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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.23319 |
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| _version_ | 1866912511969722368 |
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| author | Ferrara, Alfio Picascia, Sergio Pinnavaia, Laura Ranitovic, Vojimir Rocchetti, Elisabetta Tuveri, Alice |
| author_facet | Ferrara, Alfio Picascia, Sergio Pinnavaia, Laura Ranitovic, Vojimir Rocchetti, Elisabetta Tuveri, Alice |
| contents | Proprietary Large Language Models (LLMs) have shown tendencies toward politeness, formality, and implicit content moderation. While previous research has primarily focused on explicitly training models to moderate and detoxify sensitive content, there has been limited exploration of whether LLMs implicitly sanitize language without explicit instructions. This study empirically analyzes the implicit moderation behavior of GPT-4o-mini when paraphrasing sensitive content and evaluates the extent of sensitivity shifts. Our experiments indicate that GPT-4o-mini systematically moderates content toward less sensitive classes, with substantial reductions in derogatory and taboo language. Also, we evaluate the zero-shot capabilities of LLMs in classifying sentence sensitivity, comparing their performances against traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What's Taboo for You? - An Empirical Evaluation of LLMs Behavior Toward Sensitive Content Ferrara, Alfio Picascia, Sergio Pinnavaia, Laura Ranitovic, Vojimir Rocchetti, Elisabetta Tuveri, Alice Computation and Language Proprietary Large Language Models (LLMs) have shown tendencies toward politeness, formality, and implicit content moderation. While previous research has primarily focused on explicitly training models to moderate and detoxify sensitive content, there has been limited exploration of whether LLMs implicitly sanitize language without explicit instructions. This study empirically analyzes the implicit moderation behavior of GPT-4o-mini when paraphrasing sensitive content and evaluates the extent of sensitivity shifts. Our experiments indicate that GPT-4o-mini systematically moderates content toward less sensitive classes, with substantial reductions in derogatory and taboo language. Also, we evaluate the zero-shot capabilities of LLMs in classifying sentence sensitivity, comparing their performances against traditional methods. |
| title | What's Taboo for You? - An Empirical Evaluation of LLMs Behavior Toward Sensitive Content |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.23319 |