接受修改后：人工智能辅助科学写作的价值
Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing
AI总结：
研究探讨了AI辅助科学写作的有效性，发现AI生成摘要在披露来源信息后可达到与人工摘要相当的可接受性，且作者编辑行为受对AI作者身份的感知驱动。
摘要：
大型语言模型在各个领域中的应用正在不断扩大，然而它们作为辅助科学写作工具的有效性仍不够清楚。科学写作是一项需要精确性、多模态合成和领域专业知识的 endeavor。我们探讨了LLM支持领域专家进行科学写作的潜力，重点关注摘要的撰写。我们设计了一个激励性的随机对照试验，假设有会议设置，参与者相关专业知识被分成作者和审稿人池。受行为科学方法的启发，我们的新型激励结构鼓励作者编辑提供的摘要，以达到可接受的质量用于同行评审提交。我们的2x2组内设计扩展到两个维度：提供的摘要的隐含来源和其披露。我们发现，作者在编辑人工撰写摘要时比AI生成的摘要进行最多编辑，尤其是在没有来源归因的情况下，通常由AI生成的可读性更高指导。在披露来源信息后，两种来源的编辑量趋于一致。审稿人的决定不受摘要来源的影响，但与所做的编辑数量有显著相关性。在存在来源信息的情况下，仔细的风格编辑，尤其是在AI生成摘要的情况下，提高了被接受的可能性。我们发现，AI生成的摘要有潜力达到与人工撰写摘要相当的可接受性，只需最小的修改，并且对AI作者身份的感知，而不是客观质量，驱动了大部分观察到的编辑行为。我们的发现强调了在协作科学写作中披露来源的重要性。
英文摘要：
Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing - an endeavor requiring precision, multimodal synthesis, and domain expertise - remains insufficiently understood. We examine the potential of LLMs to support domain experts in scientific writing, with a focus on abstract composition. We design an incentivized randomized controlled trial with a hypothetical conference setup where participants with relevant expertise are split into an author and reviewer pool. Inspired by methods in behavioral science, our novel incentive structure encourages authors to edit the provided abstracts to an acceptable quality for a peer-reviewed submission. Our 2 x 2 between-subject design expands into two dimensions: the implicit source of the provided abstract and the disclosure of it. We find authors make most edits when editing human-written abstracts compared to AI-generated abstracts without source attribution, often guided by higher perceived readability in AI generation. Upon disclosure of source information, the volume of edits converges in both source treatments. Reviewer decisions remain unaffected by the source of the abstract, but bear a significant correlation with the number of edits made. Careful stylistic edits, especially in the case of AI-generated abstracts, in the presence of source information, improve the chance of acceptance. We find that AI-generated abstracts hold potential to reach comparable levels of acceptability to human-written ones with minimal revision, and that perceptions of AI authorship, rather than objective quality, drive much of the observed editing behavior. Our findings reverberate the significance of source disclosure in collaborative scientific writing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
