关于使用大语言模型研究婴儿句法学习的回顾性分析

Computer Science > Computation and Language arXiv:2609.26539 (cs) [Submitted on 22 Sep 2026] Title:A retrospective analysis on the use of LLMs to study infant syntax learning Authors:Hélie Bazin (SCAI, SND, ISIR), Anouk Barberousse (SND), François Yvon (MLIA) View a PDF of the paper titled A retrospective analysis on the use of LLMs to study infant syntax learning, by H\'elie Bazin (SCAI and 4 other authors View PDF Abstract: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 being trained on developmentally realistic corpora. In this paper, we reflect on the use of LLMs in the study of infant syntax learning by providing an epistemological assessment of several studies from this research program. We discuss how datasets are built, which models are implemented, how they are trained and syntactically evaluated. We observe significant assumptions in the methodology of BabyLM and related studies, thus mitigating their theoretical scope. We additionally observe that using developmentally-realistic corpora have limited effects on models performance on commonly-used benchmarks, which suggest important computational differences between LLMs and the infant syntax learner. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.26539 [cs.CL] (or arXiv:2609.26539v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.26539 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: EMNLP 2026 Main Conference, ACL SIGDAT, Oct 2026, Budapest (Hungary), Hungary Submission history From: Helie Bazin [view email] [via CCSD proxy] [v1] Tue, 22 Sep 2026 14:58:07 UTC (232 KB) Full-text links: Access Paper: View a PDF of the paper titled A retrospective analysis on the use of LLMs to study infant syntax learning, by H\'elie Bazin (SCAI and 4 other authors View PDF TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)