mlsanityData to 14 Oct 2026

How important is language for human-like intelligence?

arXiv 2509.15560 ↗First posted 19 Sep 2025cs.CLcited by 2

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Abstract (arXiv)

We use language to communicate our thoughts. But is language merely the expression of thoughts, which are themselves produced by other, nonlinguistic parts of our minds? Or does language play a more transformative role in human cognition, allowing us to have thoughts that we otherwise could (or would) not have? Recent developments in artificial intelligence (AI) and cognitive science have reinvigorated this old question. We argue that language may hold the key to the emergence of both more general AI systems and central aspects of human intelligence. We highlight two related properties of language that make it such a powerful tool for developing domain--general abilities. First, language offers compact representations that make it easier to represent and reason about many abstract concepts (e.g., exact numerosity). Second, these compressed representations are the iterated output of collective minds. In learning a language, we learn a treasure trove of culturally evolved abstractions. Taken together, these properties mean that a sufficiently powerful learning system exposed to language--whether biological or artificial--learns a compressed model of the world, reverse engineering many of the conceptual and causal structures that support human (and human-like) thought.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 50 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

12 of 50 references.

References · 12

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2023Can AI-Generated Text be Reliably Detected?cited by 386
  3. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
  4. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  5. 2022A fine-grained comparison of pragmatic language understanding in humans and language modelscited by 70
  6. 2024LLM Circuit Analyses Are Consistent Across Training and Scalecited by 63
  7. 2024Language models align with human judgments on key grammatical constructionscited by 49
  8. 2023Large language models predict human sensory judgments across six modalitiescited by 48
  9. 2023Break It Down: Evidence for Structural Compositionality in Neural Networkscited by 44
  10. 2025The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Modelscited by 29
  11. 2024Does Thought Require Sensory Grounding? From Pure Thinkers to Large Language Modelscited by 7
  12. 2025Language and Thought: The View from LLMscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.