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EnglishWhat Resources Does the LLM Evaluation Site Offer?
Updated
The LLM Evaluation site (llm-eval.github.io) is a research hub presenting original work on evaluating large language models, conducted by Microsoft Research and collaborating institutes. According to the site's own description, its content was last updated in October 2023. The site organizes its material around four resources: a leaderboard, papers, code, and contact/project information. It is a reference point for evaluation research, not a hosted evaluation service or a tool you run yourself.
A Leaderboard for Comparing Evaluation Results
The site's most practical resource is its leaderboard, which aggregates evaluation results so you can compare model performance across benchmarks in one place. This is useful when you need a quick read on how models stack up rather than assembling numbers from individual papers.
Two conditions matter before relying on it:
- Check the date. The site states an October 2023 update. Leaderboard standings reflect results available at that point, so newer models or benchmarks may not appear.
- Treat rankings as a starting point. A leaderboard condenses results into a table; the methodology behind each entry lives in the linked papers. If a ranking drives a decision, trace it back to the source paper for benchmark details and evaluation setup.
Links to Research Papers
The papers section connects the leaderboard to the underlying research. This is where you find the methodology: which benchmarks were used, how models were prompted, and how results were scored. For anyone reproducing an evaluation or citing a result, the papers are the authoritative layer, and the leaderboard is the summary.
A practical workflow: find a model or benchmark of interest on the leaderboard, open the corresponding paper, and confirm the evaluation conditions match your use case before quoting a number.
Access to Related Code
The code resource provides implementation material tied to the research, which supports reproducing or extending the evaluations. If your goal is to run a comparable evaluation rather than just read results, start here and cross-reference the papers for setup details. The site does not describe installation steps or prerequisites, so expect to work from the repository documentation itself.
Contact and Related Project Information
The site lists a contact (Jindong Wang) and points to related projects on LLM enhancement. This is relevant if you want to ask about methodology, report an issue with a result, or explore work adjacent to evaluation. The contact is a named researcher rather than a general support channel, so correspondence should be specific and research-oriented.
How to Use the Site, Depending on Your Goal
| Your goal | Start with | Then check |
|---|---|---|
| Get a quick comparison of model results | Leaderboard | The date (October 2023) and the linked paper for any result you rely on |
| Understand evaluation methodology | Papers | Code, to see how evaluations were implemented |
| Reproduce or extend an evaluation | Code | Papers for setup and benchmark details |
| Ask about results or related work | Contact / related projects | The papers first, so your question is specific |
What the Site Does Not Provide
The available site description covers research outputs only. It does not indicate a hosted evaluation tool, an API, pricing, or account/login requirements, so do not assume any of these exist. If you need to run evaluations yourself, the code and papers are the route; if you need current model comparisons beyond October 2023, supplement the leaderboard with newer sources.