思维概率程序
Probabilistic Programs of Thought
AI总结：
本文提出思维概率程序框架，通过利用生成程序的分布，减少GPU计算开销，提升代码生成和数学推理任务的效率。
摘要：
LLM广泛用于代码生成和数学推理任务，需生成结构化输出。传统方法需多次采样生成程序，但计算成本高。本文提出思维概率程序框架，通过生成程序的分布，构建紧凑的概率程序，从而以更少的LLM生成次数获得更多样本，提升性能。
英文摘要：
LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to reason about code, generate code for a given specification, or reason using programs of thought. The typical approach to code generation is to prompt the model and generate samples until an appropriate program is obtained. Within this process, sampling $n$ programs from the language model requires $n$ GPU compute-intensive generations which becomes prohibitively expensive for larger values of $n$. In this work, we address this limitation by exposing the LLM's distribution within the generated programs themselves. We propose a novel test-time framework we dub probabilistic programs of thought to obtain more samples from the model with fewer LLM generations. Given a program generated by a model and the associated next-token probabilities, we build a probabilistic program that compactly represents exponentially many deterministic programs. Since performing probabilistic reasoning in this probabilistic program is much cheaper, our approach allows sampling new programs without any additional GPU compute and little CPU overhead. We instantiate our approach on benchmarks for code generation, code understanding and mathematical reasoning and report improvements in performance with fewer generations from the LLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
