FT2Ra: A fine-tuning-inspired approach to retrieval-augmented code completion

• Main Authors: GUO, Qi, LIU, Shangqing, XIE, Xiaofei, TANG, Ze Tang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Code completions; Critical questions; Down-stream; Fine tuning; Language model; Large models; Model prediction; Ode completion; Retrieval-augmented language model; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/9444; https://ink.library.smu.edu.sg/context/sis_research/article/10444/viewcontent/FT2Ra__A_Fine_Tuning_Inspired_Approach_to_Retrieval_Augmented_Code_Completion.pdf
• Institution: Singapore Management University

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream...