Can Language Models Recognize Convincing Arguments? (2024)

Abstract: The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives. Here, we study their performance in detecting convincing arguments to gain insights into LLMs’ persuasive capabilities without directly engaging in experimentation with humans. We extend a dataset by Durmus and Cardie (2018) with debates, votes, and user traits and propose tasks measuring LLMs’ ability to (1) distinguish between strong and weak arguments, (2) predict stances based on beliefs and demographic characteristics, and (3) determine the appeal of an argument to an individual based on their traits. We show that LLMs perform on par with humans in these tasks and that combining predictions from different LLMs yields significant performance gains, surpassing human performance. The data and code released with this paper contribute to the crucial effort of continuously evaluating and monitoring LLMs’ capabilities and potential impact. (https://go.epfl.ch/persuasion-llm)

## introduction as llms rise in capacity and popularity, so has the concern that they may help create and propagate tailor-made, convincing narratives @xcite . while "tailormade misinformation" predates llms @xcite , frontier models such as gpt-4, claude 3, and gemini 1.5 could add to the problem by allowing malicious actors to easily create diverse, personalized content @xcite or enable the detection (and amplification) of existing content that would be particularly persuasive to individuals with specific demographics or beliefs @xcite . previous work has found llms to be persuasive in the generative setting @xcite @xcite ; for example, @xcite found that, when provided with personal attributes, gpt-4 outperformed crowdworkers in a debate setting. yet, assessing models' capacity to generate arguments requires continuous human experimentation as llms evolve, which can be time-consuming and resource-intensive. on the contrary, measuring a model's capacity to detect content persuasive to specific demographics can be done quickly and without interaction with human subjects, making it a more efficient approach for benchmarking the persuasive capabilities of llms. present work. we study whether llms can detect content that would be persuasive to individuals with specific demographics or beliefs. we center our investigation around three research questions. namely, can llms. . . to investigate these questions, we extend a dataset collected by @xcite from a defunct debate platform (debate.org). we annotate 833 politics-related debates with clear propositions, such as "the electoral college should remain unchanged." each debate contains arguments for ("pro") and against ("con") the proposition, along with votes from debate.org participants indicating the winning side. importantly, the dataset includes demographic information of the voters as well as their stances on 48 so-called "big issues". for 121 debates with 751 votes on three of the most prominent topics in the dataset, we obtained crowdsourced labels to compare the capabilities of llms to those of humans. then, using this enriched dataset, we evaluate the performance of four male 29 con pro pro con pro con "the death penalty should be legal" gender: age: euthanasia: abortion: large language model better debater? position before? position after? pro pro con con con pro figure 1: our approach to study llms' persuasiveness capabilities. we measure to which extent llms can reproduce human judgments on the quality and persuasiveness of arguments. suppose llms can predict users' positions on stances (e.g., the death penalty should be legal) before and after reading a debate and judge who the better debater was. in that case, they would be well suited to power personalized misinformation and propaganda. llms (gpt-3.5, gpt-4, llama 2, and mistral 7b) on three tasks: 1) identifying the side with more convincing arguments (rq1); 2) predicting individuals' stances on specific propositions before the debate, given their demographic and basic belief information (rq2); and 3) predicting individuals' stances on specific propositions after the debate, given their demographic and basic belief information (rq3). figure 1 illustrates our approach. our key finding is that llm exhibits human-like performance across the three proposed tasks. in judging the better debater (rq1), gpt-4 (accuracy: 60.50%) is as good as an individual voter in the dataset (accuracy: 60.69%;). when predicting users' stances on specific issues before and after reading the debate (rq2 and rq3), llms again perform similarly to humans. for instance, in the before the debate scenario (rq2), mistral yields an accuracy of 42.27%, whereas crowdworkers achieve 39.86% (random guessing would yield 33.3% accuracy). however, zero-shot prediction with llms still underperforms a supervised machine learning model [xgboost @xcite ], which achieves 58.25% accuracy in cross-validation. nevertheless, stacking the predictions of llms and using them as features in a supervised learning setting reduces the performance gap (45.91%). overall, our work contributes to the growing body of research on the societal impact of llms @xcite @xcite . we shed light on the potential misuse of llms by investigating their ability to detect persuasive content tailored to specific demographics. ## related work we review related work in three broad directions closely related to the tasks proposed. demographics, beliefs, and persuasion. demographics have long been known to impact people's political beliefs and attitudes. group-level demographic factors such as race, religion, and education shape individuals' perspectives on various political issues