Unlocking the potential of LLMs: Fine-tuning

Following the important evolution and the availability of open source foundation models offered by different AI companies, the opportunity of leveraging Large Language Models (LLM) for business use cases is incredible.

LLMs like GPT, Llama or Gemini have been surprisingly efficient in comprehending, generating and interacting using human language. This allowed to unlock huge opportunities of application like chatbot, unstructured data processing, content generation tolls, across industries to solve different problems.

However, while LLMs knowledge base is important, it is often general-purpose. This means that an LLM trained on a massive dataset of text and code might struggle to understand the nuances of specific domains like finance, medicine, or legal jargon. Therefore, By adapting an LLM to a particular domain, we can significantly improve its performance in several ways.

This article delves into the concept of fine-tuning, a powerful technique that customizes these models for specific domains and tasks. We explore key challenges associated with fine-tuning and propose effective solutions that will mitigate these challenges.

What is an LLM?

As detailed in my previous articles Recent advances in AI — Generative AI and Optimizing RAG Applications LLM (Large Language Model) is a type of AI capable of primarily understanding and generating text. It is based on machine learning type of models called transformer models.

LLMs are trained on massive amounts of dataset from mainly from internet. The quality of the dataset impact the how the model will comprehend and generate.

LLM use deep learning technique to be able to understand the relationship between characters, words and sentences. It also uses probabilistic analysis of unstructured data to be able to descern differences in content without human intervention.

What is LLM Fine-tuning?

LLM fine-tuning is the process of customizing a general purpose pre-trained large language model into a specific domain in order to get better results in a specific task/domain. It is achieved by further training the language model on a smaller but specialized dataset in the form of “input-output” examples similar to the desired model responses.

This process allows the LLM to extend and enhance its knowledge on a specific domain thus improving its output in to meet the expected results.

The process of fine-tuning involves updating the model parameters through an extensive supervised learning method using labeled data.

Dataset: it’s all about automation:

To build an enterprise-scale fine-tuned LLM, we need to have a highly clean and comprehensive domain-specific dataset. In addition, there is a need to keep the dataset up to date with the freshest data collected. Building such dataset can be expensive. Therefore, there is a need to automate and streamline the process of data collection and preparation.

An efficient way to do it is to build data pipelines that includes different data prep/processing tools in addition to RAG application. Using RAG is important to comprehending and classifying, and transforming the streams of unstructured data.

How to fine-tune an LLM:

After preparing a high quality task-oriented dataset of labeled data, we can now proceed with the process of fine-tuning, which includes the following steps:

Fine-tuning a pre-trained LLM

• The first step is to select the appropriate general-purpose LLM that best suits your need and use case. Considerations regarding the number of parameters and quality of data in the training phase are key to the performance of the LLM (I will detail this point in a future article)
• Domain-specific dataset: As discussed earlier, data preparation pipeline should be well-designed to allow for the creation of high quality labeled data using an efficient and cost-effective pipeline. Depending on the domain, an alternative is to purchase a database from the internet.
• Continuous training phase: The general purpose model is trained on the new labeled dataset using the supervised learning technique. During this process, the model recalibrates its parameters and weights in order to overwrite its general-purpose knowledge with the new acquired knowledge, making the model develop a deep understanding/expertise in that domain. This phase should be continuously running in order to accommodate for the new information coming from the data pipeline.
• Domain-specific model: This is the result of the training phase. This model will keep it general knowledge capabilities while excelling the specific domain.
• Human interaction: Using the model by the client (human or system) to implement new use cases and help solve industry-specific problems.

Fine-tuning challenges and solutions:

While fine-tuning an LLM model offers a huge potential, it comes with its specific challenges.

The data dilemma:

One of the main challenges is the availability of data. Getting an extensive dataset of particular domain can be a tedious task. In addition, the dataset used in the fine-tuning needs to be large enough and of a high quality to be able to have satisfying outcome especially if the pre-training data did not extensively cover your specific domain (which is most likely)

Training the LLM with limited dataset would result in unsuccessful outcomes. Remember that it is only through practice that human learn, and it is applicable to LLMs.

