代码推理 - PAL

程序辅助语言模型（Program-aided Language Models, PAL）(@gao2022pal) 是另一个MRKL系统的例子。给定一个问题，PAL能够编写代码解决这个问题。它将代码发送到编程运行时以获得结果。PAL的中间推理是代码，而CoT的是自然语言。

PAL 示例 (Gao et al.)

需要注意的是，PAL实际上交织了自然语言（NL）和代码。上面的图片中，蓝色的是PAL生成的自然语言推理。虽然图中没有显示，PAL实际上在每行自然语言推理前生成'#'，以便编程运行时将其解释为注释。

示例

让我们看一个PAL解决数学问题的例子。我使用了一个三样本的提示，这是这个提示(@gao2022pal)的简化版本。

我将使用langchain，这是一个用于链接LLM功能的Python包。首先，需要安装一些程序：

python

!pip install langchain==0.0.26
!pip install openai
from langchain.llms import OpenAI
import os
os.environ["OPENAI_API_KEY"] = "sk-YOUR_KEY_HERE"

然后，我们可以创建一个GPT-3 davinci-002实例（当使用此对象时会进行API调用）：

llm = OpenAI(model_name='text-davinci-002', temperature=0)

这是提示：

MATH_PROMPT = '''

Q: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?

solution in Python:

"""There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""

computers_initial = 9
computers_per_day = 5
num_days = 4  # 4 days between monday and thursday
computers_added = computers_per_day * num_days
computers_total = computers_initial + computers_added
result = computers_total
return result

Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?

solution in Python:

"""Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""

toys_initial = 5
mom_toys = 2
dad_toys = 2
total_received = mom_toys + dad_toys
total_toys = toys_initial + total_received
result = total_toys

Q: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?

solution in Python:

"""Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?"""

jason_lollipops_initial = 20
jason_lollipops_after = 12
denny_lollipops = jason_lollipops_initial - jason_lollipops_after
result = denny_lollipops

solution in Python: '''

现在我们可以将组合好的提示传递给GPT-3。

llm_out = llm(MATH_PROMPT.format(question=question))
print(llm_out)

输出是：

"""Emma took a 60 minute plane ride to seattle. She then took a 2 hour train ride to portland, and then a 30 minute bus ride to vancouver. How long did it take her to get to vancouver?"""

plane_ride = 60
train_ride = 2 * 60  # 2 hours in minutes
bus_ride = 30
total_time = plane_ride + train_ride + bus_ride
result = total_time

最后，我们可以将这段代码传递给 Python 运行时以获得答案：

exec(llm_out)
print(result)

输出是 210，这是正确的答案。

可以在Jupyter 笔记本查看这个例子。

也可以看看 PAL's colab example.

代码推理 - PAL ​

示例 ​

代码推理 - PAL

示例