Factuality of Large Language Models

Le résumé fourni par la source

As large language models (LLMs) become pivotal sources of knowledge across diverse domains, ensuring their factuality is essential to prevent the spread of harmful information. The generation of fabricated but plausible information, commonly referred to as hallucinations, poses significant risks, particularly in high-stakes applications such as healthcare, legal systems, finance, and education. This chapter examines the challenges associated with hallucinations and other forms of harmful content, focusing on the vulnerabilities of LLMs from an adversarial perspective. We identify three primary challenges: (a) the inherent tendency of LLMs to generate inaccurate information and their vulnerability to adversarial manipulation; (b) the difficulty in detecting hallucinations; and (c) the challenge of developing effective strategies to mitigate hallucinations. Through a detailed analysis, this chapter evaluates the limitations of existing factuality assessment tools, the threats posed by adversarial exploits, and proactive strategies to improve model robustness. By addressing these vulnerabilities and exploring mitigation approaches, this chapter outlines a roadmap for reducing risks and enabling safer, more truthful LLMs.

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