# Bears, all bears, and some bears. Language Constraints on Language Models' Inductive Inferences

arXiv: https://arxiv.org/abs/2601.09852 · HTML: https://mlsanity.com/paper/2601.09852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 17 of 17)

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- [Explanatory models in neuroscience: Part 1 -- taking mechanistic abstraction seriously](https://mlsanity.com/paper/2104.01490/) (2021)
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- [DevBench: A multimodal developmental benchmark for language learning](https://mlsanity.com/paper/2406.10215/) (2024)
- [Characterizing the Role of Similarity in the Property Inferences of Language Models](https://mlsanity.com/paper/2410.22590/) (2024)
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- [Representation biases: will we achieve complete understanding by analyzing representations?](https://mlsanity.com/paper/2507.22216/) (2025)
- [VAQUUM: Are Vague Quantifiers Grounded in Visual Data?](https://mlsanity.com/paper/2502.11874/) (2025)
- [VISaGE: Understanding Visual Generics and Exceptions](https://mlsanity.com/paper/2510.12548/) (2025)
- [Generics are puzzling. Can language models find the missing piece?](https://mlsanity.com/paper/2412.11318/) (2024)

## Abstract (arXiv)

Language places subtle constraints on how we make inductive inferences. Developmental evidence by Gelman et al. (2002) has shown children (4 years and older) to differentiate among generic statements ("Bears are daxable"), universally quantified NPs ("all bears are daxable") and indefinite plural NPs ("some bears are daxable") in extending novel properties to a specific member (all > generics > some), suggesting that they represent these types of propositions differently. We test if these subtle differences arise in general purpose statistical learners like Vision Language Models, by replicating the original experiment. On tasking them through a series of precondition tests (robust identification of categories in images and sensitivities to all and some), followed by the original experiment, we find behavioral alignment between models and humans. Post-hoc analyses on their representations revealed that these differences are organized based on inductive constraints and not surface-form differences.