# Redefining "Hallucination" in LLMs: Towards a psychology-informed framework for mitigating misinformation

arXiv: https://arxiv.org/abs/2402.01769 · HTML: https://mlsanity.com/paper/2402.01769/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation](https://mlsanity.com/paper/2307.03987/) (2023)
- [Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://mlsanity.com/paper/2011.02593/) (2020)
- [Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://mlsanity.com/paper/2310.06271/) (2023)
- [Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models](https://mlsanity.com/paper/2310.00836/) (2023)
- [Entity-aware Image Caption Generation](https://mlsanity.com/paper/1804.07889/) (2018)
- [Where's the Liability in Harmful AI Speech?](https://mlsanity.com/paper/2308.04635/) (2023)

## Built on by (most cited first, 5 of 5)

- [Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments](https://mlsanity.com/paper/2412.00323/) (2024)
- [Generation Constraint Scaling Can Mitigate Hallucination](https://mlsanity.com/paper/2407.16908/) (2024)
- [SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents](https://mlsanity.com/paper/2409.15817/) (2024)
- [HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection](https://mlsanity.com/paper/2504.10168/) (2025)
- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)

## Abstract (arXiv)

In recent years, large language models (LLMs) have become incredibly popular, with ChatGPT for example being used by over a billion users. While these models exhibit remarkable language understanding and logical prowess, a notable challenge surfaces in the form of "hallucinations." This phenomenon results in LLMs outputting misinformation in a confident manner, which can lead to devastating consequences with such a large user base. However, we question the appropriateness of the term "hallucination" in LLMs, proposing a psychological taxonomy based on cognitive biases and other psychological phenomena. Our approach offers a more fine-grained understanding of this phenomenon, allowing for targeted solutions. By leveraging insights from how humans internally resolve similar challenges, we aim to develop strategies to mitigate LLM hallucinations. This interdisciplinary approach seeks to move beyond conventional terminology, providing a nuanced understanding and actionable pathways for improvement in LLM reliability.