and voting behavior in the u.s. context @xcite . for example, 78% of black, 72% of asian, and 65% of hispanic workers see efforts on increasing diversity, equity, and inclusion at work positively, compared to 47% of white workers @xcite . similarly, previous work indicates that persuasion depends on the message recipients' existing values and that individual differences can influence persuasion (o'keefe, 2015). for instance, @xcite demonstrated that tailoring messages to different personality traits can make them more persuasive; @xcite showed that men and women differ significantly in their responsiveness to the different persuasive strategies. closer to the work at hand, @xcite and al @xcite showed considering demographic characteristics can enhance the prediction of argument persuasiveness. however, the extent to which llms can utilize demographic characteristics in persuasiveness judgment remains underexplored. in this work, we examine how llms can capture the correlations between demographics and beliefs (rq2) and how personal attributes determine the persuasiveness of arguments (rq3). ## data data for this study was collected by @xcite from an online debate platform (debate.org; no longer operational). the platform allowed users to participate in and vote on debates covering a breadth of topics, including politics, religion, and science. each debate within the dataset consists of multiple rounds, each round with an argument from both the "pro" and "con" perspectives. users on the platform could vote on various aspects of the debate, such as which side they believed provided a more convincing argument. the raw dataset contains 78,376 debates, 45,348 users, and 195,724 votes. each user has corresponding demographic information, such as gender and age, as well as their stances on 48 so-called "big issues," such as abortion, capital punishment, and national ## methods llms considered. for this study, we compare the performance of two open-source llms, namely mistral 7b (mistral-7b-instruct-v0.1) and meta's llama 2 70b (llama-2-70b-chat), with openai's closed-source gpt-3.5 (gpt-3.5-turbo-1106) and gpt-4 (gpt-4-0613). we use the standard temperatures for each model. prompting. we follow @xcite to develop our prompt: each had a system role, context, question, and constraint. we experimented with different structures and found that, overall, the structure mattered little as long as the wording was clear and concise. since we had three research questions to answer, we had three prompt structures that combined the debate proposition, the debate itself, and user demographics. we show the prompt structure used for rq1 in figure 2 . all prompts indicated that llms should respond only with the labels "pro," "con," or "tie". nevertheless, many of the models failed to adhere to this instruction, necessitating post-processing to extract the actual answer. generally, instances of incorrect responses involved the answer accompanied by additional spaces or punctuation or presented in a complete sentence format, such as: "based on the given demographics, the person is most likely to agree with the 'con' side in the debate." we used heuristics to extract the responses in these cases. additionally, there were occasions when the llms failed to produce any answer, resulting in responses akin to: "i cannot determine the person's position in the debate without additional information." we depict the remaining prompts in figures 3 and 4 and provide further details in appendix a. evaluation. we evaluate the accuracy of language models by comparing the answers they provide with the ground truth data from poliprop and poliissues. we obtain confidence intervals through bootstrapping. besides considering each llm individually, we also consider the performance of stacked llm predictions, obtained by using the output of different llms as features in a supervised machine learning model @xcite . baselines/benchmarks we interpret the llm accuracies by establishing the following baselines and benchmarks as metrics of comparison: ## discussion and conclusion here we studied llm's persuasive capabilities by considering its ability to identify convincing arguments in general and for people with specific arguments. we argue that if llms can detect content that is highly persuasive to specific demographics, they may be used to detect and amplify tailor-made misinformation and propaganda. our findings indicate that llms demonstrate human-level performance in (1) judging argument quality, (2) predicting users' stances on specific topics given users' demographics and basic beliefs, and (3) detecting arguments that would be persuasive to individuals with specific demographics or beliefs. however, the overall human performance is not high in each of the three tasks [around 60% for (1), and around 40% for (2) and (3)], which could be due to the inherent difficulty of the tasks, as well as variance and randomness in the data. this does not necessarily imply that llms do not pose any additional risk of tailor-made misinformation in the future. it is plausible that with access to more personal information about an individual, such as personality traits, llms could perform better at detecting persuasive arguments @xcite . nevertheless, it is important to consider that the more fine-grained the target, the harder and more costly it becomes to reach the targeted population, and the cost-benefit analysis is not straightforward @xcite . one hypothesis that could explain the relatively low accuracy for both llms and human performance is that these demographic questions and big-issue stances may not be highly relevant for the task, as suggested by @xcite .