Despite recent progress made by large language models in code generation,
they still struggle with programs that meet complex requirements. Recent work
utilizes plan-and-solve decomposition to decrease the complexity and leverage
self-tests to refine the generated program. Yet, planning deep-inside
requirements in advance can be challenging, and the tests need to be accurate
to accomplish self-improvement. To this end, we propose FunCoder, a code
generation framework incorporating the divide-and-conquer strategy with
functional consensus. Specifically, FunCoder recursively branches off
sub-functions as smaller goals during code generation, represented by a tree
hierarchy. These sub-functions are then composited to attain more complex
objectives. Additionally, we designate functions via a consensus formed by
identifying similarities in program behavior, mitigating error propagation.
FunCoder outperforms state-of-the-art methods by +9.8% on average in HumanEval,
MBPP, xCodeEval and MATH with GPT-3.5 and GPT-4. Moreover, our method
demonstrates superiority on smaller models: With FunCoder, StableCode-3b
surpasses GPT-3.5 by +18.6% and achieves 97.7% of GPT-4's performance on
HumanEval. Further analysis reveals that our proposed dynamic function
decomposition is capable of handling complex requirements, and the functional
consensus prevails over self-testing in correctness evaluation.

使用 FunCoder 框架，结合分而治之的策略和函数的共识，递归拆分子函数并将其组合，以应对复杂的需求，并通过功能共识在正确性评估方面具有优势。

分治与共识的结合：释放代码生成中函数的威力

Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in  Code Generation

We present Soda (Symbolic Objective Descriptive Analysis), a language that
helps to treat qualities and quantities in a natural way and greatly simplifies
the task of checking their correctness. We present key properties for the
language motivated by the design of a descriptive language to encode complex
requirements on computer systems, and we explain how these key properties must
be addressed to model these requirements with simple definitions. We give an
overview of a tool that helps to describe problems in an easy way that we
consider more transparent and less error-prone.

我们介绍了 Soda（符号客观描述分析），一种可以自然地处理质量和数量的语言，并极大地简化了检查它们的正确性的任务。我们提出了这种语言的关键特性，通过设计一种描述语言来对计算机系统上的复杂需求进行编码，并解释了如何使用简单的定义来建模这些需求。我们概述了一种工具，可以以更透明、更不容易出错的方式描述问题。