Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910133495267328 |
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| author | Trusca, Maria Mihaela Allein, Liesbeth |
| author_facet | Trusca, Maria Mihaela Allein, Liesbeth |
| contents | Interpretations of a single sentence can vary, particularly when its context is lost. This paper aims to simulate how readers perceive content with varying toxicity levels by generating diverse interpretations of out-of-context sentences. By modeling toxicity, we can anticipate misunderstandings and reveal hidden toxic meanings. Our proposed decoding strategy explicitly controls toxicity in the set of generated interpretations by (i) aligning interpretation toxicity with the input, (ii) relaxing toxicity constraints for more toxic input sentences, and (iii) promoting diversity in toxicity levels within the set of generated interpretations. Experimental results show that our method improves alignment with human-written interpretations in both syntax and semantics while reducing model prediction uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding Trusca, Maria Mihaela Allein, Liesbeth Computation and Language Interpretations of a single sentence can vary, particularly when its context is lost. This paper aims to simulate how readers perceive content with varying toxicity levels by generating diverse interpretations of out-of-context sentences. By modeling toxicity, we can anticipate misunderstandings and reveal hidden toxic meanings. Our proposed decoding strategy explicitly controls toxicity in the set of generated interpretations by (i) aligning interpretation toxicity with the input, (ii) relaxing toxicity constraints for more toxic input sentences, and (iii) promoting diversity in toxicity levels within the set of generated interpretations. Experimental results show that our method improves alignment with human-written interpretations in both syntax and semantics while reducing model prediction uncertainty. |
| title | Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.08159 |