Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API

Summary

Limited labeled data often prevents machine learning models from achieving high performance during traditional fine-tuning. Few-shot learning addresses this hurdle in natural language processing by supplying a short task description and a few demonstration examples at inference time. EleutherAI developed GPT-Neo, an open transformer-based model trained on the Pile dataset, which can execute few-shot tasks via the Hugging Face Accelerated Inference API. Compared to larger models like GPT-3, the 2.7B parameter version of GPT-Neo requires three to four examples alongside tuned hyperparameters like temperature and stopping sequences to generate accurate completions. Because few-shot learning relies heavily on pre-trained associations, careful output monitoring and human feedback mechanisms remain necessary to prevent automated bias.

Context

In many machine learning applications, the scarcity of available labeled data limits the ability to train or fine-tune high-performing models accurately on specific tasks.

Approach / What changed

Use few-shot learning via the Hugging Face Accelerated Inference API with EleutherAI's GPT-Neo model by providing a task description, a few input-output examples, and a prompt directly at inference time.

Takeaways

• Few-shot NLP prompts require three primary elements: a short task description, representative examples, and an incomplete prompt for the model to finish.
• Because GPT-Neo 2.7B is roughly 60 times smaller than GPT-3 175B, it generally requires three to four examples to grasp a task and respect stopping sequences.
• Few-shot models can propagate pre-training biases into classification tasks, making user opt-outs, feedback mechanisms, and failure monitoring essential.

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