Finetuned Language Models are Zero-Shot Learners

Summary

This paper from Google introduces the concept of Finetuned Language Models Are Zero-Shot Learners (FLAN). It investigates the zero-shot capabilities of language models after being fine-tuned on a diverse set of tasks. The study shows that fine-tuning on a collection of tasks, described using natural language instructions, enables the models to perform well on unseen tasks, often outperforming zero-shot baselines. The research focuses on how to instruct language models to perform tasks they haven't explicitly been trained on, using instruction tuning as the primary method. The work highlights the importance of diverse instruction tuning and demonstrates that models can learn from a wide range of instructions and generalize to new, unseen task formats.

Key Takeaways

1. Fine-tuning language models on a diverse set of tasks described by natural language instructions (instruction tuning) significantly improves their zero-shot performance.
2. The paper demonstrates that instruction tuning enables models to follow novel instructions and generalize to new tasks without requiring task-specific training data.
3. The study highlights the importance of the diversity and quality of the instructions used for fine-tuning in achieving strong zero-shot capabilities.
4. The research provides insights into how to design and leverage instruction-based learning for enhancing the generalization ability of language models.