Large Language Models (LLMs) have completely revolutionized the field of Artificial Intelligence (AI) and Machine Learning (ML). These models are ideally designed to understand and generate human language, allowing them to perform a wide variety of natural language processing (NLP) tasks. Consequently, there has been a widespread adoption of Llms in various industries, with a recent survey showing that about 34% of organizations plan to integrate these models into their own applications. [1]

However, the generic training of LLMs usually results in subpar performance of these models in certain tasks. To help overcome these limitations, organizations can fine-tune pre-trained LLMs to enhance their capabilities.

This post will delve into LLM fine-tuning and shed some light on some of the benefits, costs, and challenges behind this process.

What is a Large Language Model?

A Large Language Model (LLM) is an advanced type of AI ideally designed to process, understand, and generate text in a human-like fashion. LLMs are usually built using deep learning techniques and trained on huge amounts of data from a wide variety of sources such as webpages, books, conversation data, scientific articles, and codebases. One of the best things about large language models is their ability to understand and generate human-like text based on the input provided or the question asked.

In the world of LLMs, the narrative has always been straightforward—the bigger, the better. Therefore, large language models with more parameters and layers tend to understand the context better, make fewer mistakes, and generate better responses. Basically, huge amounts of training data help improve the quality and accuracy of a large language model. Some of the most popular examples of LLMs include Meta AI’s LlaMa, Google’s BERT, and OpenAI’s GPT-3. [2]

Google’s BERT, in particular, has played a vital role in revolutionizing large language models by considering bidirectional context during the training phase. This has helped significantly improve an LLM’s understanding of sentence structure, thus allowing for a better performance on various tasks such as speech recognition, machine translation, and sentiment analysis.

On the other hand, Meta AI’s LlaMa has also been key in helping AI researchers advance their work in this field. In fact, LlaMa outperforms several LLMs on many external benchmarks, such as knowledge, coding, proficiency, and reasoning tests. [3]

That said, LLMs have made a significant impact in various fields, including content creation, scientific research, risk assessment, training and onboarding, predictive analysis, competitive intelligence, language translation, malware analysis, and customer feedback analysis. [4]

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What is LLM Fine-Tuning?

Fine-tuning basically refers to the process of adjusting and tweaking a pre-trained model to make it suitable to perform a particular task or cater to a given domain more effectively. This process usually involves training an LLM on a smaller and more targeted dataset relevant to the task you want the model to complete.

Popular pre-trained LLMs are powerful but may not perform in specific tasks or domains. In that case, specialized training or fine-tuning is needed to help improve their performance and accuracy for your desired application.

For example, you can easily fine-tune any pre-trained model of your choice to perform specific tasks such as analyzing sentiment in customer reviews, translating text from English to French and Italian languages, classifying documents based on themes, detecting malware and viruses, predicting stock prices based on business news, or even writing love poems.

When it comes to fine-tuning LLMs, you don’t need to use large datasets. Rather, you only need to use task-specific or domain-specific data to enhance your model’s performance in the respective area.

Fine-Tuning LLMs: Step-By-Step Guide

The process of fine-tuning LLMs involves several steps including the following:

Identify the target task

The first step towards fine-tuning LLMs is to identify the specific task you want your model to specialize in. Such tasks may range from document classification and sentiment analysis to text summarization and language translation.

Prepare the dataset

Once you’ve identified the task you want your LLM to specialize in, the next step is to prepare the relevant dataset for fine-tuning. This dataset must reflect the nature of the task at hand and include relevant examples to help your large language model learn what the task entails.

For example, if the task is to generate sales proposals, the dataset should include several examples of authentic sales proposals. Most importantly, the quality and diversity of your dataset are crucial factors to consider when preparing your dataset.

Reprocess the dataset

After collecting and curating the data relevant to your task or domain, the next step is to reprocess it to get rid of noisy data and ensure it meets the requirements of your large language model. Reprocessing your dataset before feeding it into your pre-trained model will also ensure consistency and better results. This step usually involves several tasks, including data tokenization, data augmentation, data cleaning, data reduction, data integration, and data transformation.

Once the data has been reprocessed, it’s then categorized into training and validation sets and then converted into an appropriate format that the LLM can understand. [5]

Choose a foundation model and appropriate fine-tuning method

The next step after reprocessing your data is to choose a foundation LLM to use based on the task at hand and dataset size. When choosing a foundation model for your LLM, you should also consider your model’s input and output size and whether the technical infrastructure is suitable for your fine-tuning project.

Fortunately, there are several LLM architectures to choose from, including BERT, Cohere, Falcon 40B, GPT-3, GPT-3.5, GPT-4, Orca, LlaMa, PaLM, Claude, and many others. Notably, each one of these LLMs has its own strengths and weaknesses.

