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

Published: 07 Oct 2023, Last Modified: 01 Dec 2023EMNLP 2023 MainEveryoneRevisionsBibTeX
Submission Type: Regular Long Paper
Submission Track: Theme Track: Large Language Models and the Future of NLP
Submission Track 2: NLP Applications
Keywords: LLM, Prompt Engineering, Disinformation Detection, Natural Language Inference, Semantic Reasoning, In-context Learning
TL;DR: Using LLMs to detect misinformation.
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.
Submission Number: 591
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