MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning

Recent researchers have explored code generation Nowadays, LLMs have been successfully used to task by using LLMs; however, most studies (Li support developers' daily development, such as et al., 2023b, 2022a; Ahmad et al., 2021; Hu et al., code generation, test generation, etc. However, 2021) focus on generating "correct" code. There existing Code LLMs are usually general models is limited research investigating how to generate trained with large programming corpus (Zheng "personalized" code, especially for multi-user personalization, et al., 2023; Chen et al., 2022), therefore the generated with no research conducted yet. Automatically code is difficult to adapt to personalized and/or generating code according to developers' customized requests. Consider the following practical preferences or projects' consistency is a challenging scenarios: Alice is a software developer. To task: (i) Considering different programmers improve programmers' daily efficiency, her company have their own coding styles, it is too expensive provided the base LLMs that can be used for to fine-tune an LLM for each user (Guo et al., code generation.