Researchers have developed PriCoder, a novel approach to enhance Large Language Models' (LLMs) ability to generate code that utilizes private libraries. Current methods struggle even with access to API documentation, prompting the creation of PriCoder which synthesizes data to teach LLMs how to effectively invoke these private APIs. The method models data synthesis as a graph construction problem, employing progressive graph evolution for diversity and multidimensional graph pruning for quality. Experiments show PriCoder significantly boosts private-library code generation performance by over 20% without negatively impacting general coding capabilities. AI
IMPACT Enhances LLM capabilities in specialized code generation, potentially improving developer tools and workflows.
RANK_REASON The cluster contains a research paper detailing a new method for LLM code generation. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- Large Language Models
- LLMs
- PriCoder
- ScienceCast
- Yitong Zhang
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