Finetuned Language Models are Zero-Shot Learners

Overview

This paper introduces instruction tuning, a method for finetuning language models on a collection of datasets formatted as instructions. The approach significantly improves zero-shot task generalization, allowing models to perform new tasks without examples.

Best for

Best for
Researchers and engineers developing or fine-tuning language models for zero-shot task generalization

Use cases

• Training a base language model to follow diverse instructions for zero-shot generalization
• Evaluating zero-shot performance across multiple NLP tasks without per-task fine-tuning
• Benchmarking instruction-following capabilities of large language models

Notes

This paper introduces instruction tuning, a method for finetuning language models on a collection of datasets formatted as instructions. The approach significantly improves zero-shot task generalization, allowing models to perform new tasks without examples.

Use cases

• Training a base language model to follow diverse instructions for zero-shot generalization
• Evaluating zero-shot performance across multiple NLP tasks without per-task fine-tuning
• Benchmarking instruction-following capabilities of large language models

Pros

• Demonstrates a simple and effective way to boost zero-shot learning
• Works across many different tasks and model architectures
• Has become a foundational technique for modern instruction-tuned models

Cons

• Requires large-scale compute and carefully curated multi-task datasets
• May not surpass few-shot performance on all tasks, especially for smaller models
• Potential overfitting to the instruction format if the dataset distribution is narrow

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