Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

• URL: http://arxiv.org/abs/2310.15515v1
• Date: Tue, 24 Oct 2023 04:50:29 GMT
• Title: Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
• Authors: Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee, Shaurya Rohatgi, Dongwon Lee
• Abstract summary: Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused. We propose a novel "Fighting Fire with Fire" (F3) strategy that harnesses modern LLMs' generative and emergent reasoning capabilities. In our experiments, GPT-3.5-turbo consistently achieved accuracy at 68-72%, unlike the decline observed in previous customized and fine-tuned disinformation detectors.
• Score: 7.782551258221384
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (.i.e, generating large-scale harmful and misleading content). To combat this emerging risk of LLMs, we propose a novel "Fighting Fire with Fire" (F3) strategy that harnesses modern LLMs' generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation. First, we leverage GPT-3.5-turbo to synthesize authentic and deceptive LLM-generated content through paraphrase-based and perturbation-based prefix-style prompts, respectively. Second, we apply zero-shot in-context semantic reasoning techniques with cloze-style prompts to discern genuine from deceptive posts and news articles. In our extensive experiments, we observe GPT-3.5-turbo's zero-shot superiority for both in-distribution and out-of-distribution datasets, where GPT-3.5-turbo consistently achieved accuracy at 68-72%, unlike the decline observed in previous customized and fine-tuned disinformation detectors. Our codebase and dataset are available at https://github.com/mickeymst/F3.

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