Exploring the Limits of Transfer Learning
T5 (Text-To-Text Transfer Transformer) is a versatile language model that can do many language tasks by turning them into a simple “text-to-text” format.
Here’s how it works:
- Input and Output as Text: In T5, you provide input and expect output in a text format. This means you frame both your question or task and the expected answer as text.
- Universal Framework: T5 uses the same structure for various language tasks, like translation, summarization, question-answering, and more. It treats everything as converting one piece of text into another.
- Training on Lots of Text: T5 becomes smart by learning from tons of text data. It reads books, articles, and websites to understand how language works.
- Generating Text: When you give T5 a task in the text-to-text format, it uses its training to generate an appropriate text-based response. For example, if you want it to translate “Hello” into French, you’d frame it as “Translate: Hello” and expect “Bonjour” as the output.
- Adaptable to Many Tasks: T5 is versatile because you can use it for different language jobs just by phrasing your input and output in the text-to-text format. This flexibility makes it handy for various applications.
- Fine-Tuning: In some cases, people fine-tune T5 for specific tasks. This means giving it extra training for certain jobs to make it even better for them.
- Helpful Everywhere: T5 is used in lots of places, like language translation, content summarization, chatbots, and more. It helps computers understand language tasks and generate text-based answers.
Takeaways:
- Unified Training Framework: A text-to-text framework provides a straightforward method to train a singular model across diverse text tasks, ensuring consistency in loss function and decoding procedures.
- Broad Application: This approach is successfully applied to a range of tasks including generative tasks like abstractive summarization, classification tasks like natural language inference, and regression tasks like STS-B.
- Competitive Performance: Despite its simplicity, the framework matched or outperformed task-specific architectures, achieving state-of-the-art results when augmented with scale.
- Optimized Architectures: The original encoder-decoder form was identified as the most effective within the text-to-text framework, maintaining comparable computational costs while halving total parameter count through parameter sharing.
- Efficient Unsupervised Objectives: Objectives that generate shorter target sequences are recommended for more computational efficiency during unsupervised pre-training.
- Robust Dataset – Colossal Clean Crawled Corpus (C4): The introduced C4 dataset, derived from the Common Crawl web dump, advocates for large, diverse datasets to enhance generic language understanding tasks.
- Effective Training Strategies: Updating all pre-trained model parameters during fine-tuning is highlighted as superior, with multi-task learning explored, pinpointing challenges in setting mixing proportions.
- Scaling Insights: Various scaling strategies were explored, finding that ensembling models or scaling up model size often outperformed simply increasing training data.
- Pushing Performance Boundaries: State-of-the-art results across many benchmarks were achieved by training substantially larger models, leveraging over 1 trillion tokens for training, and introducing novel unsupervised training strategies.
- Open Resources: Released code, the C4 dataset, and pre-trained model weights are provided to foster further research and application in the community.
https://arxiv.org/pdf/1910.10683.pdf
https://blog.research.google/2020/02/exploring-transfer-learning-with-t5.html