打击法律硕士时代的错误信息:机遇与挑战

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2024-08-01 DOI:10.1002/aaai.12188
Canyu Chen, Kai Shu
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引用次数: 0

摘要

假新闻和谣言等虚假信息严重威胁着信息生态系统和公众信任。大型语言模型(LLMs)的出现极有可能重塑打击虚假信息的格局。一般来说,LLMs 在这场斗争中可能是一把双刃剑。一方面,LLMs 凭借其深厚的世界知识和强大的推理能力,为打击虚假信息带来了大有可为的机会;另一方面,LLMs 也有可能成为虚假信息领域的 "新宠儿"。因此,一个新出现的问题是:我们能否利用 LLM 来打击错误信息?另一方面,关键的挑战在于 LLMs 很容易被用来大规模生成欺骗性的错误信息。那么,另一个重要问题是:如何打击 LLM 生成的错误信息?在本文中,我们首先系统回顾了在 LLM 出现之前打击误导信息的历史。然后,我们分别阐述了当前的努力,并对这两个基本问题进行了展望。本调查报告的目的是促进利用 LLMs 打击误导信息的进展,并呼吁不同利益相关者为打击 LLM 生成的误导信息做出跨学科努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Combating misinformation in the age of LLMs: Opportunities and challenges

Misinformation such as fake news and rumors is a serious threat for information ecosystems and public trust. The emergence of large language models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double-edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emerging question is: can we utilize LLMs to combat misinformation? On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: how to combat LLM-generated misinformation? In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions, respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM-generated misinformation.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
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