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LLMs applied to scientific literature discovery surveyed in new arXiv chapter

By PulseAugur Editorial · [2 sources] ·

A new chapter published on arXiv explores the application of generative large language models (LLMs) for scientific knowledge discovery. The paper reviews 34 peer-reviewed studies that utilize LLMs for literature retrieval and screening of research papers against specific criteria. It categorizes these studies based on the LLMs used, their access and adaptation methods, prompting techniques, and evaluation metrics. AI

IMPACT This research provides a structured overview of how LLMs are being applied to enhance scientific literature discovery, potentially improving research efficiency.

RANK_REASON The cluster contains a published academic paper on arXiv detailing research into LLM applications.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs applied to scientific literature discovery surveyed in new arXiv chapter

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The cluster contains a published academic paper on arXiv detailing research into LLM applications.
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74 days old
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Eleni Adamidi, Serafeim Chatzopoulos, Thanasis Vergoulis ·

    Scientific Knowledge Discovery in the Age of Large Language Models

    arXiv:2607.26670v1 Announce Type: cross Abstract: The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Gener…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Thanasis Vergoulis ·

    Scientific Knowledge Discovery in the Age of Large Language Models

    The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more fl…

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