Papers: arxiv:2210.11416

Scaling Instruction-Finetuned Language Models

Hyung Won Chung ,
Le Hou ,
Shayne Longpre ,
Barret Zoph ,
Yi Tay ,
William Fedus ,
Yunxuan Li ,
Xuezhi Wang ,
Mostafa Dehghani ,
Siddhartha Brahma ,
Albert Webson ,
Shixiang Shane Gu ,
Zhuyun Dai ,
Mirac Suzgun ,
Xinyun Chen ,
Aakanksha Chowdhery ,
Alex Castro-Ros ,
Marie Pellat ,
Kevin Robinson ,
Dasha Valter ,
Sharan Narang ,
Gaurav Mishra ,
Adams Yu ,
Vincent Zhao ,
Yanping Huang ,
Andrew Dai
·published on Oct 20, 2022


Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.


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