The introduction of large language models has significantly advanced code
generation. However, open-source models often lack the execution capabilities
and iterative refinement of advanced systems like the GPT-4 Code Interpreter.
To address this, we introduce OpenCodeInterpreter, a family of open-source code
systems designed for generating, executing, and iteratively refining code.
Supported by Code-Feedback, a dataset featuring 68K multi-turn interactions,
OpenCodeInterpreter integrates execution and human feedback for dynamic code
refinement. Our comprehensive evaluation of OpenCodeInterpreter across key
benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus
reveals its exceptional performance. Notably, OpenCodeInterpreter-33B achieves
an accuracy of 83.2 (76.4) on the average (and plus versions) of HumanEval and
MBPP, closely rivaling GPT-4's 84.2 (76.2) and further elevates to 91.6 (84.6)
with synthesized human feedback from GPT-4. OpenCodeInterpreter brings the gap
between open-source code generation models and proprietary systems like GPT-4
Code Interpreter.

OpenCodeInterpreter 是一种开源代码系统，通过集成执行和人类反馈进行动态代码改进，从而弥补了开源代码生成模型与专有系统（如 GPT-4 代码解释器）之间的差距。

OpenCodeInterpreter: 将代码生成与执行和细化相整合

OpenCodeInterpreter: Integrating Code Generation with Execution and  Refinement

Using the pre-trained language models to understand source codes has
attracted increasing attention from financial institutions owing to the great
potential to uncover financial risks. However, there are several challenges in
applying these language models to solve programming language-related problems
directly. For instance, the shift of domain knowledge between natural language
(NL) and programming language (PL) requires understanding the semantic and
syntactic information from the data from different perspectives. To this end,
we propose the AstBERT model, a pre-trained PL model aiming to better
understand the financial codes using the abstract syntax tree (AST).
Specifically, we collect a sheer number of source codes (both Java and Python)
from the Alipay code repository and incorporate both syntactic and semantic
code knowledge into our model through the help of code parsers, in which AST
information of the source codes can be interpreted and integrated. We evaluate
the performance of the proposed model on three tasks, including code question
answering, code clone detection and code refinement. Experiment results show
that our AstBERT achieves promising performance on three different downstream
tasks.

本文介绍了一种 AstBERT 模型，该模型基于预训练的编程语言模型，使用抽象语法树从语义和句法两方面来更好地理解金融代码，通过 Alipay 代码库中大量的 Java 和 Python 代码进行训练，并通过代码解析器将抽象语法树信息整合进模型中，最终，在代码问答、代码克隆检测和代码优化三个任务中获得了良好的性能。