Should I fine-tune my Large Language Model?
An executive's guide to fine-tuning: What is it, its benefits & drawbacks, practical alternatives, and how to determine if it is right for you.
ChatGPT is a generalist, not a specialist.
Large Language Models (LLM), like ChatGPT, are great at a wide range of tasks - from answering questions, generating text, translating, and summarizing.
However, when asked to perform tasks that require deep specialized expertise, the model falters.
It produces subpar results or in many cases hallucinates.
Fine-tuning allows you to turn an LLM from a generalist to a specialist.
At its core, fine-tuning involves customizing a language model for specific tasks or domains.
It's a process where an existing LLM is further trained on a smaller, task-centric dataset. Thus, optimizing its performance in understanding domain-related questions and providing more accurate responses.
For example,
Customer Support Chatbots: When trained on historical customer interactions, these bots excel in responding to queries specific to an organization's products and services.
Legal Document Analysis: Models fine-tuned on legal documents, can generate accurate, concise, and relevant legal summaries.
Code Generation Tools: Models trained on code snippets, can now suggest code completions based simply on syntax.
Fine-tuing has lots of benefits but it also has a few fundamental drawbacks.
There are three main advantages to fine-tuning.
Improved Task-Specific Performance: Fine-tuning allows LLMs to excel in specific tasks by adapting to the nuances of the target application or domain.
Better Accuracy: Significant enhancement in accuracy and reduction of hallucinations as the model learns from private task-specific data.
Cheaper & Faster: Building private LLMs is expensive and time-consuming. Fine-tuning is a shortcut, saving both time and resources.
But it also comes with a set of drawbacks.
Data Dependency: Success depends on the availability and quality of task-specific data. It does not work in data-scarce scenarios.
Overfitting Risk: Fine-tuning may lead the model to over-index on the task-specific data. Ultimately, reducing the model's ability to respond to diverse inputs.
Resource Intensive: While less demanding than building a new LLM from scratch, fine-tuning does require time, expertise, and investment.
It is also not the only game in town.
Fine-tuning is not the only approach to customization. Alternatives include:
Ensemble Learning: Combine predictions from multiple pre-trained models to enhance overall performance. This approach doesn't involve individual fine-tuning but capitalizes on diverse models for improved results.
Feature-Based Extraction (Fine Tuning Lite): Train only the top 1 - 2 layers of the model. It helps drive customization without the cost, effort, and complexity of retraining the entire model.
Human-in-the-Loop Approaches: Combine machine-generated content with human oversight for more nuanced and controlled outputs. This ensures a balance between automated language generation and human expertise.
Prompt Engineering (my favorite): You can do a lot with good prompt engineering. A well-crafted prompt (the right question, asked the right way, with proper context) can produce highly customized results.
Retrieval-Augmented Generation (RAG): RAG integrates retrieval of information from external knowledge sources during generation.
So how do you decide if fine-tuning is right for you?
Consider the following 6 dimensions:
Task complexity and specificity
Accuracy requirements
Availability of quality training data
Resource constraints
Ethical and bias considerations
Maintenance cost
#1. Task Complexity and Specificity:
Fine-Tuning Benefit: Assess whether the task requires a high level of domain-specific knowledge or expertise. If the task is complex and highly domain-specific, fine-tuning will offer performance improvements.
Alternative Approaches: For less complex tasks, alternative approaches such as prompt engineering or ensemble learning might be sufficient without the need for fine-tuning.
#2. Accuracy Requirements:
Fine-Tuning Benefit: Determine the criticality of accuracy for your specific task. If it is paramount, fine-tuning with a human in the loop will be your best shot.
Alternative Approaches: For tasks where accuracy is not the sole priority, prompt engineering or ensemble learning may help you strike a balance between accuracy and resource efficiency.
#3. Availability of Quality Training Data:
Fine-Tuning Benefit: Consider the availability and quality of training data. Fine-tuning relies on having a sufficient and relevant dataset for the specific task.
Alternative Approaches: If high-quality task-specific data is lacking, alternative approaches like ensemble learning or retrieval-augmented generation (RAG) might be more suitable.
#4. Resource Constraints:
Fine-Tuning Benefit: Assess your organization's resources regarding time, expertise, and budget. Fine-tuning is expensive and time-consuming.
Alternative Approaches: If resources are limited, explore alternatives such as prompt engineering or feature-based extraction (fine-tuning lite).
#5. Ethical and Bias Considerations:
Fine-Tuning Benefit: Evaluate the potential ethical implications and biases in both your training data and fine-tuned models.
Alternative Approaches: Augment fine-tuning with human-in-the-loop. So that you can have more control over ethical considerations.
#6. Long-Term Maintenance Costs:
Fine-Tuning Benefit: Fine-tuned models will require ongoing updates to adapt to changing task dynamics and emerging patterns. This can get costly very fast.
Alternative Approaches: Exploring more cost-effective alternatives like ensemble learning or retrieval-augmented generation (RAG). They can leverage diverse models that might offer better adaptability.
Here is what I have learned while building my own product.
My rules of thumb,
Fine-tuning by itself is not enough. For best accuracy, you need to augment it with the approaches mentioned above.
You can do a lot with just prompt engineering. See how far you can get with prompt engineering first. It may be all that you need.
Fine-tune only if you have the right training data. Garbage in garbage out works just as well for LLMs too.
Start with 1 - 2 layers. Overfitting risk is real. Going too deep or overboard, it going to make your model rigid and ultimately use less.
Add human in the loop. If accuracy is paramount, augment fine-tuning with a human. This way you leverage LLM capabilities with human expertise.
Happy building!!
PS: I just launched a 4-week bootcamp on how PMs can use AI to:
Achieve PM tasks 10x faster (writing requirements, crafting metrics, synthesizing feedback, etc.)
Deliver higher quality work, and
Accelerate the product development process
This is the same playbook I use to coach PMs to improve their productivity and build great products.
If you know of any PMs who might be interested, please forward this note or they can reach out to me directly.
Course Link: AI Bootcamp for Product Managers