arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.14852cs.CLcs.AIcs.LG
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最新 GPT 模型上的 HumanEval——2024

HumanEval on Latest GPT Models -- 2024

Vanderbilt University(范德堡大学)

Daniel Li, Lincoln Murr

AI总结:

本文通过将最新 GPT-4 模型接入 HumanEval 基准,并利用 160 个多步骤提示问题集,验证了多步骤范式合成相比单轮输入能显著提升零样本 Python 代码生成性能。

摘要:

2023 年,我们使用最新的 GPT-4 模型推进程序合成。大型语言模型显著提升了该领域的最新技术水平。为使这些进展更易于获取,我们创建了一个代码仓库,将这些模型与 HumanEval 相连接。该数据集最初是为配合一种名为 CODEGEN 的语言模型在自然语言和编程语言数据上使用而开发的。这些训练模型的实用性通过其在 HumanEval 任务上的零样本 Python 代码生成中相较于以往最先进解决方案所展现的竞争性性能得到了证明。此外,这为开发更多多步骤范式合成铺平了道路。该基准包含 160 个多样化的问题集,被分解为多步骤提示,我们的分析表明,与单轮输入相比,这显著提升了程序合成效果。所有代码均在 https://github.com/daniel442li/gpt-human-eval 开源。

英文摘要:

In 2023, we are using the latest models of GPT-4 to advance program synthesis. The large language models have significantly improved the state-of-the-art for this purpose. To make these advancements more accessible, we have created a repository that connects these models to Huamn Eval. This dataset was initally developed to be used with a language model called CODEGEN on natural and programming language data. The utility of these trained models is showcased by demonstrating their competitive performance in zero-shot Python code generation on HumanEval tasks compared to previous state-of-the-art solutions. Additionally, this gives way to developing more multi-step paradigm synthesis. This benchmark features 160 diverse problem sets factorized into multistep prompts that our analysis shows significantly improves program synthesis over single-turn inputs. All code is open source at https://github.com/daniel442li/gpt-human-eval .

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。

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