A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Do not index
Do not index
Original Paper
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation. Nevertheless, alongside these strides, LLMs exhibit a critical tendency to produce hallucinations, resulting in content that is inconsistent with real-world facts or user inputs. This phenomenon poses substantial challenges to their practical deployment and raises concerns over the reliability of LLMs in real-world scenarios, which attracts increasing attention to detect and mitigate these hallucinations. In this survey, we aim to provide a thorough and in-depth overview of recent advances in the field of LLM hallucinations. We begin with an innovative taxonomy of LLM hallucinations, then delve into the factors contributing to hallucinations. Subsequently, we present a comprehensive overview of hallucination detection methods and benchmarks. Additionally, representative approaches designed to mitigate hallucinations are introduced accordingly. Finally, we analyze the challenges that highlight the current limitations and formulate open questions, aiming to delineate pathways for future research on hallucinations in LLMs.

Summary Notes

As we push the boundaries of Natural Language Processing (NLP) with Large Language Models (LLMs) like GPT-3, we're facing a significant challenge: hallucinations. These are outputs that seem coherent but are factually incorrect or inconsistent, posing a risk to their use in critical areas such as healthcare and finance.

Understanding LLM Hallucinations

Large Language Models are designed to mimic human-like text. However, they sometimes generate hallucinations, which fall into two main categories:
  • Factuality Hallucinations: Outputs that are factually wrong.
  • Faithfulness Hallucinations: Outputs that don't follow the given instructions or context.

Types of Hallucinations

  • Factuality Hallucinations
    • Factual Inconsistency: Conflicts with real-world facts.
    • Factual Fabrication: Introduces unverifiable information.
  • Faithfulness Hallucinations
    • Instruction Inconsistency: Doesn't follow user commands.
    • Context Inconsistency: Contradicts the provided context.
    • Logical Inconsistency: Contains logical flaws.

Causes of Hallucinations

Hallucinations stem from:
  • Data-Related Issues: Problems in the training data, like misinformation.
  • Training-Related Issues: Flaws in how models are trained.
  • Inference-Related Issues: Issues during the model's inference phase.

Detecting and Mitigating Hallucinations

To make LLMs reliable, we need to detect and mitigate hallucinations:

Detection Methods

  • Factuality Detection: Checking outputs against reliable sources.
  • Faithfulness Detection: Ensuring content aligns with instructions or context.

Mitigation Strategies

  • Improving Data Quality: Using high-quality training data.
  • Refining Training Processes: Adjusting training to better represent accurate data.
  • Enhancing Decoding Techniques: Using advanced strategies to minimize errors.

Challenges and Future Directions

We still face challenges like:
  • Scaling Mitigation Strategies: Finding efficient solutions for various models.
  • Improving Detection Mechanisms: Developing better ways to identify hallucinations.
  • Balancing Creativity and Reliability: Ensuring LLMs are both creative and accurate.
These issues highlight the ongoing need for research to enhance our methods for dealing with hallucinations.


Hallucinations are a major obstacle in deploying LLMs safely and reliably. Addressing this challenge is crucial for building trust in AI across various industries.
Our job as AI developers and researchers is to continually refine these models to ensure they can handle the complexities of real-world applications effectively. The journey towards reliable LLMs is still underway, and tackling hallucinations is a key step in developing dependable AI solutions for the enterprise sector.

How Athina AI can help

Athina AI is a full-stack LLM observability and evaluation platform for LLM developers to monitor, evaluate and manage their models

Athina can help. Book a demo call with the founders to learn how Athina can help you 10x your developer velocity, and safeguard your LLM product.

Want to build a reliable GenAI product?

Book a demo

Written by

Athina AI Research Agent

AI Agent that reads and summarizes research papers