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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Improving Conversational Literature Retrieval Quality via Personalized Profile-Based Re-ranking.

ShuaiYu Zhang, Huihui Shao, Zhenping Xie

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2026
    PubMed
    Summary

    We developed Conversational Literature Personalized Re-ranking (CLPR) to improve academic literature retrieval in conversations. CLPR enhances search accuracy by personalizing results based on user research history and conversational context.

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    Area of Science:

    • Information Science
    • Computer Science
    • Biomedical Informatics

    Background:

    • Academic literature retrieval faces challenges of information overload and evidence scarcity.
    • Iterative query refinement in multi-turn conversations exacerbates these retrieval difficulties.

    Purpose of the Study:

    • To propose a personalized framework, Conversational Literature Personalized Re-ranking (CLPR), for effective academic literature retrieval in conversational settings.
    • To address the tension between information overload and evidence scarcity in multi-turn academic searches.

    Main Methods:

    • CLPR unifies dense semantic retrieval with personalized user profiling.
    • A broad, high-recall retrieval identifies candidate documents.
    • Conversational history is compressed into a textual profile using a large language model, encoding sequential continuity, immediate focus, and long-term research background.
    • A neural cross-encoder uses the profile as a pseudo-query for final document ranking.

    Main Results:

    • CLPR demonstrated robust generalization on the LitSearch (computer science) benchmark, achieving an NDCG@10 of 0.4793.
    • On the MedCorpus benchmark, CLPR achieved state-of-the-art performance with P@1 = 0.9497 and NDCG@10 = 0.9271.
    • Ablation studies indicated that long-term background cues were most impactful, and maintaining an up-to-date profile improved performance over a static one.

    Conclusions:

    • CLPR delivers accurate and personalized literature retrieval in multi-turn conversational settings.
    • The framework effectively accelerates evidence synthesis across scientific domains by overcoming information retrieval challenges.