How to Fine-tune LLM to Teach AI Knowledge?

• Blockchain Council
• August 22, 2024

Fine-tuning large language models (LLMs) like ChatGPT is crucial for enhancing their ability to handle specific tasks. This is particularly helpful in domains where precise and specialized knowledge is essential. 

The process involves taking a pre-trained LLM and further training it on a specific dataset tailored to a particular task. This technique enhances the model’s ability to handle tasks that the original training data may not have fully covered. 

But how to fine tune LLM to teach AI knowledge? Let’s find out!

How to Fine-tune LLM to Teach AI Knowledge?

Fine-tuning an LLM involves several steps. Tools like Hugging Face’s transformers library help in this process. Here’s how to fine-tune LLM to teach AI knowledge:

• Model Selection: Choose a model that aligns closely with your task. For instance, LLaMA models are suitable for tasks requiring extensive reasoning, while GPT models might be better for conversational agents.
• Data Preparation: Before fine-tuning, you must prepare a dataset tailored to your specific needs. Clean, annotate, and split the data into training, validation, and test sets. Ensure the dataset is diverse enough to cover various aspects of the task but focused enough to avoid overwhelming the model with irrelevant information.
• Modify Model Architecture: Adjust the model’s architecture, such as adding task-specific layers, to better handle the specific task.
• Fine-Tuning: Use libraries like transformers to fine-tune the model. Do it on the task-specific dataset by updating its weights, monitoring performance to avoid overfitting. Techniques like QLoRA can be implemented by configuring the model to use lower precision during training, which helps manage hardware constraints.
• Tune Hyperparameters: Optimize hyperparameters like learning rate and batch size using techniques like grid search to improve model performance.
• Evaluation: After fine-tuning, evaluate the model using a validation set to ensure it performs well on the intended task. Monitor for issues like overfitting or catastrophic forgetting and adjust the fine-tuning parameters as needed​.

Key Techniques for Fine-Tuning with Examples

Supervised Fine-Tuning

Supervised Fine-Tuning (SFT) is one of the most straightforward methods. It involves training the model on labeled data for a specific task. For example, fine-tuning GPT-2 to classify sentiments in tweets would involve feeding it a dataset where each tweet is labeled as positive, neutral, or negative. The model then learns to predict these labels accurately when given new, unseen tweets.

Example: If a retail chatbot needs to understand patient queries accurately, the model can be fine-tuned using a dataset of retail inquiries paired with their correct interpretations. The result is a chatbot that can understand and respond to retail-related questions more effectively.

Parameter-Efficient Fine-Tuning (PEFT)

PEFT reduces the computational burden by updating only a subset of the model’s parameters. Techniques like LoRA (Low-Rank Adaptation) or QLoRA (Quantized Low-Rank Adaptation) are particularly effective for large models. These methods focus on crucial parameters, making the process more efficient without significant loss of model performance.

Example: In scenarios where hardware resources are limited, such as on smaller GPUs, PEFT can be used to fine-tune a model like GPT-3 for specific tasks like legal document analysis without needing extensive computational power.

Reinforcement Learning with Human Feedback (RLHF)

RLHF fine-tunes a model by incorporating human preferences into the training loop. Instead of relying solely on predefined correct outputs, the model learns from human feedback, making it more aligned with user expectations.

Example: ChatGPT utilizes RLHF to refine its conversational abilities. By training with human feedback, the model learns to generate responses that are not only correct but also contextually appropriate and engaging.

Retrieval-Augmented Fine-Tuning (RAFT)

RAFT combines retrieval-augmented generation (RAG) with fine-tuning to improve the model’s ability to handle domain-specific tasks. In this method, the LLM is fine-tuned on a dataset that includes both relevant and irrelevant documents. The model learns to filter out unhelpful information while leveraging useful context, enhancing its performance in tasks requiring external knowledge integration.

Example: RAFT can be used with models like LLaMA 2 to improve its application in legal or healthcare domains. By fine-tuning the model with domain-specific documents and using Chain-of-Thought reasoning, RAFT helps the model better navigate complex information and generate more accurate responses​.

Transfer Learning

Transfer learning involves taking a model pre-trained on one task and fine-tuning it for another, often related, task. This technique is highly effective when the new task has limited data.

Example: A model pre-trained on general text might be fine-tuned to generate legal summaries. Even with a small dataset of legal documents, transfer learning allows the model to produce accurate and concise legal summaries.

Sequential Fine-Tuning

Sequential fine-tuning gradually adapts a model to increasingly specific tasks. This technique prevents the model from losing its general understanding while becoming more proficient in niche areas.

Example: A model might first be fine-tuned for medical terminology and then further fine-tuned for cardiology-specific language, ensuring it retains broader medical knowledge while excelling in a specialized domain.

In-context Learning and Prompt Engineering

In-context learning is less about changing the model’s weights and more about providing context-specific examples at inference time. This method guides the model to generate responses based on the provided examples. Prompt engineering plays a crucial part in it. To make the most of such fine-tuning techniques, consider enrolling into industry-preferred certifications like the Certified Prompt Engineer™ program.

Example: In a customer support chatbot, providing examples of common customer queries and their resolutions during inference can help the model generate accurate responses without additional fine-tuning. 

Conclusion

Now that you know how to fine-tune LLM to teach AI knowledge, it is important to remember that: Fine-tuning is an essential technique for customizing LLMs to meet specific needs. By understanding and applying various fine-tuning methods, AI developers can enhance the performance of LLMs. However, you must take careful consideration of the challenges involved to ensure the fine-tuned models are robust and reliable in their new roles.