Can LLM-Generated Misinformation Be Detected?
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929325870153728 |
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| author | Chen, Canyu Shu, Kai |
| author_facet | Chen, Canyu Shu, Kai |
| contents | The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental research question is: will LLM-generated misinformation cause more harm than human-written misinformation? We propose to tackle this question from the perspective of detection difficulty. We first build a taxonomy of LLM-generated misinformation. Then we categorize and validate the potential real-world methods for generating misinformation with LLMs. Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm. We also discuss the implications of our discovery on combating misinformation in the age of LLMs and the countermeasures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13788 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Can LLM-Generated Misinformation Be Detected? Chen, Canyu Shu, Kai Computation and Language Artificial Intelligence Cryptography and Security Human-Computer Interaction Machine Learning The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental research question is: will LLM-generated misinformation cause more harm than human-written misinformation? We propose to tackle this question from the perspective of detection difficulty. We first build a taxonomy of LLM-generated misinformation. Then we categorize and validate the potential real-world methods for generating misinformation with LLMs. Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm. We also discuss the implications of our discovery on combating misinformation in the age of LLMs and the countermeasures. |
| title | Can LLM-Generated Misinformation Be Detected? |
| topic | Computation and Language Artificial Intelligence Cryptography and Security Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2309.13788 |