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New corpus aids LLM-based simplification of scientific papers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new workflow to simplify complex scientific papers for broader audiences using large language models. This human-in-the-loop process involves generating initial simplifications with GPT-4o-mini and then refining them based on feedback from non-specialist readers and expert editors. The resulting corpus, derived from the SciSummNet dataset, includes human judgments and evaluation results, aiming to improve cross-disciplinary scientific communication. AI

IMPACT This resource could improve accessibility of scientific research, enabling broader understanding and collaboration across disciplines.

RANK_REASON The cluster contains an academic paper detailing a new corpus and methodology for LLM-based simplification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New corpus aids LLM-based simplification of scientific papers

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The cluster contains an academic paper detailing a new corpus and methodology for LLM-based simplification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kyuri Im, Michael F\"arber ·

    A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

    arXiv:2607.25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language model (LLM)-based simplification of scientific texts and present a human-in-the-lo…

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