Disinformation Capabilities of Large Language Models
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913889642348544 |
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| author | Vykopal, Ivan Pikuliak, Matúš Srba, Ivan Moro, Robert Macko, Dominik Bielikova, Maria |
| author_facet | Vykopal, Ivan Pikuliak, Matúš Srba, Ivan Moro, Robert Macko, Dominik Bielikova, Maria |
| contents | Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for societies around the world. This paper presents a comprehensive study of the disinformation capabilities of the current generation of LLMs to generate false news articles in the English language. In our study, we evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We evaluated several aspects of the LLMs: how good they are at generating news articles, how strongly they tend to agree or disagree with the disinformation narratives, how often they generate safety warnings, etc. We also evaluated the abilities of detection models to detect these articles as LLM-generated. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08838 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Disinformation Capabilities of Large Language Models Vykopal, Ivan Pikuliak, Matúš Srba, Ivan Moro, Robert Macko, Dominik Bielikova, Maria Computation and Language Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for societies around the world. This paper presents a comprehensive study of the disinformation capabilities of the current generation of LLMs to generate false news articles in the English language. In our study, we evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We evaluated several aspects of the LLMs: how good they are at generating news articles, how strongly they tend to agree or disagree with the disinformation narratives, how often they generate safety warnings, etc. We also evaluated the abilities of detection models to detect these articles as LLM-generated. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives. |
| title | Disinformation Capabilities of Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.08838 |