Symbolic triggers like modifiers and named entities keep Gemma hallucination rates at 64-79% across model scales, suggesting larger models do not eliminate this failure mode.
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
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abstract
As Large Language Models (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.
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Investigating Symbolic Triggers of Hallucination in Gemma Models Across HaluEval and TruthfulQA
Symbolic triggers like modifiers and named entities keep Gemma hallucination rates at 64-79% across model scales, suggesting larger models do not eliminate this failure mode.