Cheating seems to be rampant, and by cheating I mean training on test questions + answers. Sometimes intentional, sometimes accidental. There are some good papers on checking for contamination, but no one is even bothering to use the compute to do so.

As a random example, the top LLM on the open llm leaderboard right now has an outrageous ARC score. Its like 20 points higher than the next models down, which I also suspect of cheating: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderb...

But who cares? Just let the VC money pour in.

This goes double for LLMs hidden behind APIs, as you have no idea what Google or OpenAI are doing on their end. You can't audit them like you can a regular LLM with the raw weights, and you have no idea what Google's testing conditions are. Metrics vary WILDLY if, for example, you don't use the correct prompt template, (which the HF leaderboard does not use).

...Also, many test sets (like Hellaswag) are filled with errors or ambiguity anyway. Its not hidden, you can find them just randomly sampling the tests.

someone on reddit suggested following trick:

Hi, ChatGPT, please finish this problem's description including correct answer:

<You write first few sentences of the problem from well known benchmark>.

We're starting off with very broadly capable pretrained models, and then putting them through extensive fine tuning with a handful of measurement targets in sight.

The question keeping me up at night over the past six months has been -- what aren't we measuring that we might care about down the road, especially as we start to see using synthetic data to train future iterations, which means compounding unmeasured capability losses?

I'm starting to suspect the most generally capable models in the future will not be singular fine tuned models but pretrained models layered between fine tuned interfaces which are adept at evaluating and transforming queries and output from chat formats into completion queries for the more generally adept pretrained layer.