--- title: "awesome-hallucination-detection vs Awesome-LLM-hallucination" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/edinburghnlp-awesome-hallucination-detection-vs-luckyyysta-awesome-llm-hallucination" tools: ["edinburghnlp-awesome-hallucination-detection", "luckyyysta-awesome-llm-hallucination"] --- # awesome-hallucination-detection vs Awesome-LLM-hallucination *GraphCanon updated Aug 6, 2026* ## Verdict Pick awesome-hallucination-detection if awesome-hallucination-detection provides a curated list of research papers focused on techniques to detect and mitigate hallucinations in large language models (LLMs), including process supervision methods for factual QA; pick Awesome-LLM-hallucination if awesome-LLM-hallucination stands out as a resource dedicated to the in-depth analysis of hallucination phenomena within Large Language Models (LLMs). Its curated list and categorization make it. [awesome-hallucination-detection](https://github.com/EdinburghNLP/awesome-hallucination-detection) reports 1.1k GitHub stars, 91 forks, and 0 open issues, last pushed Jul 24, 2026. [Awesome-LLM-hallucination](https://github.com/LuckyyySTA/Awesome-LLM-hallucination) has 339 stars, 25 forks, and 4 open issues, last pushed Mar 11, 2024. Figures are from public GitHub metadata via [awesome-hallucination-detection's repository](https://github.com/EdinburghNLP/awesome-hallucination-detection) and [Awesome-LLM-hallucination's repository](https://github.com/LuckyyySTA/Awesome-LLM-hallucination). | | [awesome-hallucination-detection](/tools/edinburghnlp-awesome-hallucination-detection.md) | [Awesome-LLM-hallucination](/tools/luckyyysta-awesome-llm-hallucination.md) | | --- | --- | --- | | Tagline | List of papers on hallucination detection in LLMs. | A Survey on Hallucination in Large Language Models | | Stars | 1,121 | 339 | | Forks | 91 | 25 | | Open issues | 0 | 4 | | Language | - | - | | Adopt for | awesome-hallucination-detection provides a curated list of research papers focused on techniques to detect and mitigate hallucinations in large language models (LLMs), including process supervision methods for factual QA | Awesome-LLM-hallucination stands out as a resource dedicated to the in-depth analysis of hallucination phenomena within Large Language Models (LLMs). Its curated list and categorization make it distinct from other tools, | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-hallucination-detection](/tools/edinburghnlp-awesome-hallucination-detection.md) | [Awesome-LLM-hallucination](/tools/luckyyysta-awesome-llm-hallucination.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 877d | | Open issues (now) | 0 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/edinburghnlp-awesome-hallucination-detection/trust.md) | [trust report](/tools/luckyyysta-awesome-llm-hallucination/trust.md) | ## Decision facts: awesome-hallucination-detection - **Adopt for:** awesome-hallucination-detection provides a curated list of research papers focused on techniques to detect and mitigate hallucinations in large language models (LLMs), including process supervision methods for factual QA ## Decision facts: Awesome-LLM-hallucination - **Requirements:** The exact language used by the repository is unknown, as no specific programming languages are listed. - **Adopt for:** Awesome-LLM-hallucination stands out as a resource dedicated to the in-depth analysis of hallucination phenomena within Large Language Models (LLMs). Its curated list and categorization make it distinct from other tools, - **License detail:** MIT ## Choose when ### Choose awesome-hallucination-detection if… - License: awesome-hallucination-detection is Apache-2.0, Awesome-LLM-hallucination is MIT. - Tags unique to awesome-hallucination-detection: evaluation, llms, nlp, observability. - - When focusing on specific methodologies like Corpus Verify (CorVer) from the paper 'Verifiable Rewards Beyond Math and Code' which utilizes lightweight, process-based rewards to mitigate hallucinat ### Choose Awesome-LLM-hallucination if… - License: Awesome-LLM-hallucination is MIT, awesome-hallucination-detection is Apache-2.0. - Requirements: The exact language used by the repository is unknown, as no specific programming languages are listed.. - Tags unique to Awesome-LLM-hallucination: large language models, llm, survey. - - When you need detailed categorizations by causes, detection methods, and mitigation strategies for LLM hallucinations. ## When NOT to use awesome-hallucination-detection - When immediate implementation or code is needed rather than research papers, this repository is not suitable as it only curates information on methodologies and benchmarks. - - If your focus is on general