How do I implement active learning in the context of Generative AI to optimize model training with limited labeled data

Question

With the help of code can you tell me How do I implement active learning in the context of Generative AI to optimize model training with limited labeled data?

Answer 1

To implement active learning in Generative AI with limited labeled data, use uncertainty sampling to iteratively select the most informative samples for labeling and model fine-tuning.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uncertainty Sampling – Selects the most uncertain samples for human annotation.
• Multiple Response Variability – Uses variance in generated responses to estimate uncertainty.
• Active Learning Loop – Iteratively selects and labels uncertain data for improved model training.
• Human-in-the-Loop Optimization – Ensures only the most useful data is labeled, reducing costs.
• Fine-Tuning Integration – Prepares labeled data for GPT-3 fine-tuning via OpenAI API.

Hence, active learning optimizes model training in Generative AI by prioritizing uncertain samples for labeling, reducing annotation effort while improving model performance with minimal labeled data.