Fine-tuning vs. In-context Learning
Considerations of LLMs for GenAI Apps
As enterprises start to build innovative Data Products on top of Large Language Models (LLMs) they soon realize that out-of-the-box LLMs are often not suitable for enterprise use cases and that there’s a need to adapt LLMs on enterprise data for them to deliver acceptable levels of performance. There are two main ways of doing this: fine-tuning and in-context learning. In this blog post, we will examine the key differences between these two approaches and provide recommendations on which approach is best suited for different enterprise needs.
Understanding Large Language Models (LLMs)
Fine-tuning: Tailoring LLMs for Specific Tasks
In-context Learning: Adapting LLMs to User Interactions
Use Cases for customizing LLMs foe GenAI Apps
Advantages Fine-tuning v/s In-context Learning
Choosing the Right Approach for Your GenAI Application
Limitations of out-of-the-box LLMs and available options
LLMs are trained on a large amount of data from multiple domains, so their knowledge is generalized. There is also a “knowledge cutoff” date, beyond which the LLM has no knowledge or understanding.
These two factors can cause LLMs to “hallucinate” incorrect facts if they do not have enough information about the topic in question. Additionally, enterprises are often looking for specialized tasks based on their own data, and the generalized performance of LLMs is not always acceptable.
There are two options for enabling LLMs to use and understand enterprise data and perform specialized tasks: fine-tuning and in-context learning. Each approach has its own nuances and uses. Let’s compare these two approaches to see which is best suited for different situations.
Fine-tuning
Fine-tuning is the process of further training an LLM on a specific task or domain. This is done by using the LLM as a starting point and then training it on a dataset of labeled data for the specific task or domain. Fine-tuning improves the performance of the LLM on the specific task or domain by adjusting the weights of the model to better fit the data. It thus creates a new version of the original LLM model with new weights tuned to the training data and specific to the use case it represents.
To fine-tune an LLM, you need to prepare a training dataset. The rules for creating a training dataset remain the same as for creating any training dataset in that the dataset should be Representative, Diverse, Balanced, Consistent, and Relevant.
In-context Learning
In-context learning is the process of providing a prompt or “context” to a pre-trained LLM to guide its answers for a specific task. Unlike fine-tuning, it does not involve any additional training. The same model can be reused for different tasks with different prompts.
These prompts can be in the form of instructions, a single example, or a few examples of the expected output based on the provided input. This approach leverages the model’s existing knowledge and generalization capabilities to understand and perform specific tasks based on the contextual clues given.
Which approach works best?
Number of distinct tasks
Fine-tuning requires creating separate and specific training data for each task that you want to adapt the LLM for. The quality and size of the training data is crucial for fine-tuning. In-context learning on the other hand is more easily adaptable to a wide range oftasks without needing separate models.
Accuracy expectations
Fine-tuning ensures higher accuracy and reliability due to finer control over the data used. Contrary to this, in-context learning responses can vary in quality depending on the effectiveness of the given context. As a result,in-context learning generally produces lower accuracy and reliability responses.
Regulatory compliance
To meet specific legal or industry standards, such as GDPR, HIPAA, or FINRA, you may need to fine-tune the model to handle data in a compliant manner. In-context learning can handle this through prompt engineering, but fine-tuning is bettersuited for such tasks.
Multilingual support
If you need the model to function well in languages other than those it was originally trained in, fine-tuning for specific languages can be very useful. With in-context learning, the prompt is all you have to adapt the LLM for multilingual support.
Availability of data
Fine-tuning requires high-quality, domain-specific data, which can be time consuming and expensive to create. If such data cannot be created then fine-tuning may not be the approach to take. As opposed to this, while in-context learning does not require training data but having a few examples can be helpful for zero or few-shot learning.
Deployment timelines
Fine-tuning requires time and effort to curate training data, fine-tune the model, and iterate to get the best accuracy. In-context learning does not require additional training, so systems can be deployed quickly. For projects with short deadlines, in-context learning allows rapid development and testing.
Availability of resources to train the model
Fine-tuning requires creating separate and specific training data for each task that you want to adapt the LLM for. The quality and size of the training data is crucial for fine-tuning. In-context learning on the other hand is more easily adaptable to a wide range of tasks without needing separate models.
Privacy concerns
In applications where data privacy is crucial, fine-tuning can help the model to better anonymize sensitive information.In-context learning relies on prompt engineering, so while it is possible to anonymize, the context may not be conveyed correctly. Or, in an effort to ensure the context is conveyed correctly, anonymization may not be effective.
Prompt engineering skills
Effective in-context learning often hinges on the art of crafting effective prompts. The quality of the output is dependent on the quality of the prompt. There is also a limitation on how much context can be provided due to token limits in the model architecture. Fine-tuning does not require prompt engineering skills, but it does require the ability to create good training data.
Maintenance requirements
Fine-tuned models may require regular updates to adapt to evolving data trends. In-context learning does not have separate models, so there are no maintenance requirements.
Conclusion
Fine-tuning and in-context learning are powerful tools for adapting LLMs for use in GenAI applications. The best approach to choose depends on various factors, including the specificity of the domain, the availability of resources, and the desired level of customization. By carefully considering these aspects, enterprises can make an informed decision that best suits their operational requirements. As we move forward in the data and analytics industry, the skillful application of these methodologies will undoubtedly be a pivotal aspect of successful GenAI deployment.
