Codex Math QA

使用Python编程通过codex-davinci-002解决math_qa问题。

Since OpenAI decided to shut off code-davinci-002 behind Azure ，此数据集旨在分享code-davinci-002 OpenAI的176B代码生成模型的生成结果。

数据拆分

dataset = load_dataset("theblackcat102/codex-math-qa", "main")

示例：

the average weight of 8 person ' s increases by 1.5 kg when a new person comes in place of one of them weighing 75 kg. what might be the weight of the new person ?
Write a short snippet of python function which solve this problem. No need to explain the answer, let the code output the answer.

输出

def average_weight(weights):
    total_weight = sum(weights)
    return total_weight / len(weights)

def new_person_weight(weights):
    average_weight = average_weight(weights)
    new_person_weight = average_weight + 1.5
    return new_person_weight

weights = [75, 80, 85, 90, 95]
print(new_person_weight(weights))

dataset = load_dataset("theblackcat102/codex-math-qa", "rational")

示例：

one copy machine can make 20 copies a minute, and a second copy machine makes 10 copies a minute. if the two copiers work together, how long would it take them to make 900 copies ?
Rationale: "total work done by both machines in a minute = 20 + 10 = 30 copies total number of copies required = 900 time = 900 / 30 = 30 mins answer d"
Write a short snippet of python function which solve this problem within 30 lines of code. Add comments in the function block for explaination.

输出

def copy_machine(copies, copy_speed):
    """
    :param copies: number of copies required
    :param copy_speed: speed of the copy machine
    :return: time in minutes
    """
    total_work = sum(copy_speed)
    time = copies / total_work
    return time


if __name__ == "__main__":
    print(copy_machine(900, [20, 10]))

注意事项：

生成的结果未经验证，即来自codex-davinci-002的实际输出。因此，大部分答案都是不正确的，且代码存在语法错误。然而，这是未来研究的工作，此数据集的目的是为codex-davinci-002提供基于代码的数学答案的来源或参考。

数据集创建

数据集来自 math_qa 并在节的末尾追加提示以生成答案的Python解决方案。这是为了提供galactica中的工作卸载数据集的目标。

code-davinci-02的生成配置如下：

引用信息

@inproceedings{amini-etal-2019-mathqa,
    title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms",
    author = "Amini, Aida  and
      Gabriel, Saadia  and
      Lin, Shanchuan  and
      Koncel-Kedziorski, Rik  and
      Choi, Yejin  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1245",
    doi = "10.18653/v1/N19-1245",
    pages = "2357--2367",
}