AI Evaluation

AI Benchmark

Good benchmark principles

• Make sure you can detect a 1% improvement

• Easy to understand the result

• Hard enough (SOTA model cannot do it)

• Use a standard metric and make it comparable over time (do not update often)

Extension

• Can include human baseline

• Includes vetting by others

Validation

• monotonicity

• low variance

To measure is to know, if you cannot measure it, you cannot improve it - Lord Kelvin

OpenAI

OpenAI Evals

https://evals.openai.com/

Central Limit Theorem to fix lacked statistical rigor form Anthropic

Adding Error Bars to Evals: A Statistical Approach to Language...

Evaluations are critical for understanding the capabilities of large language models (LLMs). Fundamentally, evaluations are experiments; but the literature on evaluations has largely ignored the...

https://arxiv.org/abs/2411.00640

Jim Fan on Twitter / X

It is *incredibly* easy to game the LLM benchmarks. Training on test set is for the rookies. Here're some tricks to practice magic at home:1. Train on paraphrased examples of the test set. "LLM-decontaminator" paper from LMSys found that you can beat GPT-4 with a 13B model (!!)… pic.twitter.com/iMKHBJH4eG— Jim Fan (@DrJimFan) September 9, 2024

https://x.com/DrJimFan/status/1833160432833716715

Alex Strick van Linschoten - How to think about creating a dataset for LLM finetuning evaluation

I summarise the kinds of evaluations that are needed for a structured data generation task.

https://mlops.systems/posts/2024-06-25-evaluation-finetuning-manual-dataset.html