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arXiv 2406.14508cs.CLcs.AIcs.CYcs.HC
大型语言模型政治说服力的对数缩放律证据
Evidence of a log scaling law for political persuasion with large language models
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）；Bocconi University（博科尼大学）；Meedan（米丹（Meedan））
Kobi Hackenburg, Ben M. Tappin, Paul Röttger, Scott Hale, Jonathan Bright, Helen Margetts
Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 U.S. political issues from 24 language models spanning several orders of magnitude in size. We then deploy these messages in a large-scale randomized survey experiment (N = 25,982) to estimate the persuasive capability of each model. Our findings are twofold. First, we find evidence of a log scaling law: model persuasiveness is characterized by sharply diminishing returns, such that current frontier models are barely more persuasive than models smaller in size by an order of magnitude or more. Second, mere task completion (coherence, staying on topic) appears to account for larger models' persuasive advantage. These findings suggest that further scaling model size will not much increase the persuasiveness of static LLM-generated messages.
The Levers of Political Persuasion with Conversational AI
UK AI Security Institute（英国人工智能安全研究所）；University of Oxford（牛津大学）；The London School of Economics and Political Science（伦敦政治经济学院）；Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes
Ukrainian Catholic University（乌克兰天主教大学）；Johannes Gutenberg University Mainz（美因茨约翰内斯·古滕贝格大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Centre for European Research in Trusted AI (CERTAIN)（欧洲可信人工智能研究中心）
Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；National University of Singapore（新加坡国立大学）；Inner Mongolia Research Institute, Shanghai Jiao Tong University（上海交通大学内蒙古研究院）
In this paper, we apply Item Response Theory, popular in education and political science research, to the analysis of argument persuasiveness in language. We empirically evaluate the model's performance on three datasets, including a novel dataset in the area of political advocacy. We show the advantages of separating these components under several style and content representations, including evaluating the ability of the speaker embeddings generated by the model to parallel real-world observations about persuadability.
Information Bargaining: Bilateral Commitment in Bayesian Persuasion
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Tongji University（同济大学）
It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics
Université de Montréal, MILA（蒙特利尔大学，MILA）；Carnegie Mellon University（卡内基梅隆大学）；MIT, Center for Research and Teaching in Economics（麻省理工学院，经济研究与教学中心）；Cornell University, University of Regina（康奈尔大学， Regina大学）；Cornell University, MIT（康奈尔大学，麻省理工学院）
While the effect of various lexical, syntactic, semantic and stylistic features have been addressed in persuasive language from a computational point of view, the persuasive effect of phonetics has received little attention. By modeling a notion of euphony and analyzing four datasets comprising persuasive and non-persuasive sentences in different domains (political speeches, movie quotes, slogans and tweets), we explore the impact of sounds on different forms of persuasiveness. We conduct a series of analyses and prediction experiments within and across datasets. Our results highlight the posi…
Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one's stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a ne…
Two studies tested the hypothesis that a Large Language Model (LLM) can be used to model psychological change following exposure to influential input. The first study tested a generic mode of influence - the Illusory Truth Effect (ITE) - where earlier exposure to a statement (through, for example, rating its interest) boosts a later truthfulness test rating. Data was collected from 1000 human participants using an online experiment, and 1000 simulated participants using engineered prompts and LLM completion. 64 ratings per participant were collected, using all exposure-test combinations of the…
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings
Graduate School of Data Science, Seoul National University（首尔国立大学数据科学研究生院）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Department of Communication, Seoul National University（首尔国立大学传播系）
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
Many recent political events, like the 2016 US Presidential elections or the 2018 Brazilian elections have raised the attention of institutions and of the general public on the role of Internet and social media in influencing the outcome of these events. We argue that a safe democracy is one in which citizens have tools to make them aware of propaganda campaigns. We propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. We further design a novel multi-granularity neural network, and we show that it ou…
Enhancing Conflict Resolution in Language Models via Abstract Argumentation
School of Philosophy, Zhejiang University（浙江大学哲学系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The State Key Lab of Brain-Machine Intelligence（脑机智能国家重点实验室）
MIT Center for Collective Intelligence（麻省理工学院集体智慧中心）
This literature review focuses on the use of Natural Language Generation (NLG) to automatically detect and generate persuasive texts. Extending previous research on automatic identification of persuasion in text, we concentrate on generative aspects through conceptualizing determinants of persuasion in five business-focused categories: benevolence, linguistic appropriacy, logical argumentation, trustworthiness, tools and datasets. These allow NLG to increase an existing message's persuasiveness. Previous research illustrates key aspects in each of the above mentioned five categories. A researc…
Red and blue language: Word choices in the Trump & Harris 2024 presidential debate
Institute for Information and Language Processing (CIS), LMU Munich（慕尼黑大学信息与语言处理研究所）；University Bologna（博洛尼亚大学）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）
