Advanced fine-tuning
1. Advanced fine-tuning
Welcome back! Let's learn about some advanced techniques.2. Where are we?
Advanced fine-tuning is our final building block for LLMs. It's time to understand how everything comes together to give rise to these colossal models.3. Reinforcement Learning through Human Feedback
So far, we have learned the two-stage process of training an LLM - pre-training and fine-tuning. In this video, we learn about the third phase of LLM training, the Reinforcement Learning through Human Feedback (RLHF) technique. But first, let's quickly revisit pre-training and fine-tuning steps.4. Pre-training
Let's recall that LLMs are pre-trained on large amounts of text data from diverse sources, like websites, books, and articles, using transformer architecture. Its primary goal is to learn general language patterns, grammar, and facts. During this stage, the model learns to predict the next word or missing word using next-word prediction or masked language modeling techniques.5. Fine-tuning
After pre-training, the model is fine-tuned using N-shot techniques (such as zero, few, and multi-shot) on small labeled datasets to learn specific tasks.6. But, why RLHF?
So, why do we need a third technique, RLHF? The concern is that the large general-purpose training data may contain noise, errors, and inconsistencies, reducing its accuracy in specific tasks. For example, when a model is trained on data from online discussion forums, it will have a mix of unvalidated opinions and facts. The model treats this training data as the truth, therefore reducing the accuracy. RLHF introduces an external expert to validate the data and avoid these inaccuracies.7. Starts with the need to fine-tune
While pre-training enables the model to learn underlying language patterns, it may not capture the complexities of language in these specific contexts. During the fine-tuning stage the model's performance can improve using quality labeled data for specific tasks. This is where techniques like RLHF come into play. It is an advanced fine-tuning technique that unlocks the true potential of language models by gathering human feedback.8. Simplifying RLHF
In this approach, the model generates output, which is then reviewed by a human who provides feedback on how well the model performed. The model is then updated based on the feedback to improve its performance over time. Let's break this down into three steps. First, the model generates multiple responses to a given question or prompt based on what it has learned from reading lots of text.9. Enters human expert
Next, a human expert, such as a language teacher or someone who knows the topic well, is presented with these different responses generated by the model. The expert ranks the responses according to their quality, such as their accuracy, relevance, and coherence. This ranking process provides valuable information to the model about which responses are better or worse.10. Time for feedback
Finally, the model learns from the expert's ranking of responses, trying to understand how to generate better responses in the future that align with the expert's preferences. The model continues to generate responses, receive rankings from the expert, and learn from the feedback, improving its ability to provide helpful and accurate information over time.11. Recap
In summary, pre-training an LLM captures general language knowledge, followed by fine-tuning on specific tasks, which is further enhanced with RLHF techniques to incorporate human feedback and preferences. This combination of training methods allows the model to become highly effective at understanding and generating human-like text for various applications.12. Completing the LLM
After the advanced fine-tuning step of RLHF, we completed the training process.13. Let's practice!
This video brings together all that we have learned so far. Let's reinforce these concepts with some exercises.Create Your Free Account
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