  Large Language Models have many methods for solving the same problem. This
introduces novel strengths (different methods may work well for different
problems) and weaknesses (it may be difficult for users to know which method to
use). In this paper, we introduce Multi-Method Self-Training (MMST), where one
method is trained on the filtered outputs of another, allowing us to augment
the strengths and ameliorate the weaknesses of each method. Using a 176B
parameter model trained on both language and code, we show that MMST can 1)
improve the less performant method (up to 30%) making the model easier to use,
2) improve the more performant method (up to 32.2%) making the model more
performant, and 3) improve the performance of related but distinct tasks (up to
10.3%) by improving the ability of the model to generate rationales. We then
conduct ablation analyses to explore why MMST works. We show that MMST
generates more data than traditional self-training, but the improvement in
performance is driven by the use of multiple methods. We also analyze
prompt-engineering and anti-correlated performance between methods as means of
making MMST more effective. We hope the evidence from our paper motivates
machine learning researchers to explore ways in which advances in language
models allow for new forms of training.
