LLM-eval-survey
The official GitHub page for the survey paper "A Survey on Evaluation of Large Language Models".
Star Growth
Overview
LLM-eval-survey is a comprehensive academic repository that serves as the official companion to the survey paper 'A Survey on Evaluation of Large Language Models'. This resource provides a systematically organized collection of papers, methodologies, and evaluation frameworks for assessing large language models across multiple domains. The repository covers six major evaluation categories: natural language processing tasks, robustness and trustworthiness assessments, social science applications, natural science and engineering use cases, medical applications, and agent-based applications. Developed through collaboration between prestigious institutions including Jilin University, Microsoft Research, Carnegie Mellon University, Westlake University, and Peking University, this resource addresses the critical need for standardized LLM evaluation approaches. The repository is actively maintained and encourages community contributions through pull requests and issues, ensuring it remains current with the rapidly evolving field of LLM evaluation. With over 1,500 GitHub stars, it has become a go-to reference for researchers, practitioners, and organizations seeking to implement rigorous evaluation methodologies for their language models. The resource is particularly valuable because it bridges the gap between academic research and practical implementation, providing both theoretical frameworks and concrete evaluation tools that can be applied across various industries and research contexts.
Deep Analysis
Comprehensive survey and curated collection of LLM evaluation papers and resources organized by what, where, and how to evaluate
⚡ Capabilities
- • survey-paper
- • evaluation-benchmarks
- • taxonomy
- • resource-collection
- • multi-domain-coverage
✓ Best For
- ✓ llm-evaluation-research
- ✓ finding-evaluation-benchmarks
- ✓ understanding-eval-landscape
✗ Not Ideal For
- ✗ running-evaluations
- ✗ production-benchmarking
- ✗ non-researchers
Deployment
⚠ Known Limitations
- ⚠ paper-and-resource-collection-only
- ⚠ no-runnable-code
- ⚠ requires-manual-updates
Pros
- + Comprehensive coverage of LLM evaluation across diverse domains including NLP, ethics, science, and medical applications
- + Backed by authoritative survey paper from leading academic institutions and Microsoft Research
- + Actively maintained with community contributions and real-time updates beyond the original arXiv publication
Cons
- - Primarily academic resource focused on papers and methodologies rather than ready-to-use evaluation tools
- - May require significant domain expertise to effectively implement the suggested evaluation frameworks
- - Limited practical implementation guidance for organizations without strong research backgrounds
Use Cases
- • Academic researchers developing new LLM evaluation methodologies or benchmarking existing approaches
- • AI practitioners seeking comprehensive evaluation frameworks to assess model performance across multiple dimensions
- • Organizations implementing responsible AI practices who need systematic approaches to evaluate model robustness, bias, and trustworthiness