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"I understand your perspective": LLM Persuasion through the Lens of Communicative Action Theory
Esra Dönmez, Agnieszka Falenska
TL;DR
LLM persuasion is not fully explained by argument quality alone, motivating a study of communicative intent in online opinion change. The paper compares LLM-generated counter-arguments with successful human comments from ChangeMyView, finding denser social signaling, strong trust expression, and greater crowdworker preference for LLM responses. It concludes that LLMs can reproduce nuanced communicative actions that may shape human opinions, while noting important scope and evaluation constraints.
Problem
Research has limited evidence explaining why LLMs persuade humans through established theories of communicative intent and social dimensions.
Method
The study simulates ChangeMyView discussions, generates counter-arguments with three LLMs, and compares their social dimensions and interaction dynamics with human opinion-changing comments.
Results
LLM-generated arguments express illocutionary intent often more frequently and densely than humans, and crowdworkers consistently prefer them over human-written opinion-changing arguments.
Takeaways & Limitations
LLMs’ persuasive power extends beyond argument quality to nuanced communicative actions, including trust expression and adaptation to human social intent.
Takeaways & Limitations
The findings are based on ChangeMyView data and may differ on other online platforms or in real one-to-one conversations with LLMs.
Abstract
from arXiv · showhide
Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored. This work explores the persuasive potential of LLMs through the framework of Jürgen Habermas' Theory of Communicative Action. It examines whether LLMs express illocutionary intent (i.e., pragmatic functions of language such as conveying knowledge, building trust, or signaling similarity) in ways that are comparable to human communication. We simulate online discussions between opinion holders and LLMs using conversations from the persuasive subreddit ChangeMyView. We then compare the likelihood of illocutionary intents in human-written and LLM-generated counter-arguments, specifically those that successfully changed the original poster's view. We find that all three LLMs effectively convey illocutionary intent -- often more so than humans -- potentially increasing their anthropomorphism. Further, LLMs craft sycophantic responses that closely align with the opinion holder's intent, a strategy strongly associated with opinion change. Finally, crowd-sourced workers find LLM-generated counter-arguments more agreeable and consistently prefer them over human-written ones. These findings suggest that LLMs' persuasive power extends beyond merely generating high-quality arguments. On the contrary, training LLMs with human preferences effectively tunes them to mirror human communication patterns, particularly nuanced communicative actions, potentially increasing individuals' susceptibility to their influence.
1 Introduction
The paper asks how LLMs shape online opinion through communicative and social dimensions beyond argument quality. Using ChangeMyView discussions, it compares LLM-generated counter-arguments with human opinion-changing comments and finds stronger social signaling, including trust, alongside high crowdworker preference for LLM responses.
- Motivation: LLM persuasion remains underexplained because existing work has not sufficiently connected it to established theories of communicative intent.The paper frames language as conveying information while also exercising illocutionary force through social dimensions.
- Motivation: Social dimensions such as knowledge, similarity, trust, and conflict can influence opinion change beyond argument quality alone.Prior work found that matching a dimension in the original post, such as responding to conflict with conflict, increases persuasion likelihood.
- Research design: The study simulates ChangeMyView conversations by generating counter-arguments with three LLMs and comparing nine social dimensions against human opinion-changing comments.The analysis also examines whether LLM replies model opinion holders’ communicative intent and whether people prefer them to human-written arguments.
- Findings: LLM-generated texts contain more social dimensions than human counter-arguments and consistently express trust toward the opinion holder.GPT-3.5-turbo sometimes mirrors persuasive human strategies more strongly than opinion-changing human responses.
- Findings: 83% of cases favored LLM-generated arguments as more likely to change the opinion holder’s view.Crowdworkers also found the AI-generated responses more agreeable and overwhelmingly preferred them over human-written opinion-changing comments.
- Contribution: The paper argues that LLM persuasive capability extends beyond producing high-quality arguments to adapting to human discourse and social intent.This highlights an interaction between communicative strategies and persuasion in opinion formation.
2 Related Work
Prior research shows that AI-generated messages can be persuasive, especially when personalized or designed with social dimensions, but the mechanisms behind LLM persuasion remain insufficiently explained. This paper addresses that gap by studying naturally produced social dimensions and directly comparing LLM and human arguments.
- Prior findings: AI-generated messages have been perceived as more persuasive than human messages in some settings, though explicit AI labeling can reduce that effect.Other work found human and LLM policy commentaries equally effective in influencing policy support.
- Personalization: Personalized messages and assigned model personas can significantly alter the persuasiveness of LLM-generated text.These findings connect persuasion to tailoring content to psychological traits and interactional roles.
- Research gap: Research has often measured whether LLMs persuade people, but less often explained why they are persuasive through established theoretical frameworks.The paper identifies this as a gap in the literature on LLM persuasiveness.
