Scaling Instruction-Finetuned Language Models

Summary

This paper investigates the impact of scaling instruction-tuned language models, focusing on the Flan-T5 and PaLM families. It explores how the performance of language models improves with increasing model size and training data when fine-tuned on a diverse set of instruction-based tasks. The research demonstrates significant improvements in zero-shot and few-shot performance across various benchmarks as model size and training data scale. It introduces and analyzes different prompting strategies and instruction tuning methodologies. The authors also discuss the architectural and training choices contributing to these performance gains, highlighting the importance of dataset diversity and scale. The paper provides evidence for the benefits of instruction tuning for general-purpose language models and provides insights for future scaling efforts.

Key Takeaways

1. Scaling instruction-tuned models, such as Flan-T5 and PaLM, leads to substantial performance gains in few-shot and zero-shot settings.
2. The diversity and size of the instruction tuning dataset are critical factors in achieving strong performance across different tasks.
3. Instruction tuning improves the generalization ability of language models, allowing them to perform well on unseen tasks.
4. The paper provides a framework for understanding and evaluating the benefits of scaling language models with instruction tuning.