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{
    "pk": 24378,
    "title": "Improving the Readability of Scientific Concept Analogies with Cognitive Conflict Reinforcement Learning",
    "subtitle": null,
    "abstract": "Large language models are increasingly being used for education and science communication by automatically generating explanations of scientific concepts. However, prior research has found that the analogies produced by LLMs lack human-like psycholinguistic properties important for readability. In this work, we propose cognitive conflict reinforcement learning (CCRL) to improve the psycholinguistic properties of analogies generated by LLMs. Specifically, we create cognitive conflict between the original LLM and a cloned LLM during reinforcement learning. This helps address the cognitive rigidity problem in LLMs. Experimental results demonstrate that our approach significantly outperforms existing RL algorithms and human performance in improving various readability metrics of generated analogies.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Analogy; Cognitive architectures; Natural Language Processing; Large Language Models"
        }
    ],
    "section": "Papers with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/0mz3n583",
    "frozenauthors": [
        {
            "first_name": "Yuang",
            "middle_name": "",
            "last_name": "Cai",
            "name_suffix": "",
            "institution": "Beijing University of Posts and Telecommunications",
            "department": ""
        },
        {
            "first_name": "Yuyu",
            "middle_name": "",
            "last_name": "Yuan",
            "name_suffix": "",
            "institution": "Beijing University of Posts and Telecommunications",
            "department": ""
        },
        {
            "first_name": "Jinsheng",
            "middle_name": "",
            "last_name": "Shi",
            "name_suffix": "",
            "institution": "Beijing University of Posts and Telecommunications",
            "department": ""
        },
        {
            "first_name": "Rui",
            "middle_name": "",
            "last_name": "Han",
            "name_suffix": "",
            "institution": "Beijing University of Posts and Telecommunications",
            "department": ""
        },
        {
            "first_name": "Zhenyu",
            "middle_name": "",
            "last_name": "Zhao",
            "name_suffix": "",
            "institution": "Beijing University of Post and Telecommunications",
            "department": ""
        },
        {
            "first_name": "Zijie",
            "middle_name": "",
            "last_name": "Shi",
            "name_suffix": "",
            "institution": "Beijing University of Posts and Telecommunications",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2024-01-01T23:30:00+05:30",
    "render_galley": null,
    "galleys": [
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            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/24378/galley/13975/download/"
        },
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/24378/galley/21261/download/"
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}