{"id":868239,"date":"2022-08-08T13:12:22","date_gmt":"2022-08-08T20:12:22","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-08-08T13:12:22","modified_gmt":"2022-08-08T20:12:22","slug":"exploring-and-evaluating-personalized-models-for-code-generation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/exploring-and-evaluating-personalized-models-for-code-generation\/","title":{"rendered":"Exploring and Evaluating Personalized Models for Code Generation"},"content":{"rendered":"<p>Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code. Transformers are usually pre-trained on large unsupervised corpora, learning token representations and transformations relevant to modeling generally available text, and are then fine-tuned on a particular downstream task of interest. While fine-tuning is a tried-and-true method for adapting a model to a new domain &#8212; for example, question-answering on a given topic &#8212; generalization remains an on-going challenge. In this paper, we explore and evaluate transformer model fine-tuning for personalization. In the context of generating unit tests for Java methods, we evaluate learning to personalize to a specific software project using several personalization techniques. We consider three key approaches: (i) <em>custom fine-tuning<\/em>, which allows all the model parameters to be tuned; (ii) <em>lightweight fine-tuning<\/em>, which freezes most of the model&#8217;s parameters, allowing tuning of the token embeddings and softmax layer only or the final layer alone; (iii) <em>prefix tuning<\/em>, which keeps model parameters frozen, but optimizes a small project-specific prefix vector. Each of these techniques offers a trade-off in total compute cost and predictive performance, which we evaluate by code and task-specific metrics, training time, and total computational operations. We compare these fine-tuning strategies for code generation and discuss the potential generalization and cost benefits of each in various deployment scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code. Transformers are usually pre-trained on large unsupervised corpora, learning token representations and transformations relevant to modeling generally available text, and are then fine-tuned on a particular downstream task of interest. While 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