Supervised fine-tuning

Supervised fine-tuning of generative AI models involves training a pre-existing model on a labelled dataset to improve its performance on specific tasks. This process helps the model generate outputs that are more aligned with desired outcomes, whether that’s producing coherent text, generating relevant images, or other applications.

    1. Language Models (e.g., GPT-3, T5, BERT):

  • Objective: Fine-tuning these models for tasks like text generation, summarization, question answering, translation, or sentiment analysis.
  • Process:
  • Pre-train on a large corpus of general text.
  • Fine-tune on a smaller dataset with labeled examples for a specific task (e.g., fine-tuning a model for movie review sentiment analysis).

    2. Image Generation Models (e.g., GANs, VAEs):

  • Objective: Fine-tuning models like GANs or VAEs to generate domain-specific images, such as generating realistic images of products or medical scans.
  • Process:
  • Pre-train on a large dataset of generic images.
  • Fine-tune on a smaller dataset of task-specific images (e.g., fine-tuning a GAN to generate images of birds or medical X-rays).

    3. Audio Generation Models (e.g., WaveNet, Tacotron):

  • Objective: models like WaveNet or Tacotron to generate audio with specific characteristics (e.g., speech with a particular accent or emotion).
  • Process:
  • Pre-train on a general audio dataset.
  • Fine-tune on a dataset that contains specific speech data (e.g., a dataset of customer service calls for a voice assistant).

    Example: Fine-Tuning GPT-3 for a Specific Task (Customer Support Chatbot)

    1. Pre-training: GPT-3 is trained on vast amounts of text from various sources to learn general language patterns.
    2. Fine-tuning: Fine-tune GPT-3 on a dataset of customer service interactions. The dataset could contain customer inquiries and responses (labeled pairs of input and output).
    3. Training Objective: model learns to generate contextually appropriate responses to customer queries.
    4. Evaluation: the fine-tuned model on a held-out set of customer service conversations to evaluate how well it generates relevant and accurate responses.