- Social dimensions: Synthetic studies found that combinations of knowledge, trust, support, and status cues were rated most persuasive, but natural use in real-world posts remained open.No direct comparison had established how LLM-generated and human-written messages differ in social dimensions and perceived persuasiveness.
3 Data
The study uses the ChangeMyView corpus, where users invite challenges to their opinions and award deltas when comments change their views. It cleans and filters the corpus to sociopolitical discussions, then identifies 7,254 opinion-changing comments.
- Corpus: ChangeMyView is a forum where users state opinions and invite others to challenge them; a delta marks a successful change of view.The reward is issued by the original poster when reconsidering their stance.
- Corpus: The publicly available pre-2016 corpus contains human-written discussions, ensuring its posts and comments were not generated by LLMs.The merged corpus contains 20,626 posts and 1,260,266 comments before cleaning and filtering.
- Cleaning: After removing deleted, incomplete, or extremely short entries, the dataset contains 20,151 posts and 1,193,483 comments.Entries of two words or fewer were excluded as insufficiently meaningful arguments.
- Filtering: Sociopolitical filtering produced the experimental dataset of 13,504 posts and 864,890 comments.The filtering classifier was selected for compatibility with prior work by Monti et al.
- Delta comments: The authors identify 7,254 delta comments by treating all parent comments from a rewarded author in the thread as opinion-changing.This accounts for delays between the successful comment and the bot-generated delta notification.
4 Methods
The method generates LLM counter-arguments to ChangeMyView posts, extracts nine social dimensions, and compares their prevalence and associations with successful persuasion. It also evaluates whether LLMs reciprocate opinion holders’ social intent using odds-ratio analyses.
- Models: The study selects three representative LLMs to examine illocutionary intent in argumentative contexts.The broader model pool included Llama2-chat, Llama3-chat, Mistral-7B-instruct, GPT-3.5-turbo, and GPT-4.
- Generation: Models receive each original post with the instruction “You have one chance to change my view” and generate one counter-argument per post.Generation uses nucleus sampling with temperature 0.9, top_p 0.6, and a 600-token maximum.
- Model selection: LLAMA2-7B, MISTRAL-7B-instruct, and GPT-3.5-turbo are selected using effectiveness and impact scores from argument-quality classifiers.The selection begins with seven widely used models and a 50-post sociopolitical subsample.
- Social dimensions: Social-dimension classifiers estimate scores from 0 to 1 for nine dimensions, which are binarized at the 85th-percentile threshold.Dimension scores are weight-discounted to reduce penalties for shorter arguments.
- Statistical analysis: Odds ratios measure associations between social dimensions and opinion change, and compare dimensions in LLM-generated versus human delta comments.The analysis also measures post-comment pairs to assess whether LLMs reciprocate social intent like successful human commenters.
- Statistical analysis: The analysis reports probability variation only for statistically highly significant odds-ratio differences with P < 0.01.The baseline probability represents the chance of a dimension appearing in a randomly paired message.
5 Results
Across three models, LLM-generated comments express more social intent than human comments, especially trust, but differ from successful human comments in dimensions associated with opinion change. Their interaction patterns vary by model, while crowdworkers nevertheless preferred GPT-3.5-turbo-generated arguments in most cases.
- 5.1 Social Dimensions in LLM Comments: Trust appears in nearly all LLM-generated messages, sharply contrasting with opinion-changing comments where trust is the second least frequent dimension.Generated responses often begin with phrases such as “I understand your perspective.”
- 5.1 Social Dimensions in LLM Comments: All three LLMs consistently convey more social intent than humans, with generated messages containing at least one dimension and sometimes as many as six.Human comments often rely on one dimension, while around 23% contain none and about 2% contain four.
- 5.2 LLMs vs. Opinion-changing Comments: 84% and 58%: successful human comments are more likely than original posts to convey knowledge and similarity, while dimension-free comments are 40% less likely to change views.These results identify knowledge and similarity as especially relevant dimensions for successful human persuasion.
- 5.2 LLMs vs. Opinion-changing Comments: Compared with successful human comments, LLMs are more likely to express support by 554% and less likely to express similarity and knowledge by 39% and 47%.They are also more likely to express status, power, conflict, identity, and fun.
- 5.3 Social Intent Dynamics: GPT-3.5-turbo shows stronger reciprocity than the other models, significantly exceeding successful human comments for support by 103%, knowledge by 69%, and similarity by 50%.Across models, post-comment dynamics differ; GPT-3.5-turbo aligns more closely with the original post’s intent.
- 5.4 Human Preferences: 83%: crowdworkers selected GPT-3.5-turbo-generated messages as more likely to change the opinion holder’s view, with annotator agreement of 0.79.Preferences remained largely consistent across knowledge-and-trust combinations, and generated arguments were often preferred even when workers disagreed with both stances.