To mitigate those challenges, there are some techniques that can boost the performance of fine-tuned LLM like:

• Data augmentation: this method involves applying different techniques such as rotation, noise injection, translation and scaling, to data sample in order to generate additional training data.
• Transfer learning: is adapting the model to another related task. Instead of re-training the model from scratch to perform a new task, this technique leverages the knowledge gained to solve the new related task.
• Semi-supervised learning: this technique uses combination of labeled and un-labeled data to hit the right balance between supervised and unsupervised learning. By leveraging the superabundance of un-labeled data along with labeled data, this technique will improve the model’s performance and reduce the dependancy on labeled data that requires the extensive labeling effort.

Catastrophic interference:

While fine-tuning an LLM, you are most likey to experience catastrophic interference cases where the model is trained on a new domain but “forgets” about tasks it was already good at.

This regression happens due to the update on the model’s parameters during the fine-tuning phase. Overcoming this hurdle is critical to ensure that the model learned a new domain(s) without compromising the prior knowledge.

To overcome this challenge, several techniques should be employed:

• Elastic Weight Consolidation (EWC): This technique allows to help preserve important parameters that were trained to be good at specific tasks. This is achieved by assigning importance to specific parameters based on their significance in previous tasks and penalieze their update during the training on the new tasks.
• Progressive Neural Networks (PNNs): PNNs are a subset of neural networks used to enable the model’s continuous learning without catastrophic interference. In this technique, each new task is learned by adding a new neural network modules while retaining the knowledge on previously learned tasks. This is a modular approach that allows to expand the model’s progressively over time without compromising the existing knowledge.
• Tasks Sequencing and Knowledge distillation: Task sequencing enforces priority-based ordered learning of tasks while Knowledge distillation is a technique to transfer knowledge from a complex model “teacher” to a simpler one “student”. When combined, task sequencing can enforce in whitch the distillation is applied, ensuring that the “student” is learning from the “teacher” model in an ordered manner which improves both the efficiency and the performance of the model on multiple tasks.

These methods allow to fine-tune the model while retaining previously learned knowledge.

Overfitting:

Good-fitting vs overfitting — Source: https://shorturl.at/OUV68

During fine-tuning, the LLM might become overly specific to the training examples. It is the case where the model memorizes training data instead of learning real world general language patterns.

There several approaches to overcome this problem:

• Regularization techniques: this includes dropouts and weight decay methods to prevent the model from memorizing specific examples of the learning data by introducing randomness and penalizing overly large weights.
• Data strategies: training data quality selection using methods like cross-validation helps maintain the balance between the model’s complexity and its ability to generalize to new situations.
• Training strategies: considering training strategies like early stop, ensemble learning (combining multiple models), and regularly monitor the model’s performance during the training phase will prevent overfitting.

Hyperparameter Tuning:

During fine-tuning, several settings called hyperparameters are adjusted, which dramatically impacts the model’s performance. Choosing the wrong hyperparameters can lead to poor performance on unseen data, slow learning and training failure.

Hyperparameters tuning

There are many techniques that can help with this kind of tuning. One of which is automation, which uses techniques like grid search and Bayesian optimization to automate the process of detecting hyperparameter combinations and thus coming out with the best possible configuration. Additional techniques like adjusting the learning rate during training phase can also enhance the efficiency of hyperparameter tuning.

Bias amplification:

Fine-tuning can amplify the existing pre-trained LLM bias (from pre-training dataset) leading to prejudiced outputs. This is a crucial matter to address due to the high negative impact it can cause to the users.

To mitigate this risk, a multi-layered approach needs to be put in place. This includes the following:

• Data techniques: handle the bias in both the pre-processing to reduce bias and post-processing training techniques to debias the outputs.
• Monitoring and evaluation: Regularly audit the model’s behavior to help identify and address the biased outputs.
• Curating data: involves selecting, pre-processing and organizing datasets in order to reduce bias in the training datasets
• Neural prompts: using input instructions when interacting with the model to better generate debiased outputs.

Conclusion:

LLMs are disrupting nowadays business in so many ways. Fine-tuning unlocks the true potential of LLMs by customizing them for specific domaines or tasks. However, it comes with many challenges like data limitations, knowledge loss, and bias. This article explores solutions for these challenges, including data augmentation and bias mitigation techniques. By addressing these concerns, you can leverage fine-tuning to create powerful domain-specific LLMs. As AI evolves, fine-tuning will play a key role in maximizing LLM potential across various industries.

Next:

In my next article, I will be evaluating and comparing both Retrieval Augmented Generation (RAG) and Fine-tuning approaches. Stay tuned.