Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
This addresses the risk of LLM-generated disinformation for online safety, but it is incremental as it builds on existing LLM capabilities for detection.
The paper tackles the problem of LLMs being misused to generate disinformation by proposing a 'Fighting Fire with Fire' strategy that uses GPT-3.5-turbo to both create and detect deceptive content, achieving 68-72% accuracy in zero-shot detection across datasets.
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.