用大模型简化复杂文本，显著提升阅读理解与认知轻松度。
LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load
- 基于自修正机制的LLM实现低损失文本简化。
- 简化文本使用户答题正确率平均提升3.9%，医学类最高达14.6%。
- 简化后用户感知任务更轻松，适合想获取专业信息的普通读者。
网络上的科学论文和维基内容常超出用户阅读水平。为解决此问题，我们采用自修正方法开发了大模型驱动的最小损失文本简化能力。通过涵盖6个领域的31篇文本、4563名参与者的随机对照实验验证该方法：参与者被随机分配阅读原文或简化版文本，并回答多选题以测试理解程度。结果显示，阅读简化文本的用户正确率平均高出3.9%（p<0.05），其中医学领域提升最显著（14.6%），金融、航空航天/计算机科学、法律领域分别提升5.5%、3.8%、3.5%。即使无法回看文本，简化版仍保持约4%的准确率优势。此外，使用简化文本的用户在简化版NASA任务负荷量表上主观感受更轻松（5点量表上提升0.33，p<0.05）。本研究是迄今规模最大的相关实证，证明大模型可有效提升复杂信息的可读性与可及性。
原文摘要 · Abstract (English)
Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.
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