Scaling Instruction-Finetuned Language Models

TL;DR

This paper demonstrates that instruction finetuning, especially with increased task diversity, model size, and chain-of-thought data, significantly enhances language models' performance across various tasks and benchmarks.

Contribution

It systematically explores the effects of scaling instruction finetuning in terms of tasks, model size, and data type, achieving state-of-the-art results.

Findings

Flan-PaLM 540B outperforms original PaLM 540B by 9.4% on average.

State-of-the-art 75.2% on five-shot MMLU.

Flan-T5 checkpoints show strong few-shot performance, rivaling larger models.

Abstract

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.