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{
    "pk": 21677,
    "title": "A systematic investigation of learnability from single child linguistic input",
    "subtitle": null,
    "abstract": "Language models (LMs) have demonstrated remarkable profi-\nciency in generating linguistically coherent text, sparking dis-\ncussions about their relevance to understanding human lan-\nguage learnability. However, a significant gap exists between\nthe training data for these models and the linguistic input a\nchild receives. LMs are typically trained on data that is or-\nders of magnitude larger and fundamentally different from\nchild-directed speech (Warstadt & Bowman, 2022; Warstadt\net al., 2023; Frank, 2023a). Addressing this discrepancy,\nour research focuses on training LMs on subsets of a sin-\ngle child's linguistic input. Previously, Wang, Vong, Kim,\nand Lake (2023) found that LMs trained in this setting can\nform syntactic and semantic word clusters and develop sen-\nsitivity to certain linguistic phenomena, but they only consid-\nered LSTMs and simpler neural networks trained from just one\nsingle-child dataset. Here, to examine the robustness of learn-\nability from single-child input, we systematically train six dif-\nferent model architectures on five datasets (3 single-child and\n2 baselines). We find that the models trained on single-child\ndatasets showed consistent results that matched with previous\nwork, underscoring the robustness of forming meaningful syn-\ntactic and semantic representations from a subset of a child's\nlinguistic input.\nKeywords: learnability; single-child; distributional learning;\nrobustness; language models",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Linguistics; Psychology; Concepts and categories; Language development; Language learning; Natural Language Processing; Computational Modeling"
        }
    ],
    "section": "Papers with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/9986685c",
    "frozenauthors": [
        {
            "first_name": "Yulu",
            "middle_name": "",
            "last_name": "Qin",
            "name_suffix": "",
            "institution": "New York University",
            "department": ""
        },
        {
            "first_name": "Wentao",
            "middle_name": "",
            "last_name": "Wang",
            "name_suffix": "",
            "institution": "New York University",
            "department": ""
        },
        {
            "first_name": "Brenden",
            "middle_name": "",
            "last_name": "Lake",
            "name_suffix": "",
            "institution": "NYU",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2024-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
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            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/21677/galley/11276/download/"
        },
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/21677/galley/22070/download/"
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}