Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916622393933824 |
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| author | Saha, Sougata Pandey, Saurabh Kumar Gupta, Harshit Choudhury, Monojit |
| author_facet | Saha, Sougata Pandey, Saurabh Kumar Gupta, Harshit Choudhury, Monojit |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps? Saha, Sougata Pandey, Saurabh Kumar Gupta, Harshit Choudhury, Monojit Computation and Language Artificial Intelligence 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 |
| title | Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.09636 |