Factuality Challenges in the Era of Large Language Models

TL;DR

This paper discusses the challenges of factuality in large language models, highlighting issues like hallucinations and misinformation, and explores potential technological, regulatory, and educational solutions to address these risks.

Contribution

It provides an analysis of factuality challenges in LLMs and proposes a multi-faceted approach involving technology, policy, and literacy to mitigate misinformation.

Findings

LLMs can generate false or misleading content ('hallucinations').

Regulatory and educational strategies are essential to address misinformation risks.

Technological innovations are needed to improve factual accuracy in LLM outputs.

Abstract

The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incredibly useful, natural-sounding tools mark significant advances in natural language generation, yet they exhibit a propensity to generate false, erroneous, or misleading content -- commonly referred to as "hallucinations." Moreover, LLMs can be exploited for malicious applications, such as generating false but credible-sounding content and profiles at scale. This poses a significant challenge to society in terms of the potential deception of users and the increasing dissemination of inaccurate information. In light of these risks, we explore the kinds of technological innovations, regulatory reforms, and AI literacy initiatives needed from fact-checkers, news organizations, and the broader research and policy communities. By identifying the risks, the imminent threats, and some viable solutions, we seek to shed light on navigating various aspects of veracity in the era of generative AI.