大模型能否识别跨文化阅读中的理解障碍？
Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?
- 分析57篇跨文化书评，发现83%含文化特异性难懂内容
- GPT-4o在识别文化障碍上表现参差，仍有提升空间
- 适合研究跨文化沟通与AI可解释性的学者参考
在全球化与数字化加速的背景下，来自不同文化的读者在阅读书籍、产品评论等文本时，常因文化特定元素而产生理解障碍。本文通过针对Goodreads平台57篇书评的用户研究发现，其中83%的评论包含至少一个对异文化读者而言难以理解的文化特异性内容。我们进一步评估了GPT-4o在结合读者文化背景的前提下识别这些障碍的能力，结果呈现混合表现，表明当前模型在跨文化理解任务中仍存在显著改进空间。相关数据集已公开于GitHub。
原文摘要 · Abstract (English)
In a rapidly globalizing and digital world, content such as book and product reviews created by people from diverse cultures are read and consumed by others from different corners of the world. In this paper, we investigate the extent and patterns of gaps in understandability of book reviews due to the presence of culturally-specific items and elements that might be alien to users from another culture. Our user-study on 57 book reviews from Goodreads reveal that 83\% of the reviews had at least one culture-specific difficult-to-understand element. We also evaluate the efficacy of GPT-4o in identifying such items, given the cultural background of the reader; the results are mixed, implying a significant scope for improvement. Our datasets are available here: https://github.com/sougata-ub/reading_between_lines
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。