Predicting Emergent Capabilities by Finetuning
Authors: name: Charlie Snell, name: Eric Wallace, name: Dan Klein, name: Sergey Levine

A fundamental open challenge in modern LLM scaling is the lack of
understanding around emergent capabilities. In particular, language model
pretraining loss is known to be highly predictable as a function of compute.
However, downstream capabilities are far less predictable -- sometimes even
exhibiting emergent jumps -- which makes it challenging to anticipate the
capabilities of future models. In this work, we first pose the task of
emergence prediction: given access to current LLMs that have random few-shot
accuracy on a task, can we predict whether future models (GPT-N+1) will have
non-trivial accuracy on that task? We then discover a simple insight for this
problem: finetuning LLMs on a given task can shift the point in scaling at
which emergence occurs towards less capable models. To operationalize this
insight, we can finetune LLMs with varying amounts of data and fit a parametric
function that predicts when emergence will occur (i.e., "emergence laws"). We
validate this approach using four standard NLP benchmarks where large-scale
open-source LLMs already demonstrate emergence (MMLU, GSM8K, CommonsenseQA, and
CoLA). Using only small-scale LLMs, we find that, in some cases, we can
accurately predict whether models trained with up to 4x more compute have
emerged. Finally, we present a case study of two realistic uses for emergence
prediction.