Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
arXiv:2601.05751v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade. Prior work has shown that LLMs can successfully persuade humans and amplify persuasive language. It is therefore essential to understand how user instructions affect the generation of persuasive language, and to understand whether the generated persuasive language differs, for example, when targeting different groups. In this work, we propose a framework for evaluating how per
The increasing use of LLMs in everyday communication tasks, particularly for persuasion, necessitates immediate understanding of their biases and ethical implications as their capabilities expand.
This research reveals how LLMs might perpetuate or even amplify societal biases through their persuasive language, indicating a critical need for ethical AI development and deployment strategies.
Our understanding of LLM output is now more nuanced, highlighting that even 'neutral' instructions can lead to biased persuasive content, affecting how these models are designed, trained, and regulated.
- · AI ethicists
- · Social scientists
- · Responsible AI developers
- · Users aware of AI biases
- · Unregulated LLM deployment
- · AI systems perpetuating bias
- · Uninformed public
- · AI developers ignoring social implications
LLMs are found to generate persuasive language exhibiting stereotypical gender patterns based on user instructions.
This discovery prompts stricter guidelines and auditing for AI models, especially those used in public-facing or influential applications.
Increased public scrutiny and potential regulatory action could lead to a 'bias-aware' certification for AI, impacting market adoption and trust.
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Read at arXiv cs.AI