Language Development and Disorders

Latest papers

• A retrospective analysis on the use of LLMs to study infant syntax learning

  H\'elie Bazin (SCAI, SND, ISIR), Anouk Barberousse (SND), Fran\c{c}ois Yvon (MLIA) · 23 September 2026

  Large language models (LLMs) have increasingly been used to investigate how children acquire syntax at an early stage of development. This is notably the central scientific goal of the BabyLM challenge, a community-wide effort to develop models that achieve human-level syntactic performance while be…

• Lost but not erased: Finding traces of a forgotten language in neural speech models

  Peter Plantinga, Charlotte Moore, Peter W. Donhauser, Krista Byers-Heinlein, Denise Klein · 27 August 2026

  International adoptees retain phonological traces of a birth language they can no longer speak or comprehend, a persistence typically attributed to a biologically-timed critical period. We asked whether it could instead reflect the ordinary dynamics of learning, using automatic speech recognition mo…

• From Exposure to Expectation: Frequency, Surprisal, and Language Across Development in Spanish

  Francisco Portillo L\'opez · 25 August 2026

  Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it contribute as much to explaining when children acquire individual words? Frequency reflects a learner's cumulative exposure to a word, whereas surpri…

• Children, but not language models, show accelerating returns in word learning

  Michael C. Frank · 19 August 2026

  Children learn hundreds of words over the first years of their lives, in a process that begins slowly but quickly picks up speed. Prior models describe vocabulary growth as evidence accumulation over time. Here we show that the process is best characterized as accelerating accumulation: children lea…

• Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models

  Fan Zhou, Weitian Wang, Tim Van de Cruys · 11 August 2026

  Classifier-free guidance (CFG) is usually kept on throughout masked diffusion language model decoding, although its benefit varies across prompts and over time. We study when CFG is actually needed by comparing, from any partial output, the probability of eventual constraint satisfaction under conti…

• The Calibration Floor: Format Repair Can Masquerade as Self-Correction at Small-to-Mid Scale

  Mingguang Chen, Bo Qu, Licheng Wang · 6 August 2026

  Accuracy changes after language-model self-revision are usually interpreted as changes in reasoning. We show this can fail at the answer-extraction boundary, and test the failure causally rather than only observationally. Across Qwen3.5 (0.8B-9B), Gemma-4-12B, and two frontier models via API (Tencen…

• The Learning Objective Governs Perceptual Narrowing: A Cross-Lingual, Layer-Wise, Ten-Seed Study of Self-Supervised Speech Encoders

  Sejin Yoo · 4 August 2026

  Perceptual narrowing---the developmental loss of non-native phoneme discrimination in the first year of life \citep{werker1984}---is a canonical developmental finding, yet \emph{what learning objective produces it} remains open. We train a \(\sim\)7\,M-parameter Transformer encoder on child-directed…

• Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes

  Brian K Chen · 21 July 2026

  To test how correct logical judgments respond to learned context, we prepend a soft prefix to an exactly labeled syllogistic reasoning benchmark while keeping the model fixed. Soft prefixes are opaque continuous vectors, so we characterize them through the behavior they induce across controlled vari…

• RL Post-Training Builds Compositional Reasoning Strategies

  Azwar Abdulsalam, Nishil Patel, Andrew Saxe · 9 July 2026

  Does RL post-training merely amplify primitive skills already latent in a base model, or can it compose primitive skills into new higher-level strategies? We study this question in a fully observable rewrite-grammar environment where the pretraining distribution is known and every generated rewrite …

• Early Language Learning via Spreading Activation and Category Exploration in Complex Networks

  Salvatore Citraro · 8 July 2026

  Is word acquisition in children uneven with respect to semantic and lexical categories? To answer this question, we model early language learning as a search on a graph-based mental lexicon, driven by two interacting processes: spreading activation and an enforced exploration (rather than exploitati…

• Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities

  Kaveri K. Sheth, Lawrence Borst, Tarek Kunze, Marvin Lavechin, Okko R\"as\"anen, Sho Tsuji, Loann Peurey, Alix Bourr\'ee, Alejandrina Cristia · 7 July 2026

  Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use. First, LFR corpora are collected across sites with heterogeneous formats and consent structures, making cross-corpus use non-trivial. Second…

• Developmental approach reveals the statistical learning of Neural Language Models: Transformers generalize from the most abstract statistical patterns

  Wang Bojun, Holly Jenkins, Elizabeth Wonnacott · 29 June 2026

  In this study, we use a developmental approach to investigate the statistical learning and mental representation of neural language models (NLM). A series of Generative Transformer models are trained on a synthetic grammar. The model states are saved at multiple stages in the course of training. Thr…

• When to Plan, When to Polish: Noise Level as a Granularity Axis for Diffusion Language Models

