Stance Drift: How AI-mediated Communication Distorts Our Message
Large language models (LLMs) increasingly mediate human communication, from drafting emails to summarizing scientific reports, yet whether they faithfully preserve a speaker's position remains largely untested. We model AI-mediated communication as a two-step generation-extraction pipeline: one LLM produces an argument from a specified stance, and a second LLM extracts the stance from that argument. We represent the pipeline as a probabilistic state transition over five Likert-type stance categories and define the stance preservation rate (SPR) as the average probability that the extracted stance matches the initial stance. Across 112 debate propositions, none of the nine LLMs tested exce...
| Authors | Lingchong Liu, Yanfei Zhou, Jacob Bien et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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