arXiv:2501.07892v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicity and effectiveness. However, few-shot methods depend on curated or manually crafted reference examples, limiting their applicability in data-free coding scenarios such as real-world data-free coding scenarios and benchmarks without training sets. Existing methods that generate reference examples via recitation or analogy cannot guarantee their authenticity or accuracy. Inspired by human metamemor
This paper leverages metamemory, a cognitive capability, suggesting a new path for LLMs to generate reliable code references without needing pre-existing data, addressing a current limitation in AI development.
Improved data-free code generation enhances the autonomy and applicability of LLMs in novel or proprietary environments, reducing reliance on extensive and potentially sensitive training data.
LLMs can now generate more authentic and accurate reference examples themselves, expanding their utility into scenarios where curated datasets are unavailable or impractical, making them more self-sufficient in code generation.
- · AI developers
- · Software engineering companies
- · LLM providers
- · Startups in specialized coding domains
- · Manual code example curators
- · Legacy code generation platforms
Increased efficiency and accuracy of code generation in data-scarce environments due to LLMs' enhanced self-sufficiency.
Accelerated development of domain-specific software solutions without the need for large, pre-existing codebases.
Potential for an exponential increase in AI-generated software with minimal human oversight, leading to novel applications and significant productivity gains.
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Read at arXiv cs.AI