Summary of Catcode: a Comprehensive Evaluation Framework For Llms on the Mixture Of Code and Text, by Zhenru Lin et al.

CatCode: A Comprehensive Evaluation Framework for LLMs On the Mixture of Code and Text

by Zhenru Lin, Yiqun Yao, Yang Yuan

First submitted to arxiv on: 4 Mar 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper proposes a novel evaluation framework for large language models (LLMs), such as ChatGPT, in solving coding problems. Current methods are limited or lack standardization, which can lead to an incomplete understanding of the models’ abilities. The proposed framework, called CatCode, uses category theory to represent code debugging and transformation, code translation, and code generation, explanation, and reproduction. This comprehensive evaluation framework assesses the coding abilities of LLMs, including ChatGPT, Text-Davinci, and CodeGeeX.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Imagine a machine that can write and fix computer code, just like a human programmer! Researchers want to know how good these machines are at doing this. They’re proposing a new way to test these machines, using something called “category theory”. This will help us understand what these machines can do and how well they do it. The new method is called CatCode, and it’s designed to test ChatGPT, Text-Davinci, and CodeGeeX, among others.

Keywords

» Artificial intelligence » Translation

CatCode: A Comprehensive Evaluation Framework for LLMs On the Mixture of Code and Text

by Zhenru Lin, Yiqun Yao, Yang Yuan

Categories

GrooveSquid.com Paper Summaries

Keywords

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