PAL: Program-aided Language Models

PAL: Program-aided Language Models

ArXiv:https://arxiv.org/pdf/2211.10435

GitHub:https://reasonwithpal.com/

一、动机

  • 大模型与Chain-of-Thought可以很好地将一些复杂的问题分解为若干个子问题并进行逐步推理;
  • 但是对于一些较为复杂的数学运算,活着涉及到交大的数字符号运算时,即便大模型对任务的规划和分解思考是正确的,但是依然会存在计算错误,这些错误通常大模型很难很好地解决;
  • 因此,本文思考将一些复杂的推理任务先用大模型进行分解,其次让大模型生成python代码并基于解释器来实现计算。通过引入代码,可以弥补由于计算错误所带来的问题。

This bridges an important gap in chain-of- thought-like methods, where reasoning chains can be correct but produce an incorrect answer.

二、方法

本文提出Program-Aided Language Model(PAL)。

相比于Chain-of-thought,每一个exemplar中包含一个推理路径,这个推理路径时融合了自然语言和python代码。且最终只提供完整的变成代码,不提供最终答案。大模型在该prompt的引导下对目标测试样本进行推理和代码生成,最终借助python解释器获得最终答案。

下图展示了一个同时含有自然语言和python代码的推理路径:

PAL方法与CoT的对比图如下所示:

Exemplar的构建

对于评测数据集中,如果现有的工作如果已经提供了exemplar,则直接使用,否则则随机采样3~6个标注样本作为exemplar。

推理路径中的代码函数名称也要与原始变量名保持一致,采用下划线分割的形式定义。

For example, a variable that describes the number of apples in the basket should have a name such as num apples in basket. This keeps the generated code linked to the entities in the question.

三、实验

数据集:

  • 数学运算:GSM8K、SVAMP、ASDIV、MAWPS;
  • 符号推理:BBH-Hard
  • 算法推理:BBH-Hard

GSM8K-Hard:

作者通过启发式更改数字的方式构建了一个新的数据集,并基于这个数据发现50%的情况下大模型虽然给出正确的推理思路但是由于交大的数字计算存在错误导致最终预测错误。

符号推理的prompt样例:

数学运算实验结果:

四、实现

Prompt

针对数学运算、符号推理、算法运算三种类型的任务分别设计了带有编程语言和自然语言的prompt。

python 复制代码
MATH_CHAT_BETA_PROMPT = '''
Let's use python to solve math problems. Here are three examples how to do it,
Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?

def solution():

"""Olivia has 23. She bought five bagels for 3 each. How much money does she have left?"""

money_initial = 23

bagels = 5

bagel_cost = 3

money_spent = bagels * bagel_cost

money_left = money_initial - money_spent

result = money_left

return result

复制代码
Q: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?

def solution():

"""Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""

golf_balls_initial = 58

golf_balls_lost_tuesday = 23

golf_balls_lost_wednesday = 2

golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday

result = golf_balls_left

return result

复制代码
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?

def solution():

"""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

复制代码
How about this question?
Q: {question}
'''.strip()
相关推荐
摇滚侠8 小时前
AI 编程工具 《TRAE 官方手册》阅读笔记 AI 编程核心 上
人工智能·笔记
一个王同学8 小时前
从零到一 | CV转多模态大模型 | week19 | 基于 FastAPI 和 vLLM 的多模态大模型部署
人工智能·深度学习·计算机视觉·fastapi·改行学it·vllm
m沐沐9 小时前
【深度学习】YOLOv2目标检测算法——改进点、网络结构与聚类先验框解析
人工智能·pytorch·深度学习·算法·yolo·目标检测·transformer
企业老板ai培训9 小时前
破解中小企业AI变现难:2026年企业AI培训与陪跑行业趋势深度报告,为何从‘陪跑’到‘变现’才是关键?
大数据·人工智能
神奇霸王龙10 小时前
国产音乐视频 Prompt 三段式屠夫榜:5 个国产视频模型实测对比
人工智能·ai·prompt·音视频·agent·ai编程·agi
OpenCSG10 小时前
Hugging Face遭遇AI Agent攻击:AI资产管理正在进入新阶段
人工智能·大模型
神奇霸王龙10 小时前
AI视频Prompt结构化实战指南:可灵/万相/豆包
人工智能·ai·prompt·aigc·音视频·ai编程
aax121345310 小时前
VOC 集群治理工程实操|美丽蓝天绿岛项目 RTO 工艺选型、管控方案与申报要
大数据·数据库·人工智能
Scott9999HH11 小时前
【工控选型破局】拒绝盲目溢价与低质陷阱:基于 Modbus-RTU 状态诊断与自适应滑动中值滤波的 C++ 实战,兼论“性价比高又好用的仪器仪表厂家”选型之道
人工智能
To_OC11 小时前
调了一上午 DeepSeek 参数,我终于摸透了 temperature 和 Top K 的真实作用
人工智能·llm·deepseek