Can (A)I Change Your Mind?
- URL: http://arxiv.org/abs/2503.01844v1
- Date: Mon, 03 Mar 2025 18:59:54 GMT
- Title: Can (A)I Change Your Mind?
- Authors: Miriam Havin, Timna Wharton Kleinman, Moran Koren, Yaniv Dover, Ariel Goldstein,
- Abstract summary: The study was conducted entirely in Hebrew with 200 participants.<n>It assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics.
- Score: 0.6990493129893112
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing integration of large language model (LLM) based conversational agents into everyday life raises critical cognitive and social questions about their potential to influence human opinions. Although previous studies have shown that LLM-based agents can generate persuasive content, these typically involve controlled, English-language settings. Addressing this, our preregistered study explored LLM's persuasive capabilities in more ecological, unconstrained scenarios, examining both static (written paragraphs) and dynamic (conversations via Telegram) interaction types. Conducted entirely in Hebrew with 200 participants, the study assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics. Results indicated that participants adopted LLM and human perspectives similarly, with significant opinion changes evident across all conditions, regardless of interlocutor type or interaction mode. Confidence levels increased significantly in most scenarios, except in static LLM interactions. These findings demonstrate LLM-based agents' robust persuasive capabilities across diverse sources and settings, highlighting their potential impact on shaping public opinions.
Related papers
- Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions [27.38030183605309]
Large language models (LLMs) generate persuasive content at scale and reinforce existing biases.
This study investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformative content.
Our findings show that demographic factors influence susceptibility to misinformation in LLMs, closely reflecting the demographic-based patterns seen in human susceptibility.
arXiv Detail & Related papers (2025-03-03T20:30:22Z) - Examining Identity Drift in Conversations of LLM Agents [5.12659586713042]
This study examines identity consistency across nine Large Language Models (LLMs)<n>Experiments involve multi-turn conversations on personal themes, analyzed in qualitative and quantitative ways.
arXiv Detail & Related papers (2024-12-01T13:19:32Z) - Persuasion with Large Language Models: a Survey [49.86930318312291]
Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication.
In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness.
Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
arXiv Detail & Related papers (2024-11-11T10:05:52Z) - Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
arXiv Detail & Related papers (2024-10-24T04:02:30Z) - Bias in the Mirror: Are LLMs opinions robust to their own adversarial attacks ? [22.0383367888756]
Large language models (LLMs) inherit biases from their training data and alignment processes, influencing their responses in subtle ways.
We introduce a novel approach where two instances of an LLM engage in self-debate, arguing opposing viewpoints to persuade a neutral version of the model.
We evaluate how firmly biases hold and whether models are susceptible to reinforcing misinformation or shifting to harmful viewpoints.
arXiv Detail & Related papers (2024-10-17T13:06:02Z) - Can Language Models Recognize Convincing Arguments? [12.458437450959416]
Large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives.
We study their performance in detecting convincing arguments to gain insights into their persuasive capabilities.
arXiv Detail & Related papers (2024-03-31T17:38:33Z) - How should the advent of large language models affect the practice of
science? [51.62881233954798]
How should the advent of large language models affect the practice of science?
We have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate.
arXiv Detail & Related papers (2023-12-05T10:45:12Z) - The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry [2.300664273021602]
This research explores the effect of Large Language Models (LLMs) on consumer complaints submitted to the Consumer Financial Protection Bureau from 2015 to 2024.<n>We find that LLM usage is associated with an increased likelihood of obtaining relief from financial firms.
arXiv Detail & Related papers (2023-11-28T04:07:34Z) - Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation.
We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process.
We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
arXiv Detail & Related papers (2023-11-15T00:02:25Z) - Quantifying the Impact of Large Language Models on Collective Opinion
Dynamics [7.0012506428382375]
We create an opinion network dynamics model to encode the opinions of large language models (LLMs)
The results suggest that the output opinion of LLMs has a unique and positive effect on the collective opinion difference.
Our experiments also find that introducing extra agents with opposite/neutral/random opinions, we can effectively mitigate the impact of biased/toxic output.
arXiv Detail & Related papers (2023-08-07T05:45:17Z) - Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
This study aims to determine the reliability of applying personality assessments to Large Language Models.
Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
arXiv Detail & Related papers (2023-05-31T15:03:28Z) - Influence of External Information on Large Language Models Mirrors
Social Cognitive Patterns [51.622612759892775]
Social cognitive theory explains how people learn and acquire knowledge through observing others.
Recent years have witnessed the rapid development of large language models (LLMs)
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
arXiv Detail & Related papers (2023-05-08T16:10:18Z) - Can ChatGPT Assess Human Personalities? A General Evaluation Framework [70.90142717649785]
Large Language Models (LLMs) have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored.
This paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers Briggs Type Indicator (MBTI) tests.
arXiv Detail & Related papers (2023-03-01T06:16:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.