Generalization of structured debate findings to open online platforms

Determine the extent to which the persuasive effects observed in this study—specifically, the increased odds of higher post-debate agreement when participants debate GPT-4 with and without personalization in a structured, time-constrained opening–rebuttal–conclusion format—generalize to unstructured, spontaneous discussions on social networks and other open online platforms.

Background

The study measures persuasive effects by comparing pre- and post-debate agreement scores in controlled, structured, time-limited debates against human or GPT-4 opponents, with and without access to anonymized personal information for personalization. While this design supports causal inference, the authors note that the debate format may diverge from typical, spontaneous online interactions.

Because real-world social media conversations lack fixed stages, time limits, and role assignments, the authors explicitly acknowledge uncertainty regarding whether the observed effects—particularly GPT-4’s superior performance with personalization—would carry over to open, less structured online environments.

References

Therefore, it is not entirely clear how our results would generalize to discussions on social networks and other open online platforms.

— On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial  (2403.14380 - Salvi et al., 2024) in Section Discussion (Limitations)

Existing work shows that LLM-generated arguments can shift human opinions and that LLMs can be persuasive in multi-turn debates with humans~\citep{durmus2024persuasiveness,salvi2025gpt4}, but it is unclear how these findings transfer to the judging domains and pressure protocols of our study.

— Jagged Judges: Epistemic Stability Under Silence, Pressure, and Persistence  (2608.12645 - Zhao et al., 12 Aug 2026) in Section 5, Limitations, paragraph “No human baseline” (also repeated in Appendix, Section “Limitations (Full Discussion)”)