Awesome-LLM-hallucination

Overview

A curated GitHub repository listing research papers on hallucination in large language models. It organizes papers by category to help developers and researchers track mitigation strategies and detection methods.

Best for

Best for
Researchers and developers seeking a comprehensive paper bibliography on LLM hallucination

Use cases

• Surveying academic literature on LLM hallucination causes and solutions
• Identifying detection techniques for hallucinated outputs
• Benchmarking model reliability against known hallucination taxonomies

Notes

A curated GitHub repository listing research papers on hallucination in large language models. It organizes papers by category to help developers and researchers track mitigation strategies and detection methods.

335 stars on GitHub. Last updated 2024-03-11. Licensed MIT.

Use cases

• Surveying academic literature on LLM hallucination causes and solutions
• Identifying detection techniques for hallucinated outputs
• Benchmarking model reliability against known hallucination taxonomies

Pros

• Structured categorization of papers for quick reference
• Active community maintenance with 335 stars
• Free and open access to a centralized resource

Cons

• Limited to paper listings, no code or tooling provided
• May not cover the most recent preprints or industry practices
• No built-in evaluation or testing capabilities

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