The Effects of Demographic Instructions on LLM Personas
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910950561415168 |
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| author | de Paula, Angel Felipe Magnossão Culpepper, J. Shane Moffat, Alistair Cherumanal, Sachin Pathiyan Scholer, Falk Trippas, Johanne |
| author_facet | de Paula, Angel Felipe Magnossão Culpepper, J. Shane Moffat, Alistair Cherumanal, Sachin Pathiyan Scholer, Falk Trippas, Johanne |
| contents | Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized definitions, but often neglect the subjective nature of sexist language and fail to consider individual users' perspectives. To address this gap, we adopt a perspectivist approach, retaining diverse annotations rather than enforcing gold-standard labels or their aggregations, allowing models to account for personal or group-specific views of sexism. Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Effects of Demographic Instructions on LLM Personas de Paula, Angel Felipe Magnossão Culpepper, J. Shane Moffat, Alistair Cherumanal, Sachin Pathiyan Scholer, Falk Trippas, Johanne Information Retrieval Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized definitions, but often neglect the subjective nature of sexist language and fail to consider individual users' perspectives. To address this gap, we adopt a perspectivist approach, retaining diverse annotations rather than enforcing gold-standard labels or their aggregations, allowing models to account for personal or group-specific views of sexism. Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism. |
| title | The Effects of Demographic Instructions on LLM Personas |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2505.11795 |