Data Skeptic

Program Aided Language Models

Nov 13, 2023

PhD students Aman Madaan and Shuyan Zhou discuss their paper on Program-Aided Language Models. They talk about the evolution and performance of LLMs on arithmetic tasks. Aman introduces PAL and its improvement on arithmetic tasks. Shuyan explains how PAL's performance was evaluated and the limitations of LLMs. They discuss the potential impact of PAL on math education and future research steps.

32:20

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Episode guests

Aman Madaan

Shuyan Zhou

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Podcast summary created with Snipd AI

Quick takeaways

Program-aided language models (PAL) leverage the strengths of language models and code generation to improve math problem-solving accuracy.

PAL offers advantages over text-based reasoning models by offloading complex arithmetic calculations and providing a structured approach to problem-solving, with potential applications in various subjects.

Deep dives

Large language models struggle with math problems

Large language models, such as GPT, have shown great performance in various domains like coding and graph description. However, when it comes to math problems, they surprisingly perform poorly. This limitation may change in the future, but currently, large language models are not reliable calculators.

Introduction

4min

Generating Code with Limited Success

9min

Program Accuracy Rates and Comparison to Human Scores

2min

Applying Program Aided Language Models in Math Education

7min

Choosing Python and the Trend of End-to-End in Machine Learning

3min

Different Approaches to Prompting in Language Models

8min

We are joined by Aman Madaan and Shuyan Zhou. They are both PhD students at the Language Technology Institute at Carnegie Mellon University. They join us to discuss their latest published paper, PAL: Program-aided Language Models.

Aman and Shuyan started by sharing how the application of LLMs has evolved. They talked about the performance of LLMs on arithmetic tasks in contrast to coding tasks. Aman introduced their PAL model and how it helps LLMs improve at arithmetic tasks. He shared examples of the tasks PAL was tested on. Shuyan discussed how PAL’s performance was evaluated using Big Bench hard tasks.

They discussed the kind of mistakes LLMs tend to make and how the PAL’s model circumvents these limitations. They also discussed how these developments in LLMS can improve kids learning.

Rounding up, Aman discussed the CoCoGen project, a project that enables NLP tasks to be converted to graphs. Shuyan and Aman shared their next research steps.

Follow Shuyan on Twitter @shuyanzhxyc. Follow Aman on @aman_madaan.

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Data Skeptic

Program Aided Language Models

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Large language models struggle with math problems

Using program-aided language models for math problem solving

Advantages of program-aided language models

Future directions and implications

Remember Everything You Learn from Podcasts