Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness (2309.11064v1)

Abstract: As LLMs have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern.

Understanding Hallucinations in LLMs

Hallucination in LLMs: An Introduction

In the field of artificial intelligence, LLMs, such as GPT-4, have demonstrated exceptional capabilities in generating human-like responses. However, they are prone to generating what is known as "hallucinations"—responses with untrue or fabricated content. Addressing this issue involves understanding the conditions under which hallucinations occur. This paper explores how the linguistic nuances of prompts—specifically readability, formality, and concreteness—affect the tendency of LLMs to hallucinate.

The Influence of Prompt Linguistics

The research indicates that prompts exhibiting greater formality and specificity tend towards generating fewer hallucinatory responses from LLMs. In contrast, the relationship between prompt readability and hallucination was less clear-cut, presenting a mixed pattern in the experimental results. The impact of readability on hallucinatory tendencies varied, indicating that both easy-to-read and more formal prompts could still result in lower rates of hallucinations.

The Mitigating Role of Formality and Concreteness

Delving deeper into the facets of formality and concreteness, the paper demonstrates that more formal language cues in prompts consistently correlate with a reduced incidence of hallucination. Additionally, prompts with higher levels of concreteness, containing tangible and clear language, also seem to mitigate the occurrence of hallucination, especially in categories related to numbers and acronyms.

Summary of Findings and Implications

The paper concludes a significant link between the linguistic attributes of prompts and the rate of hallucinations in LLM outputs. Moreover, leading-edge LLMs, such as GPT-4, show a pattern where improved prompt structures—those that are more formal and concrete—are effective in reducing hallucinations. These findings can be pivotal in guiding further development of prompt engineering techniques, leading to better, more reliable LLM behavior, potentially enhancing their applicability in various domains.