T5 and Flan-T5: The Text-to-Text Framework That Powers Many LLMs
T5 unifies all NLP tasks as sequence-to-sequence text generation, and Flan-T5 extends this with instruction tuning across 1800+ tasks, making it a practical base for fine-tuning custom generation tasks.
The T5 paper introduced a simple but powerful idea: frame every NLP task as converting input text to output text. Translation, summarization, classification, and question answering all become the same type of problem:
Classification: "classify sentiment: This product is terrible." → "negative"
Translation: "translate English to French: The weather is nice." → "Le temps est beau."
This unified interface enables multitask learning - T5 can be trained on all tasks simultaneously - and makes fine-tuning for new tasks straightforward.
T5 vs BERT for Generation Tasks
BERT is an encoder-only model; it cannot generate text. For any task requiring generated output (summarization, translation, question generation, text completion), T5 or another encoder-decoder model is the correct baseline - not BERT or RoBERTa.
Use BERT/RoBERTa for: classification, named entity recognition, token classification.
Use T5 for: summarization, translation, question answering with long answers, any generative task.
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Flan-T5 is T5 instruction-tuned on 1800+ task datasets from FLAN (Fine-tuned Language Net). This dramatically improves zero-shot and few-shot performance on unseen tasks. Flan-T5-XL (3B) outperforms GPT-3 (175B) on several benchmarks - instruction tuning compensates for scale difference.
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Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
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