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Large Language Models have many methods for solving the same problem. Thisintroduces novel strengths (different methods may work well for differentproblems) and weaknesses (it may be difficult for users to know which method touse). In this paper, we introduce Multi-Method Self-Training (MMST), where onemethod is trained on the filtered outputs of another, allowing us to augmentthe strengths and ameliorate the weaknesses of each method. Using a 176Bparameter 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 moreperformant, and 3) improve the performance of related but distinct tasks (up to10.3%) by improving the ability of the model to generate rationales. We thenconduct ablation analyses to explore why MMST works. We show that MMSTgenerates more data than traditional self-training, but the improvement inperformance is driven by the use of multiple methods. We also analyzeprompt-engineering and anti-correlated performance between methods as means ofmaking MMST more effective. We hope the evidence from our paper motivatesmachine learning researchers to explore ways in which advances in languagemodels allow for new forms of training.

Multi-Method Self-Training: Improving Code Generation With Text, And Vice Versa