Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models
It addresses concerns about misuse and unintended consequences of LLMs in generating persuasive or deceptive outputs, which is an incremental review of existing research.
This review examines the capacity of Large Language Models (LLMs) to generate persuasive and deceptive content, synthesizing empirical work and analyzing theoretical risks, with current effects being relatively small but potential for increased impact through mechanisms like fine-tuning.
Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these systems become more widely deployed. This review synthesizes recent empirical work examining LLMs' capacity and proclivity for persuasion and deception, analyzes theoretical risks that could arise from these capabilities, and evaluates proposed mitigations. While current persuasive effects are relatively small, various mechanisms could increase their impact, including fine-tuning, multimodality, and social factors. We outline key open questions for future research, including how persuasive AI systems might become, whether truth enjoys an inherent advantage over falsehoods, and how effective different mitigation strategies may be in practice.