| Challenge: | a long-standing debate concerns whether the linguistic input children receive is sufficient to explain the grammatical knowledge they develop. |
| Approach: | They evaluate baby language models trained on child-oriented input from the BabyLM Challenge and two base models trained in 10M and 100M tokens. |
| Outcome: | The proposed models acquire filler-gap dependencies but fail to generalize or fully capture island constraints. |
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Filling in the Mechanisms: How do LMs Learn Filler-Gap Dependencies under Developmental Constraints? (2026.findings-acl)
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| Challenge: | Language models lack language-specific biases, yet still posit some important syntactic generalizations. |
| Approach: | They applied Distributed Alignment Search to checkpoints of a language model from the BabyLM challenge to evaluate whether representations of filler-gap dependencies transfer between wh-questions and topicalization. |
| Outcome: | The results suggest shared, yet item-sensitive mechanisms may develop with limited training data. |
BabyLM’s First Constructions: Causal interventions provide a signal of learning (2025.emnlp-main)
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| Challenge: | Recent work shows sensitivity to constructions in pretrained language models, but their relevance to human language learning is doubted. |
| Approach: | They use construction grammars to demonstrate sensitivity to constructions in pretrained language models. |
| Outcome: | The proposed models learn diverse constructions even hard cases that are superficially indistinguishable. |
Do BabyLMs Wanna Learn Wanna Contraction? On the Learnability without Language-Specific Bias (2026.findings-acl)
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| Challenge: | grammatical constraints on wanna contraction can be learned by artificial learners under cognitively plausible learning conditions. |
| Approach: | They evaluate 24 BabyLMs from 2024 BabyLM Challenge and four standard models . they find that only encoder-based BabyLM models can capture wanna contraction . |
| Outcome: | The proposed models show modest but meaningful sensitivity on large datasets and high-frequency wanna instances. |
Fine-Grained Analysis of Shared Syntactic Mechanisms in Language Models (2026.acl-long)
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| Challenge: | Recent advances in language models have demonstrated their ability to process linguistic expressions with complex syntactic structures. |
| Approach: | They investigate whether language models employ shared neural mechanisms across different constructions by applying causal interpretability methods at a granular level. |
| Outcome: | The proposed model performance improves on acceptability judgment benchmarks. |
Measuring and Narrowing the Compositionality Gap in Language Models (2023.findings-emnlp)
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| Challenge: | a language model can correctly answer all sub-problems but not generate the overall solution. |
| Approach: | They propose a method that asks itself and then answers follow-up questions to narrow the compositionality gap by reasoning explicitly instead of implicitly. |
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Is Child-Directed Speech Effective Training Data for Language Models? (2024.emnlp-main)
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| Challenge: | High-performing language models are typically trained on hundreds of billions of words, but human learners use language fluently after far less training data. |
| Approach: | They train GPT-2 and RoBERTa models on 29M words of English child-directed speech and a new matched, synthetic dataset. |
| Outcome: | The proposed models show that child language input is not valuable for training language models. |
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better (P18-1)
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| Challenge: | a recent study found that language models fail to learn long-range syntax sensitive dependencies. |
| Approach: | They propose to use a subject-verb agreement diagnostic to determine whether language models can learn long-range syntax sensitive dependencies. |
| Outcome: | The proposed model outperforms left-corner and bottom-up variants in learning non-local dependencies. |
Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)
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Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma
| Challenge: | Recent studies have looked into the ability of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc. However, few studies investigate the controllability of large languages. |
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| Outcome: | The proposed model can meet hard constraints and perform better than state-of-the-art models. |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
Word Acquisition in Neural Language Models (2022.tacl-1)
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| Challenge: | Language models acquire individual words during training, based on unigram token frequencies, before transitioning loosely to bigram probabilities, eventually converging on more nuanced predictions. |
| Approach: | They examine how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory. |
| Outcome: | The models follow consistent patterns during training for both unidirectional and bidirectional models, and for both LSTM and Transformer architectures. |