Summary of Easyinstruct: An Easy-to-use Instruction Processing Framework For Large Language Models, by Yixin Ou et al.
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
by Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Yida Xue, Runnan Fang, Kangwei Liu, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 This paper presents EasyInstruct, a modular framework for instruction processing in Large Language Models (LLMs). The authors highlight the importance of instruction tuning, which has gained attention in recent years. They note that existing approaches have led to inconsistencies among different methods, making it difficult for researchers to develop and advance instruction processing techniques. To address this challenge, EasyInstruct offers a comprehensive solution by modularizing instruction generation, selection, and prompting, as well as considering their combination and interaction. The framework is publicly available on GitHub and accompanied by an online demo app and video. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for researchers to work with Large Language Models (LLMs) by creating a special tool called EasyInstruct. Right now, there’s no single way to make LLMs better because different ways of doing things don’t agree. To fix this problem, the authors created a framework that breaks down instruction processing into smaller parts and shows how they work together. This makes it easier for people to use and improve LLMs. |
Keywords
* Artificial intelligence * Attention * Instruction tuning * Prompting
