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Founder's Pitch
"Auto-customized LLMs for efficient and precise code completion in proprietary repositories."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
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4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research addresses the challenge of adapting large language models to proprietary code bases, which is essential for enhancing productivity and accuracy in enterprise software development.
Product Angle
The product would be a plugin for code editors that integrates seamlessly into enterprise environments, automatically aligning code completions with the company's existing code base style and conventions.
Disruption
This solution could replace traditional manual tuning practices and generic code completion plugins that do not cater to individual code base styles, providing a more tailored and efficient approach.
Product Opportunity
The market includes large software development houses and enterprise IT departments that rely heavily on custom code bases. These entities would pay for tools that significantly boost developer efficiency and maintain code quality standards.
Use Case Idea
Develop an enterprise tool that automatically customizes pre-trained language models to enhance code completion features within proprietary software repositories, reducing development time and increasing accuracy.
Science
The approach involves automating the customization of large language models (LLMs) through semantic scope-based data preparation, allowing them to generate code that is aligned with a proprietary repository's style and standards. This is achieved by fine-tuning the models on specific code patterns and semantic scopes extracted from the target code base.
Method & Eval
The research involved fine-tuning LLMs using semantic scope-based data preparation. The model's performance was evaluated on both proprietary code bases and public benchmarks, demonstrating superior performance in generating precise code consistent with existing styles.
Caveats
The approach relies on accurately identifying semantic scopes, which may be challenging across diverse and complex code bases. Additionally, the customization process requires initial setup and computational resources.
Author Intelligence
Ulrich Finkler
IBM Research
ufinkler@us.ibm.com
Irene Manotas
IBM Research
irene.manotas@ibm.com
Wei Zhang
IBM Research
weiz@us.ibm.com
Geert Janssen
IBM Research
geert@us.ibm.com
Octavian Popescu
IBM Research
o.popescu@us.ibm.com
Shyam Ramji
IBM Research
ramji@us.ibm.com