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EnglishWhat Is the LLM Evaluation Website?
Updated
The LLM Evaluation website (llm-eval.github.io) is a research hub for original work on evaluating large language models, conducted by Microsoft Research and collaborating institutes. It offers three main resources — a leaderboard, papers, and code — and its content is marked as updated in October 2023. It is best suited to readers who want research-grade evaluation results and artifacts rather than a general-purpose model comparison tool.
What the site provides
The homepage describes the project as "Original research on evaluation of LLMs conducted by Microsoft Research and other collaborated institutes." From that description, the site's scope is narrower than a typical benchmark aggregator: it centers on the team's own evaluation research rather than collecting third-party results.
| Resource | What it is for | When to use it |
|---|---|---|
| Leaderboard | Comparing model performance on the project's evaluations | You want a ranked view of results from this research line |
| Papers | The published methodology and findings behind the evaluations | You need to understand how metrics were defined or check claims |
| Code | Implementation artifacts for reproducing or extending the work | You plan to run or adapt the evaluation yourself |
Who maintains it and how current it is
The site states the research is conducted by Microsoft Research together with collaborating institutes, and lists Jindong Wang as the contact, pointing to related work on LLM enhancement. The page carries an explicit update marker of 2023/10.
That date matters for how you use the site. Treat the leaderboard as a snapshot of results as of that update, not a live ranking. If you need current standings across the newest models, verify against a source with a more recent update before citing numbers.
How to use it for a concrete task
Suppose you need to justify an evaluation metric in a report. A practical sequence:
- Open the Papers section and locate the work defining the metric you plan to cite.
- Check the Leaderboard for the results that metric produced, and note the update date so your citation reflects the right snapshot.
- If you intend to reproduce results, go to Code and confirm the implementation matches the paper version you read.
Expected outcome: you can state which evaluation, which results, and which code version your claim rests on — the three things a reviewer is most likely to question.
Common sticking points
- Assuming the leaderboard is current. The only date signal on the page is 2023/10; confirm recency before treating rankings as up to date.
- Expecting broad model coverage. The site presents this research group's evaluations, so it may not include every model you want to compare.
- Skipping the papers. Leaderboard numbers without the accompanying methodology are easy to misread, especially when comparing across evaluation setups.
Is it the right source for you?
Use it if you want primary research on LLM evaluation, need the papers and code behind specific results, or are building on this team's methodology. Look elsewhere if you need a continuously updated, multi-source leaderboard, or if your question is about a model released after October 2023 — in that case, check whether the site has newer material before relying on it.