Once you’ve selected a foundation model for your fine-tuning project, you need to select a fine-tuning method. The fine-tuning method you choose also depends on the task and data at hand. Some of the most commonly used fine-tuning methods include task-specific tuning, reinforcement learning, multi-task learning, adapter-based fine-tuning, and sequential fine-tuning.

Load the pre-trained model

After selecting the appropriate foundation model and fine-tuning method, the next step is to load the pre-trained model with the right pre-trained weights. These pre-trained weights simply represent the knowledge your large language model has gained from its initial pre-training phase and help speed up the fine-tuning process. In other words, these weights ensure that the LLM has learned general language related to the task at hand.

Fine-tuning

Fine-tuning is the core step in improving the performance of LLMs in various tasks and domains. In this step, the pre-trained model is trained on the task-specific or domain-specific dataset. As aforementioned, this process involves adjusting and optimizing the model’s weights and parameters using the new data. The fine-tuning process uses lower learning rates than the initial pre-training process to help minimize the loss function.

Remember, you only want the LLM to improve its performance on the target task and not lose its initial language on the subject. The fine-tuning process usually involves tasks, such as multiple rounds of training on the task-specific/domain-specific dataset, validation on the validation dataset, and hyperparameter tuning to help enhance the model’s performance.

Notably, the size of the task-specific/domain-specific dataset, how similar the target task is to the pre-training data, and the available computing infrastructure will determine how long and complex the fine-tuning process will be.

Iteration and evaluation

When fine-tuning LLMs, iteration and evaluation are important steps for increasing the model’s efficacy. That said, your model’s performance needs to be evaluated once the fine-tuning process is complete. During this phase, the fine-tuned model is evaluated on a validation dataset. This process helps gauge how well the large language model is responding to the new data and whether or not it’s performing the target task effectively.

Some of the evaluation metrics used in this step include accuracy, precision, recall, and F1 score. [6] If the model’s performance on the target task is not satisfactory, adjustments can be made to the data, and the fine-tuning process can be repeated.

Deployment

Once the fine-tuned large language model is evaluated and tested, it can now be deployed in the target application. The deployment process involves integrating the fine-tuned LLM into a larger system in an organization, setting up the necessary infrastructure, and continuously monitoring the model’s performance in the real world.

LLM Fine-Tuning Techniques

There are several methods used in fine-tuning LLMs. Here are the most common ones:

Transfer learning

In the context of machine learning, transfer refers to the practice of using a pre-trained model’s weights and architecture as the basis of a new target task or domain. For example, a pre-trained model like OpenAI’s GPT-4 can be used as the starting point for another LLM that needs to be fine-tuned. Since GPT-4 is trained on a large dataset, the transfer learning process allows for faster and more effective adaptation of the fine-tuned model to specific tasks or domains.

This method of fine-tuning LLMs is popular because it saves time and resources that would have otherwise been spent training a large language model from scratch.

Task-specific fine-tuning

Task-specific fine-tuning is a technique used to adjust a pre-trained model for a specific task or domain using task-specific or domain-specific data. Although this method requires more data and time to complete than transfer learning, it usually results in a higher performance on a target task. For example, you can create a more effective model for machine translation by fine-tuning a pre-trained model like Seq2Seq.

Sequential fine-tuning

Sequential fine-tuning is a fine-tuning method whereby a pre-trained method is fine-tuned on multiple target tasks or domains sequentially. This technique allows LLMs to learn more complex language patterns so that they can adapt and improve their performance in different tasks, applications, and domains.

For example, you can train an LLM on a general text corpus, and then fine-tune it on a health record dataset to help improve its performance in identifying the symptoms of various diseases.

Multi-task learning

This method entails fine-tuning a pre-trained model on multiple target tasks simultaneously. Multi-task learning is commonly used when fine-tuning LLMs on tasks with similar characteristics. Using this fine-tuning technique, an LLM is able to learn and leverage the similarities shared by the different tasks, thus leading to improved performance and generalization.

For example, a single model can be fine-tuned using multi-task learning to perform tasks such as document classification, clustering, text summarization, and short text expansion.

Adapter-based fine-tuning

Adapter-based fine-tuning is a relatively new technique in fine-tuning LLMs that mainly uses small, learnable modules known as adapters. In this process, the small adapters are inserted into a pre-trained model at different layers and fine-tuned to perform specific tasks. During adapter-based fine-tuning, the original pre-trained model’s parameters are left undisturbed, and its performance on other tasks is not affected.

