mlsanityData to 7 Oct 2026

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

arXiv 2309.11064 ↗First posted 20 Sep 2023cs.AIcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 20 Sep 2023.

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14 of 34 references.

References · 14

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  6. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  7. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  8. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  9. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  10. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  11. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
  12. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
2 more
  1. 2023Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learningcited by 115
  2. 2022Continuous-Time Meta-Learning with Forward Mode Differentiationcited by 12

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Citing papers per year: 2023: 2; 2024: 6; 2025: 5.

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  1. 2024The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Modelscited by 87
  2. 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
  3. 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 26
  4. 2024ChatGPT as Research Scientist: Probing GPT's Capabilities as a Research Librarian, Research Ethicist, Data Generator and Data Predictorcited by 21
  5. 2025Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Modelscited by 15
  6. 2023Fine-tune Language Models to Approximate Unbiased In-context Learningcited by 12
  7. 2024CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularitycited by 12
  8. 2025Out of Style: RAG's Fragility to Linguistic Variationcited by 10
  9. 2024Evaluating Consistency and Reasoning Capabilities of Large Language Modelscited by 7
  10. 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
  11. 2024"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasingcited by 2
  12. 2025The Illusionist's Prompt: Exposing the Factual Vulnerabilities of Large Language Models with Linguistic Nuancescited by 2
1 more
  1. 2023A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interactioncited by 0