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Comment by mbowcut2 | Hacker News Reader

It makes me wonder about the gaps in evaluating LLMs by benchmarks. There almost certainly is overfitting happening which could degrade other use cases. "In practice" evaluation is what inspired the Chatbot Arena right? But then people realized that Chatbot arena over-prioritizes formatting, and maybe sycophancy(?). Makes you wonder what the best evaluation would be. We probably need lots more task-specific models. That's seemed to be fruitful for improved coding.

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The best benchmark is one that you build for your use-case. I finally did that for a project and I was not expecting the results. Frontier models are generally "good enough" for most use-cases but if you have something specific you're optimizing for there's probably a more obscure model that just does a better job.

If you and others have any insights to share on structuring that benchmark, I'm all ears.

There a new model seemingly every week so finding a way to evaluate them repeatedly would be nice.

The answer may be that it's so bespoke you have to handroll every time, but my gut says there's a set of best practiced that are generally applicable.

Generally, the easiest:

1. Sample a set of prompts / answers from historical usage.

2. Run that through various frontier models again and if they don't agree on some answers, hand-pick what you're looking for.

3. Test different models using OpenRouter and score each along cost / speed / accuracy dimensions against your test set.

4. Analyze the results and pick the best, then prompt-optimize to make it even better. Repeat as needed.

How do you find and decide which obscure models to test? Do you manually review the model card for each new model on Hugging Face? Is there a better resource?

Just grab the top ~30 models on OpenRouter[1] and test them all. If that's too expensive make a sample 'screening' benchmark that's just a few of the hardest problems to see if it's even worth the full benchmark.

1. https://openrouter.ai/models?order=top-weekly&fmt=table

Thank you! I'll see about building a test suite.

Do you compare models' output subjectively, manually? Or do you have some objective measures? My use case would be to test diagnostic information summaries - the output is free text, not structured. The only way I can think to automate that would be with another LLM.

Advice welcome!

Yeah - things are easy when you can objectively score an output, otherwise as you said you'll probably need another LLM to score it. For summaries you can try to make that somewhat more objective, like length and "8/10 key points are covered in this summary."

This is a real training method (like Group Relative Policy Optimization), so it's a legitimate approach.

Thank you. I will google Group Relative Policy Optimization to learn about that and the other training methods. If you have any resources handy that I should be reading, that would be appreciated as well. Have a great weekend.

Nothing off the top of my head! If you find anything good let me know. GRPO is a training technique likely not exactly what you'd do for benchmarking, but it's interesting to read about anyway. Glad I cuold help

I don’t think benchmark overfitting is as common as people think. Benchmark scores are highly correlated with the subjective “intelligence” of the model. So is pretraining loss.

The only exception I can think of is models trained on synthetic data like Phi.

If the models from the big US labs are being overfit to benchmarks, than we also need to account for HN commenters overfitting positive evaluations to Chinese or European models based on their political biases (US big tech = default bad, anything European = default good).

Also, we should be aware of people cynically playing into that bias to try to advertise their app, like OP who has managed to spam a link in the first line of a top comment on this popular front page article by telling the audience exactly what they want to hear ;)

Americans have an opposing bias via the phenomenon of "safe edgy", where for obvious reasons they're uncomfortable with being biased towards anyone who looks like a US minority, and redirect all that energy towards being racist to the French. So it's all balanced.

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