by neya 2 hours ago
What is the methodology of your benchmark?
On the contrary, I personally think these broader benchmarks are meaningless. I think personalized benchmarks are the way to go. They should answer "How does this model perform for MY use-case?" rather than trying to answer "How does this model perform across all coding environments?"
Case in point: I use Elixir which is not as popular as Python, is always a hit or miss with most SOTA models at the top of these benchmarks. Whereas, the ones in the middle of the benchmarks (like the GLM) almost always outperform even SOTA models from Google / Anthropic. However, this is relevant only for my use case and I wouldn't just advocate a model for everyone based off my use-case alone.
We use a rotating pool of ~100 games for the coding parts of the benchmark, and are scored objectively based on ratings similar to Elo. Models write code submissions to interact with the environment, then are evaluated in large batches against other submissions.
We test 11 popular/interesting languages (you can see the Languages chart to filter), but not Elixir -- although other evaluations have found that many LLMs solve more problems when working with Elixir [0]. Why models write code well in some languages over others seems to go beyond pre-training data (Python scores quite low for most models) and we don't fully understand it.
[0] https://elixirforum.com/t/llm-coding-benchmark-by-language/7...
Thanks!