awesome-hallucination-detection

Overview

A community-curated list of papers and resources focused on hallucination detection in large language models. It organizes research by categories such as detection methods, benchmarks, and surveys, providing a structured reference for builders and researchers.

Best for

Best for
Researchers and developers who need a curated bibliography on hallucination detection

Use cases

• Exploring state-of-the-art hallucination detection techniques
• Finding benchmark datasets for evaluating model hallucinations
• Surveying recent academic literature on LLM reliability

Notes

A community-curated list of papers and resources focused on hallucination detection in large language models. It organizes research by categories such as detection methods, benchmarks, and surveys, providing a structured reference for builders and researchers.

1,096 stars on GitHub. Last updated 2026-05-25. Licensed Apache-2.0.

Use cases

• Exploring state-of-the-art hallucination detection techniques
• Finding benchmark datasets for evaluating model hallucinations
• Surveying recent academic literature on LLM reliability

Pros

• Comprehensive coverage of research papers across detection approaches
• Regularly updated with contributions from the community
• Well-organized into categories for quick reference

Cons

• Not a runnable tool or library; no code implementations included
• Requires manual paper reading and evaluation
• May lack practical guidance for real-world deployment

Indexed from awesome-llm and enriched against its public facts.