  We investigate the predictability of large language model (LLM) capabilities:
given records of past experiments using different model families, numbers of
parameters, tasks, and numbers of in-context examples, can we accurately
predict LLM performance on new experiment configurations? Answering this
question has practical implications for LLM users (e.g., deciding which models
to try), developers (e.g., prioritizing evaluation on representative tasks),
and the research community (e.g., identifying hard-to-predict capabilities that
warrant further investigation).
  We study the performance prediction problem on experiment records from
BIG-bench. On a random train-test split, an MLP-based predictor achieves an
$R^2$ score greater than 95%, indicating the presence of learnable patterns
within the experiment records. We then formulate the problem of searching for
"small-bench," an informative subset of BIG-bench tasks from which the
performance on the full set can be maximally recovered. We find a subset as
informative as BIG-bench Hard for evaluating new model families, while being
$3\times$ smaller. Additionally, we find competitive subsets by clustering task
representations learned by our MLP-based predictor and selecting tasks close to
cluster centroids, highlighting the importance of task diversity in
constructing "small-bench."
