3 ms·
What else should you train for? If the benchmark dosn't represent real world scenarios, isn't that a problem with the benchmark rather than the model?
by dodslaser 2y ago
What else should you train for? If the benchmark dosn't represent real world scenarios, isn't that a problem with the benchmark rather than the model?
- exitb 2y agoOverfitting is a concern.
- isoprophlex 2y agoIf your benchmark covers all possible programming tasks then you dont need an llm, you need search over your benchmark. Hypothetically let's say the benchmark contains "test divisibility of this integer by n" for all n of the form 3x+1. An extremely overfit llm won't be able to code divisibility for all n not of the form 3x+1, and your benchmark will never tell.
- YetAnotherNick 2y agoNo, because solving a well defined problem with well defined right or wrong is generally not what people use llm for. Most of the times my query to llm is underspecified, and lot of time I figure out the problem when chatting with LLM. And benchmark by definition only measures just right/wrong answer.
- LeoPanthera 2y agoThis is called Goodhart's law, who said: "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." But in modern usage it is often rephrased to: "When a measure becomes a target, it ceases to be a good measure" https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law
- ithkuil 2y agoIt's more subtle than this: https://en.m.wikipedia.org/wiki/Training,_validation,_and_test_data_sets https://en.m.wikipedia.org/wiki/Training,_validation,_and_te...