Truth Amidst the Noise: Tackling Misinformation with LLMs
Dive into the labyrinth of misinformation sources, from hallucination to intentional manipulation, and explore solutions.
Sources and Impact of Misinformation
The common sources of misinformation in Large Language Models (LLMs) include their ability to replicate proper signifiers of knowledge even when they are wrong, the plausibility of their responses, and their capacity to create coherent and logical misinformation that can be harder to detect compared to human-written misinformation, potentially leading to more harm (Sun et al., Chen et al.).
Some examples of misinformation spread by LLMs in the past include fake news, rumors, conspiracy theories, clickbait, misleading claims, and cherry-picking across various domains such as healthcare, science, politics, finance, law, education, social media, and the environment. The sources of misinformation range from hallucination and arbitrary generation to controllable generation, both unintentional and intentional scenarios (Analytics Vidhya).
Adversarial attacks on LLMs can lead to the spread of misinformation through techniques like input perturbations, manipulation of context and prompting, and word substitutions, which can alter the outputs of LLMs to generate false information, conspiracy…
