Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness

• URL: http://arxiv.org/abs/2309.11064v1
• Date: Wed, 20 Sep 2023 05:04:16 GMT
• Title: Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
• Authors: Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Amit Sheth, Amitava Das
• Abstract summary: 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.
• Score: 6.009751153269125
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation [44.486880633185756]
  Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. LLMs have been shown to suffer from a critical limitation pertinent to 'hallucination' in their output. We study four tasks with negation: 'false premise completion', 'constrained fact generation','multiple choice question answering', and 'fact generation' We show that open-source state-of-the-art LLMs such as LLaMA-2-chat, Vicuna, and Orca-2 hallucinate considerably on all these tasks involving negation.
  arXiv  Detail & Related papers  (2024-06-08T15:20:56Z)
• Hallucination Diversity-Aware Active Learning for Text Summarization [46.00645048690819]
  Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallucinations typically require costly human annotations to identify and correct hallucinations in LLM outputs. We propose the first active learning framework to alleviate LLM hallucinations, reducing costly human annotations of hallucination needed.
  arXiv  Detail & Related papers  (2024-04-02T02:30:27Z)
• On Large Language Models' Hallucination with Regard to Known Facts [74.96789694959894]
  Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics. Our study shed light on understanding the reasons for LLMs' hallucinations on their known facts, and more importantly, on accurately predicting when they are hallucinating.
  arXiv  Detail & Related papers  (2024-03-29T06:48:30Z)
• A Survey on Hallucination in Large Vision-Language Models [18.540878498840435]
  Large Vision-Language Models (LVLMs) have attracted growing attention within the AI landscape for its practical implementation potential. However, hallucination'', or more specifically, the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs. We dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation.
  arXiv  Detail & Related papers  (2024-02-01T00:33:21Z)
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models [134.6697160940223]
  hallucination poses great challenge to trustworthy and reliable deployment of large language models. Three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them. This work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation.
  arXiv  Detail & Related papers  (2024-01-06T12:40:45Z)
• Alleviating Hallucinations of Large Language Models through Induced Hallucinations [67.35512483340837]
  Large language models (LLMs) have been observed to generate responses that include inaccurate or fabricated information. We propose a simple textitInduce-then-Contrast Decoding (ICD) strategy to alleviate hallucinations.
  arXiv  Detail & Related papers  (2023-12-25T12:32:49Z)
• Hallucination Augmented Contrastive Learning for Multimodal Large Language Model [53.65682783591723]
  Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning.
  arXiv  Detail & Related papers  (2023-12-12T04:05:15Z)
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions [42.007305423982515]
  Large language models (LLMs) produce hallucinations, resulting in content inconsistent with real-world facts or user inputs. This survey aims to provide a thorough and in-depth overview of recent advances in the field of LLM hallucinations.
  arXiv  Detail & Related papers  (2023-11-09T09:25:37Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)
• On Hallucination and Predictive Uncertainty in Conditional Language Generation [76.18783678114325]
  Higher predictive uncertainty corresponds to a higher chance of hallucination. Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties. It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
  arXiv  Detail & Related papers  (2021-03-28T00:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.