Update README.md

README.md

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-# SELF-INSTRUCT: Aligning LM with Self Generated Instructions
-This repository contains code and data for the SELF-INSTRUCT paper, a method for aligning pretrained language models with instructions.
+# Self-Instruct: Aligning LM with Self Generated Instructions
+This repository contains code and data for the [Self-Instruct paper](https://arxiv.org/abs/2212.10560), a method for aligning pretrained language models with instructions.
 
 ## Introduction
-SELF-INSTRUCT is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With SELF-INSTRUCT, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.
+Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.
 
 ### Background
 In recent years, there has been a growing interest in building models that can follow natural language instructions to perform a wide range of tasks. These models, known as "instruction-tuned" language models, have demonstrated the ability to generalize to new tasks. However, their performance is heavily dependent on the quality and quantity of the human-written instruction data used to train them, which can be limited in diversity and creativity. To overcome these limitations, it is important to develop alternative approaches for supervising instruction-tuned models and improving their instruction-following capabilities.
 
-### How SELF-INSTRUCT works?
-The SELF-INSTRUCT process is an iterative bootstrapping algorithm that starts with a seed set of manually-written instructions and uses them to prompt the language model to generate new instructions and corresponding input-output instances. These generations are then filtered to remove low-quality or similar ones, and the resulting data is added back to the task pool. This process can be repeated multiple times, resulting in a large collection of instructional data that can be used to fine-tune the language model to follow instructions more effectively.
+### How Self-Instruct works?
+The Self-Instruct process is an iterative bootstrapping algorithm that starts with a seed set of manually-written instructions and uses them to prompt the language model to generate new instructions and corresponding input-output instances. These generations are then filtered to remove low-quality or similar ones, and the resulting data is added back to the task pool. This process can be repeated multiple times, resulting in a large collection of instructional data that can be used to fine-tune the language model to follow instructions more effectively.
 
-Here is an overview of SELF-INSTRUCT:
+Here is an overview of Self-Instruct:
 
 ![](docs/pipeline.JPG)
 
 
 ## Setup
-Code and data for the SELF-INSTRUCT framework will be made available in this repository soon.
+Code and data for the Self-Instruct framework will be made available in this repository soon.
 
 ## Citation
-If you use the SELF-INSTRUCT framework or data, feel free to cite us. 
+If you use the Self-Instruct framework or data, feel free to cite us. 
 ```bibtex
 @misc{selfinstruct,
-  title={SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions},
+  title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
   author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
-  journal={arXiv preprint arXiv:2212.01349},
+  journal={arXiv preprint arXiv:2212.10560},
   year={2022}
 }
 ```