Researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec Artificial Intelligence Institute have developed a new method for ensuring that AI-generated codes are more accurate and useful. In the paper, the researchers used Sequential Monte Carlo (SMC) to “tackle a number of challenging semantic parsing problems, guiding generation with incremental static and dynamic analysis.” Sequential Monte Carlo refers to a family of algorithms that help figure out solutions to filtering problems. This method spans various programming languages and instructs the LLM to adhere to the rules of each language. The group found that by adapting new sampling methods, AI models can be guided to follow programming language rules and even enhance the performance of small language models (SLMs), which are typically used for code generation, surpassing that of large language models. João Loula, co-lead writer of the paper, said that the method “could improve programming assistants, AI-powered data analysis and scientific discovery tools.” It can also cut compute costs and be more efficient than reranking methods. Key features of adapting SMC sampling to model generation include proposal distribution where the token-by-token sampling is guided by cheap constraints, important weights that correct for biases and resampling which reallocates compute effort towards partial generations.