
  6. 2024Cross-Lingual and Cross-Cultural Variation in Image Descriptionscited by 5

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.

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  1. 2025The Representational Alignment between Humans and Language Models is implicitly driven by a Concreteness Effect
  2. 2026Real Images, Worse Judgments: Evaluating Vision-Language Models on Concreteness and Imagery
  3. 2026The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans
  4. 2026Grounded Concreteness: Human-Like Concreteness Sensitivity in Vision-Language Models
  5. 2025Linguistic Interpretability of Transformer-based Language Models: a systematic review
  6. 2026Beyond Hooking Onto the World: Referential Profiles and the Numerical Structure of LLM Grounding
  7. 2023Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models
  8. 2023Separating form and meaning: Using self-consistency to quantify task understanding across multiple senses