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{
    "pk": 50032,
    "title": "Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction",
    "subtitle": null,
    "abstract": "Child-directed speech (CDS) is characterized by its adaptive nature: Caregivers not only talk to children, but engage in dy- namic interactions with them. The adaptive/interactive nature of this type of language is understudied in computational mod- eling research, particularly given the limited availability of nat- uralistic data. While recent advances in large language models (LLMs) have demonstrated potential for generating viable syn- thetic dialogue data in various domains, their ability to capture the dynamics of child-caregiver communication remains un- explored. This paper introduces a systematic framework for evaluating LLMs' capacity to generate developmentally ap- propriate CDS in interaction, examining both static linguistic features and dynamic conversational patterns. We evaluated state-of-the-art LLMs (GPT-4o and Llama 3) against natural interactions from the CHILDES dataset using both single- and multi-turn testing approaches. In single-turn evaluation, mod- els generated responses to individual child utterances, enabling direct comparison with actual caregiver responses. Multi-turn testing assessed sustained interaction capabilities through sim- ulated child-caregiver dialogues. Our results show that while LLMs can successfully approximate surface-level linguistic patterns after few-shot prompting, they struggle with higher- level communicative aspects, with excessive alignment and re- duced diversity compared to natural interactions. Our bench- marking framework elucidates both the potential and limita- tions of LLMs in generating data that preserves the essential properties of child-caregiver language in interactions.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Linguistics; Language acquisition; Natural Language Processing; Pragmatics"
        }
    ],
    "section": "Abstracts with Poster Presentation (accepted as Abstracts)",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/36c9w8qn",
    "frozenauthors": [
        {
            "first_name": "Jing",
            "middle_name": "",
            "last_name": "Liu",
            "name_suffix": "",
            "institution": "ENS",
            "department": ""
        },
        {
            "first_name": "Abdellah",
            "middle_name": "",
            "last_name": "Fourtassi",
            "name_suffix": "",
            "institution": "Aix-Marseille University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2025-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/50032/galley/37994/download/"
        }
    ]
}