More accurate coding: Researchers adapt Sequential Monte Carlo for AI-generated code
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Summary
Researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec AI Institute have found a way to make AI-generated code more accurate.
Their method ensures that large language models (LLMs) adhere to the rules of different programming languages, improving the performance of code generation, especially when using small language models.
The researchers used Sequential Monte Carlo (SMC), a family of algorithms that help to solve filtering problems, to bring together constraints that couldn’t previously be incrementally evaluated and to guide generation with incremental static and dynamic analysis.
When tested on various experiments, the method was shown to be more efficient than reranking methods and to improve the performance of small language models compared to larger ones.
The researchers hope this new method could be used to improve programming assistants and AI-powered data analysis and scientific discovery tools, while reducing compute costs.