Summary of Cursorcore: Assist Programming Through Aligning Anything, by Hao Jiang et al.
CursorCore: Assist Programming through Aligning Anything
by Hao Jiang, Qi Liu, Rui Li, Shengyu Ye, Shijin Wang
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed conversational framework integrates various information sources, including coding history, current code, and user instructions, to improve programming assistance tasks. The APEval benchmark evaluates the performance of models in these tasks, while the Programming-Instruct pipeline generates training data from diverse sources like GitHub and online judge platforms. Fine-tuning multiple models leads to the development of the CursorCore series, which outperforms comparable-sized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are used for programming assistance tasks, but there’s room for improvement. A new framework is proposed that combines different types of information to make coding more efficient. To test this idea, a benchmark is created and data is generated using GitHub and online judge platforms. The results show that the new approach performs well and could be useful in real-world applications. |
Keywords
» Artificial intelligence » Fine tuning
