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+---
+title: hold
+date: Dec 19, 2023
+---
+(meme)
+## TL;DR
+
+## Background and Related Work
+
+(Def wanna give this a more creative name)
+
+- historically, machine learning research has consisted of researchers intelligently building datasets with hard problems in them to evaluate models' ability to predict the right answer for, whatever that looks like
+- someone comes along, builds a model that generalizes well on the benchmarks, and the cycle repeats itself, with a new, harder dataset being built and released
+- this brings us to today, where datasets like [MMLU](https://arxiv.org/abs/2009.03300), [HumanEval](https://arxiv.org/abs/2107.03374v2), and the hilariously named [HellaSwag](https://arxiv.org/abs/1905.07830)
+- what they all have in common is they're trying to explore a problem space as exhaustively as possible, providing a large number of diverse examples to evaluate on (MMLU - language understanding, HumanEval - coding, HellaSwag - reasoning)
+- high performance on these datasets demonstrates incredible *general* abilities
+	- and in fact their performance on these diverse datasets proves their capabilities are probably much more vast than we think they are
+- but they're not given the opportunity to query these diverse capabilities in current user-facing systems
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