Papers with human language acquisition
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling (2024.findings-acl)
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| Challenge: | Neural language models (LMs) are trained on orders of magnitude more language data than human language learners receive, but without supervision from other sensory modalities that play a crucial role in human learning. |
| Approach: | They propose a grounded language learning procedure that leverages visual supervision to improve textual representations. |
| Outcome: | The proposed procedure outperforms standard language-only models in terms of learning efficiency in small and developmentally plausible data regimes and improves perplexity by around 5% on multiple language modeling tasks compared to other models trained on the same amount of text data. |
Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks (2026.acl-short)
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| Challenge: | Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. |
| Approach: | They propose a framework that integrates Language Learning Tasks alongside standard next-token prediction to stimulate the acquisition of morphological, syntactic, and semantic knowledge. |
| Outcome: | The proposed framework improves performance on linguistic competence benchmarks while maintaining competitive performance on reasoning tasks. |
Humans and transformer LMs: Abstraction drives language learning (2026.eacl-long)
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| Challenge: | lexical semantic and syntactic categories emerge using novel divergence-based metrics . |
| Approach: | They compare transformer-based language model's linguistic categories learning to exemplar-based accounts of human language acquisition. |
| Outcome: | The proposed model can be used as an existence proof for human language acquisition. |
Modelling Language Acquisition through Syntactico-Semantic Pattern Finding (2023.findings-eacl)
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| Challenge: | Usage-based theories of language acquisition have documented the processes by which children acquire language through communicative interaction. |
| Approach: | They propose a method for learning grammars based on similarities and differences in linguistic observations alone. |
| Outcome: | The proposed method is able to learn compositional lexical and item-based constructions of variable extent and degree of abstraction, along with a network of emergent syntactic categories. |
Learning Functional Distributional Semantics with Visual Data (2022.acl-long)
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| Challenge: | Functional Distributional Semantics models the meaning of a word as a binary classifier rather than a numerical vector. |
| Approach: | They propose a method to train a Functional Distributional Semantics model with grounded visual data. |
| Outcome: | The proposed model outperforms previous work on learning semantics from Visual Genome on four external evaluation datasets. |
Priorless Recurrent Networks Learn Curiously (2020.coling-main)
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| Challenge: | a recent study shows domain-general recurrent neural networks reproduce human language behaviours . a lack of a unified concept of number agreement between these processes is a limitation of the model . |
| Approach: | They propose to use domain-general recurrent neural networks without explicit linguistic inductive biases to reproduce human language behaviours. |
| Outcome: | The proposed model can learn number agreement within unnatural sentences, the authors show . they show that the model has an effective understanding of singular versus plural for individual sentences . |
Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition (2025.acl-long)
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| Challenge: | Large language models possess general linguistic abilities comparable to humans but their efficiency in language acquisition remains far inferior. |
| Approach: | They propose a method that initially constrains working memory during the early stages of training and gradually relaxes this constraint as learning progresses. |
| Outcome: | The proposed method outperforms conventional methods without memory constraints or with static memory constraints. |
Modeling Overregularization in Children with Small Language Models (2024.findings-acl)
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| Challenge: | Existing research has analyzed regularization in language acquisition only by modeling word inflection directly, which is unnatural in light of human language acquisition. |
| Approach: | They hypothesize that language models that imitate errors children make during language acquisition have a learning process more similar to humans. |
| Outcome: | The proposed model shows child-like U-shaped learning curves clearly for certain verbs, but the preferences for types of overgeneralization did not fully match the observations in children. |