Summary of Executing Natural Language-described Algorithms with Large Language Models: An Investigation, by Xin Zheng et al.
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
by Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the ability of large language models (LLMs) to comprehend and execute algorithms described in natural language, a long-standing goal in computer science. To assess this capability, the authors created an algorithm test set from “Introduction to Algorithms” and evaluated popular LLMs’ code execution abilities on 30 randomly selected algorithms. The results show that LLMs, particularly GPT-4, can effectively execute programs described in natural language, as long as they do not involve heavy numeric computation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how large language models (LLMs) can understand and follow instructions written in everyday language, like a recipe or a set of steps. The researchers tested 30 algorithms from a famous computer science book to see if LLMs could perform the tasks described. They found that some LLMs, especially GPT-4, can do this well, as long as the task isn’t too complicated mathematically. |
Keywords
» Artificial intelligence » Gpt
