REVIEW 2 cited by
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 2 Pith papers
-
Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances
A Fair Equality of Chances-based framework decomposes GenAI unfairness into harms/benefits, morally arbitrary factors, and morally decisive factors to improve measurement validity.
-
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.
Discussion (0). Sign in to comment.