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Founder's Pitch
"PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research aims to overcome the limitations of LLMs in generating code using private libraries, a critical challenge in real-world software development scenarios where such libraries are extensively used but rarely included in training data.
Product Angle
Productize PriCoder as a code generation tool that can be integrated with developer platforms to enhance LLMs' ability to use proprietary APIs effectively, especially for enterprise clients that use custom libraries.
Disruption
PriCoder could disrupt current API usage by making it easier for LLMs to generate code involving private libraries, potentially replacing manual API documentation retrieval processes.
Product Opportunity
The solution targets enterprise software development where private libraries are common. Companies would pay for enhanced developer productivity and faster on-boarding by integrating private libraries into LLM training data efficiently.
Use Case Idea
Develop an AI-based IDE plugin that suggests optimized API calls for private libraries, enhancing developer productivity and code reliability.
Science
PriCoder addresses the challenge of teaching LLMs to use private library APIs by synthesizing diverse and high-quality training data. It constructs and refines a graph where nodes represent training samples, progressively increasing diversity and ensuring high quality through a rigorous pruning process.
Method & Eval
The method utilizes a graph-based approach to synthesize and refine training data for private APIs, significantly improving LLM performance on custom code generation tasks, demonstrated by a 20% gain in pass@1 rates on new benchmarks.
Caveats
A potential limitation is the dependence on the accuracy of synthesized data. Poorly synthesized data could degrade performance, and scalability might be limited if the private libraries evolve faster than the model retraining cycle.
Author Intelligence
Yitong Zhang
LEAD Tsinghua University, Proxseer Inc.
zhangyt42@buaa.edu.cn
Chengze Li
Nanjing University
231220004@smail.nju.edu.cn
Ruize Chen
Nanjing University
231250003@smail.nju.edu.cn
Guowei Yang
Proxseer Inc.
gavin@eniacode.com
Xiaoran Jia
Beijing Institute of Technology
jiaxiaoran576@gmail.com
Yijie Ren
Beihang University
23373276@buaa.edu.cn
Jia Li
Tsinghua University
jia_li@mail.tsinghua.edu.cn