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针对人类与大型语言模型的对抗性社会认识论Adversarial Social Epistemology for Assemblies of Humans and Large Language Models

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

一种新的框架——对抗性社会认识论（ASE）被引入，以更好地理解大型语言模型（LLMs）与人类用户互动时信任操控的动态。该研究批判了传统概念，如认识泡沫和虚假信息传播，提供了关于如何在公共沟通中破坏信任的新视角。ASE框架旨在分析人类与LLMs之间的互动，强调这些系统在显著影响感知和信念方面的潜力。

A new framework called adversarial social epistemology (ASE) has been introduced to better understand the dynamics of trust manipulation in environments where large language models (LLMs) interact with human users. This research critiques traditional concepts such as epistemic bubbles and misinformation diffusion, providing a fresh perspective on how trust can be breached in public communications. The ASE framework aims to analyze the interactions between humans and LLMs, highlighting the potential for these systems to influence perceptions and beliefs in significant ways.

要点

  1. ASE框架为理解人类与LLM互动中的信任动态提供了新方法。

    The ASE framework offers a novel approach to understanding trust dynamics in human-LLM interactions.

  2. 它批判了现有的虚假信息和信任理论，建议在AI时代需要更新模型。

    It critiques existing theories on misinformation and trust, suggesting a need for updated models in the age of AI.

  3. 该研究强调了在涉及AI的公共沟通中解决信任操控的重要性。

    The research emphasizes the importance of addressing trust manipulation in public communications involving AI.

为何重要

这项研究的重要性在于它解决了关于AI背景下信任和虚假信息日益增长的担忧，尤其是随着LLMs越来越多地融入日常沟通。

This research is significant as it addresses the growing concerns about trust and misinformation in the context of AI, particularly as LLMs become more integrated into everyday communication.

按谁的角度看

产品经理

对于产品经理来说，理解ASE框架可以帮助设计促进信任并减轻虚假信息风险的AI系统，从而确保更好的用户参与和满意度。

For product managers, understanding the ASE framework can help in designing AI systems that foster trust and mitigate misinformation risks, ensuring better user engagement and satisfaction.

开发者

开发者应意识到AI系统中信任操控的影响，因为这可以为更负责任和伦理的AI应用的设计和实施提供指导。

Developers should be aware of the implications of trust manipulation in AI systems, as it can inform the design and implementation of more responsible and ethical AI applications.

学生与求职者

在AI及相关领域的学生和求职者应认识到AI系统中信任动态的重要性，因为这一知识可以增强他们对伦理AI开发及其社会影响的理解。

Students and job-seekers in AI and related fields should recognize the importance of trust dynamics in AI systems, as this knowledge can enhance their understanding of ethical AI development and its societal impacts.

覆盖来源

来源标题与摘录保留原文，以便核对与追溯。

  • arXiv cs.AI

    Adversarial Social Epistemology for Assemblies of Humans and Large Language Models

    Researchers introduced an adversarial social epistemology (ASE) framework to analyze trust manipulation by communicative agents in environments with large language models. The framework critiques existing concepts like epistemic bubbles and misinformation diffusion, offering new insights into trust breaches in public communications.