6 Conclusions and Discussion
The study finds that LLMs express illocutionary intent frequently and densely, often exceeding humans, while using social strategies associated with opinion change. Crowdworkers also prefer LLM-generated arguments, raising concerns about their influence on public opinion.
- LLMs effectively convey illocutionary intent, often more frequently and densely than humans.
- All three models consistently express trust in conversation partners, potentially affirming their views and increasing perceived likability.
- GPT-3.5-turbo shows stronger post-comment reciprocity patterns, a behavior closely linked to opinion change.
- LLMs use rhetorical strategies that differ from successful human arguments, especially in knowledge and similarity.
- Crowdworkers find LLM-generated arguments more agreeable and consistently prefer them over human-written opinion-changing arguments.
7 Limitations
The study’s limitations concern the simulated CMV setting, generated-text length, uncontrolled topic effects, and speculative judgments by crowdworkers. These constraints bound how directly the findings generalize to real conversations and opinion change.
- The study simulates exchanges using /r/ChangeMyView, so other platforms and real one-to-one LLM conversations may differ.
- Fixed maximum lengths for LLM texts may affect human comprehension and perceived writing style relative to human comments.
- The annotation study does not control for topic confounders that may influence model texts and crowdworkers’ judgments.
- Because crowdworkers evaluate arguments that are not their own posts, their judgments about opinion change are partly speculative.
8 Ethical Considerations
The study identifies a dual-use risk: findings about why LLMs persuade could support targeted harmful persuasion. It therefore advises against such applications and supports detecting and preventing manipulative deployment.
- Persuasion findings could enable developers to train LLMs for harmful influence on specific target groups.
- The authors advocate detecting and preventing manipulative model deployment.
A.1 Data
The study uses CMV discussions as an opinion-exchange dataset, with deltas marking successful persuasion, and supplements the analysis with filtering, model-selection, and argument-quality procedures. The appendix also reports quality differences among selected and evaluated models.
- CMV Data: CMV users post viewpoints, debate through comments and replies, and can award a delta to comments that change their view.
- CMV Data: The dataset may contain offensive content because it comes from online discourse, although the researchers did not create or endorse that material.
- Analysis Pipeline: The study describes procedures for model selection, counter-argument generation, social-dimension classification, and metric calculation.
- Analysis Pipeline: Argument quality is assessed with RoBERTa-based adapters producing scores for seven dimensions across generated arguments.
- Analysis Pipeline: Quality scores are evaluated on 50 sociopolitical CMV posts, with higher scores indicating better argument quality and a 600-token maximum.
- Model Evaluation: Among the evaluated models, MISTRAL-7B-instruct is the overall quality winner, while LLAMA3-chat models generate substantially lower-quality arguments.
A.2.2 LLM Data
This section describes how LLM outputs and human comments were generated, scored for argument quality and social dimensions, and evaluated through crowdsourced agreement judgments.
- LLM data: The study used LLAMA2-7B, MISTRAL-7B-instruct, and GPT-3.5-turbo, with LLM comments generated using nucleus sampling.No prompt engineering or optimization was used.
- Argument quality: Argument quality was assessed across seven dimensions using RoBERTa-based adapters, with scores averaged across nucleus-sampling and greedy-decoding settings.Higher scores indicate better argument quality, while black bars represent variance across decoding settings.
- Measuring dimensions: The length-discounted prior probability aggregates dimension scores across messages and scales them between 0 and 1.The scaling factor of 2 reflects dimension scores ranging from 0 to 2.
- Opinion change analysis: Odds ratios compare the likelihood of a social dimension in comments receiving different outcomes, including human- and LLM-written comments.The analysis also defines conditional probabilities for comments containing a dimension and uses confidence intervals for comparisons.
- Crowdsourcing: Crowdworkers evaluated posts alongside human-written and GPT-3.5-turbo comments by indicating agreement with each displayed text.Participants could agree with both comments, and failed attention checks led to exclusion.
C Further Results
The further-results analysis finds that LLM-generated comments express some social dimensions more often than human comments, especially trust, while model differences appear in several other dimensions.
- LLMs versus CMV comments: LLMs were substantially more likely than human commenters to express trust and rarely lacked any illocutionary intent.Figure 7 compares odds ratios for social dimensions in LLM-generated and CMV comments.
- LLMs versus CMV comments: Support, fun, status, and power showed the largest remaining differences between LLM-generated and human comments.The magnitude of these disparities varied across models.
- Model variation: Support, fun, and status were approximately 8% less likely in LLAMA2-7B and GPT-3.5-turbo outputs, a pattern less evident for MISTRAL-7B-instruct.The authors suggest alignment training may shape communicative behavior across models.