Scaling Instruction-Finetuned Language Models

Overview

This paper introduces a framework for scaling instruction fine-tuning across multiple large language models, including Flan-T5 and Flan-PaLM. It demonstrates that fine-tuning on a diverse set of tasks described via natural language instructions improves zero-shot and few-shot generalization on unseen tasks.

Best for

Best for
Researchers and engineers who want to fine-tune open-source language models on diverse instructions for better zero-shot task performance

Use cases

• Fine-tuning a base language model on a curated instruction dataset for improved task generalization
• Evaluating zero-shot and few-shot performance of instruction-tuned models on held-out benchmarks
• Reproducing the Flan recipe to build custom instruction-following variants of T5 or PaLM

Notes

This paper introduces a framework for scaling instruction fine-tuning across multiple large language models, including Flan-T5 and Flan-PaLM. It demonstrates that fine-tuning on a diverse set of tasks described via natural language instructions improves zero-shot and few-shot generalization on unseen tasks.

Use cases

• Fine-tuning a base language model on a curated instruction dataset for improved task generalization
• Evaluating zero-shot and few-shot performance of instruction-tuned models on held-out benchmarks
• Reproducing the Flan recipe to build custom instruction-following variants of T5 or PaLM

Pros

• Shows consistent performance gains across model scales and architectures
• Provides a clear, reproducible methodology for instruction tuning
• Publicly released Flan-T5 checkpoints enable immediate application

Cons

• Requires substantial compute resources for training at scale
• The instruction dataset composition may not transfer to all domain-specific tasks
• Limited analysis on long-tail or highly specialized instructions

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