LLM training techniques without a specific emphasis on hallucination detection or calibration ## When NOT to use Awesome-LLM-hallucination - - Avoid using this resource for practical, hands-on tools or code that helps mitigate hallucinations directly (it's primarily informative). - - Do not use if you are looking for real-time diagnostic software for identifying and correcting LLM hallucination mistakes in live applications. - - This tool is not suitable as a standalone guide for implementing mitigation techniques within your own large language models; it lacks detailed technical instructions. ## Common questions ### What is the difference between awesome-hallucination-detection and Awesome-LLM-hallucination? awesome-hallucination-detection: List of papers on hallucination detection in LLMs.. Awesome-LLM-hallucination: A Survey on Hallucination in Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-hallucination-detection over Awesome-LLM-hallucination? Choose awesome-hallucination-detection over Awesome-LLM-hallucination when License: awesome-hallucination-detection is Apache-2.0, Awesome-LLM-hallucination is MIT; Tags unique to awesome-hallucination-detection: evaluation, llms, nlp, observability; - When focusing on specific methodologies like Corpus Verify (CorVer) from the paper 'Verifiable Rewards Beyond Math and Code' which utilizes lightweight, process-based rewards to mitigate hallucinat. ### When should I choose Awesome-LLM-hallucination over awesome-hallucination-detection? Choose Awesome-LLM-hallucination over awesome-hallucination-detection when License: Awesome-LLM-hallucination is MIT, awesome-hallucination-detection is Apache-2.0; Requirements: The exact language used by the repository is unknown, as no specific programming languages are listed.; Tags unique to Awesome-LLM-hallucination: large language models, llm, survey; - When you need detailed categorizations by causes, detection methods, and mitigation strategies for LLM hallucinations. ### When should I avoid awesome-hallucination-detection? When immediate implementation or code is needed rather than research papers, this repository is not suitable as it only curates information on methodologies and benchmarks. - If your focus is on general LLM training techniques without a specific emphasis on hallucination detection or calibration ### When should I avoid Awesome-LLM-hallucination? - Avoid using this resource for practical, hands-on tools or code that helps mitigate hallucinations directly (it's primarily informative). - Do not use if you are looking for real-time diagnostic software for identifying and correcting LLM hallucination mistakes in live applications. - This tool is not suitable as a standalone guide for implementing mitigation techniques within your own large language models; it lacks detailed technical instructions. ### Is awesome-hallucination-detection or Awesome-LLM-hallucination more popular on GitHub? awesome-hallucination-detection has more GitHub stars (1,121 vs 339). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-hallucination-detection and Awesome-LLM-hallucination open source? Yes - both are open-source projects on GitHub (awesome-hallucination-detection: Apache-2.0, Awesome-LLM-hallucination: MIT). ### Where can I find alternatives to awesome-hallucination-detection or Awesome-LLM-hallucination? GraphCanon lists graph-backed alternatives at [awesome-hallucination-detection alternatives](/tools/edinburghnlp-awesome-hallucination-detection/alternatives) and [Awesome-LLM-hallucination alternatives](/tools/luckyyysta-awesome-llm-hallucination/alternatives) ([awesome-hallucination-detection markdown twin](/tools/edinburghnlp-awesome-hallucination-detection/alternatives.md), [Awesome-LLM-hallucination markdown twin](/tools/luckyyysta-awesome-llm-hallucination/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/edinburghnlp-awesome-hallucination-detection-vs-luckyyysta-awesome-llm-hallucination.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-hallucination-detection or Awesome-LLM-hallucination? awesome-hallucination-detection: Active. Awesome-LLM-hallucination: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for awesome-hallucination-detection and Awesome-LLM-hallucination? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-hallucination-detection trust report](/tools/edinburghnlp-awesome-hallucination-detection/trust); [Awesome-LLM-hallucination trust report](/tools/luckyyysta-awesome-llm-hallucination/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=edinburghnlp-awesome-hallucination-detection`](/api/graphcanon/graph?tool=edinburghnlp-awesome-hallucination-detection) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_