  Peihong Li, Yuanjie Shi, Yan Yan · 23 June 2026

  Standard tokenwise diffusion LMs keep training corruption and inference commitment at token granularity throughout denoising. At high noise, this leaves scattered local fragments rather than coherent evidence, making it hard to form early coarse structure, exactly what planning-sensitive generation …

• Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation

  Uwe Konig, Hamza Kazmi, Ruizhe Li, Maheep Chaudhary · 11 June 2026

  Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude…

• Child-directed speech facilitates production, not comprehension, in BabyLMs

  Bastian Bunzeck, Sina Zarrieß · 2 June 2026

  Recent studies suggest that child-directed speech is not conducive to language learning in BabyLMs. However, current evaluations focus predominantly on comprehension and not production, which is central to usage-based theories of language acquisition which argue how CDS facilitates early language us…

• Measuring Form and Function in Language Models

  H\'ector Javier V\'azquez Mart\'inez, Charles Yang · 28 May 2026

  We introduce quantitative metrics for child language acquisition to evaluate language models. Our focus is on the formal syntactic and functional discourse properties of determiners in English, which young children acquire early and accurately. We propose Contextual Alternative Choice (CAC), a new p…

• Probing LLMs for Syntactic Structure Beyond Universal Dependencies: A Minimalist Phase Account in English

  Yuanhao Chen, Peter Chin · 27 May 2026

  We show that LLMs encode syntactic distinctions not present in the Universal Dependencies (UD) tree distances that structural probes are trained to recover. On English wh-movement stimuli, we measure the probe distance between an embedded subject and its verb, whose UD tree distance is invariant acr…

• Do Language Models Know What Not to Say? Causal Evidence for Statistical Preemption in LLMs

  Dongxin Guo, Jikun Wu, Siu Ming Yiu · 25 May 2026

  How do learners acquire knowledge of what is unacceptable without negative evidence? Construction Grammar proposes statistical preemption: exposure to a conventional form (e.g., "donated the books to the library") preempts structurally possible but unattested alternatives ("*donated the library the …

• Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks

  Claire Hobbs, R. Thomas McCoy · 22 May 2026

  In what ways might statistical signals in linguistic input assist with the acquisition of syntax? Here we hypothesize a mechanism called collocational bootstrapping, in which regularities in word co-occurrence patterns can provide cues to syntactic dependencies. We investigate whether this mechanism…

• CAIT: A Syntactic Parsing Toolkit for Child-Adult InTeractions

  Francesca Padovani, Xiulin Yang, Bastian Bunzeck, Jaap Jumelet, Yevgen Matusevych, Nathan Schneider, Arianna Bisazza · 20 May 2026

  CHILDES is a paramount resource for language acquisition studies -- yet computational tools for analyzing its syntactic structure remain limited. Leveraging the recent release of the UD-English-CHILDES treebank with gold-standard Universal Dependencies (UD) annotations, we train a state-of-the-art d…

• LLMs for automatic annotation of Mandarin narrative transcripts

  Qingwen Zhao, Hongao Zhu, Yunqi He, Rui Wang, Aijun Huang, Hai Hu · 19 May 2026

  Linguistic annotation of transcribed speech is essential for research in language acquisition, language disorders, and sociolinguistics, yet remains labor-intensive and time-consuming. While Large Language Models (LLMs) have shown promise in automating annotation tasks, their ability to handle compl…

• A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research

  Divyesh Pratap Singh, Dakshesh Gusain, Federica Bulgarelli, Alison Eisel Hendricks, John Beavers, Nathan M. Beers, Ifeoma Nwogu · 19 May 2026

  Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinction for studying early verb learning. However, this distinction remains difficult to measure at scale because large annotated resources for manner an…

• Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space

  Kelvin Kan, Xingjian Li, Benjamin J. Zhang, Tuhin Sahai, Stanley Osher, Markos A. Katsoulakis · 19 May 2026

  Discrete diffusion has become a leading framework for generative modeling in various applications including language, vision, and biology. Existing convergence theory, however, exhibits fundamental limitations. KL-based analyses diverge under singular priors such as the masked distribution, while bo…

• Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition

  Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, Arianna Bisazza · 13 May 2026

  Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate this question using neural language models trained on CDL versus adult-directed language (ADL). We selectively remove syntactic or lexical co-occurr…

• A Computational Operationalisation of Competing Maturational Theories of Syntactic Development via Statistical Grammar Induction

  Mila Marcheva, Suchir Salhan, Weiwei Sun · 12 May 2026

  This paper is concerned with what intermediate syntactic categories children acquire during first language development, and in what order. Maturational theories make different predictions. Bottom-up accounts (GROWING) propose that lexical and inflectional structure emerges first, while inward accoun…