Reinforcement learning

Reinforcement learning (RL) is a technique used in fine-tuning LLMs whereby a pre-trained model is fine-tuned to interact with a certain environment on a trial-and-error basis. The large language model is usually rewarded for taking actions that lead to desired outcomes and penalized for performing actions that lead to bad/undesired outcomes.

Over time, the pre-trained large language model learns to only perform actions that lead to the best results possible. This technique has been used to fine-tune LLMs on various tasks such as machine translation, sentiment analysis, question-answering, and summarization.

Benefits of fine-tuning LLMs

Although it may seem simpler and more economical to use an existing LLM like ChatGPT, fine-tuning a pre-trained model on specific tasks offers several benefits, including the following:

• Allows you to tailor a pre-trained large language model to your organization’s unique needs and objectives.
• Enhances the overall user experience since the output provided is more accurate and specific to a given task or domain.
• Reduces the cost of AI development, thus making the adoption of LLMs in the workplace more accessible and affordable.
• Offers better control of large language models, thus minimizing the risk of generating controversial or biased content.

Common Challenges of Fine-Tuning LLMs

Fine-tuning LLMs is not always a simple process. Sometimes, this process is met with a set of challenges, including the following:

• Overfitting: This is an undesirable behavior in fine-tuning that occurs when an LLM performs well on the training data but not on new task-specific or domain-specific data.
• Catastrophic Forgetting: Sometimes fine-tuning an LLM can lead to catastrophic forgetting whereby the model forgets everything it had learned during pre-training.
• Expensive: Fine-tuning LLMs to perform specific tasks requires huge amounts of data which may be difficult or costly to obtain. This is particularly the case for target tasks where the labeled data is scarce or insufficient. Additionally, fine-tuning an LLM requires a complex computing infrastructure that is costly to set up.
• Bias: Some pre-trained LLMs are biased, which means that this bias can easily be amplified during the fine-tuning process. That said, it’s important to implement the necessary steps to mitigate bias before commencing the fine-tuning process.

Case Studies for Fine-Tuning LLMs

Below are some case studies where fine-tuning large language models has helped solve various real-world problems:

Lawgeex

Generally, legal document analysis is a tedious and time-consuming task that requires expertise and excellent attention to detail. This is mainly because most legal documents contain complex language and jargon that most people may not understand.

However, Lawgeex, a legal technology company, has been able to fine-tune LLMs using a large dataset of legal texts and create a model that can analyze and summarize legal documents in a matter of seconds. [7] This helps save the time lawyers spend reviewing and analyzing legal documents so that they can focus on more demanding and strategic tasks.

Google Translate

Google Translate is a great machine translation tool that uses a variety of fine-tuned LLMs to translate text in over 100 languages used worldwide. Some of the languages supported by Google Translate include English, French, Spanish, German, Italian, Japanese, Chinese, Arabic, Portuguese, Danish, Finnish, Swahili, Dutch, Thai, and many others. [8]

Salesforce Einstein

Salesforce Einstein AI is a state-of-the-art AI platform that relies on fine-tuned LLMs to help businesses transform their operations. This platform harnesses the power of AI in NLP, machine learning, image recognition, and speech recognition to help organizations improve their processes.

With the help of the customer data insights gained using this model, businesses can generate personalized emails and create personalized marketing campaigns. [9] In the long run, this leads to better customer engagement, improved customer support, better forecasting, and more revenue for the businesses.

Final thoughts

In the fast-changing world of artificial intelligence and machine learning, large language models play an important role in understanding and generating human-like text. However, since there is no one-size-fits-all solution when it comes to LLMs, fine-tuning these models has become increasingly important for improving their performance in specific tasks and domains.

By understanding the different LLM fine-tuning techniques and when to use them, you can easily create the ideal model and unlock its potential. Adopting such a model will help simplify your internal processes, boost employee productivity, improve customer experience, and lead your business to success.

References

[1] Cutter.com. Enterprises Are Keen Adopting LLMs, But Issues Exist. URL: https://www.cutter.com/article/enterprises-are-keen-adopting-large-language-models-issues-exist. Accessed October 4, 2023
[2] Techtarget.com. 12 of the Best LLMs. URL: https://www.techtarget.com/whatis/feature/12-of-the-best-large-language-models. Accessed October 4, 2023
[3] Promptengineering.org. Does Llma 2 Compare to Chat gpt and other Language Models. URL: https://bit.ly/45xyIpG. Accessed October 4, 2023
[4] Techopedia, com. Practical LLM Applications. URL: https://www.techopedia.com/12-practical-large-language-model-llm-applications, Accessed October 4, 2023
[5] Kili-technology.com, Training Data, Validation, and Test Sets: how to Split ML Data. URL: https://bit.ly/3LTyZw7. Accessed October 4, 2023
[6] Geeksforgeeks.com. Metrics for ML Model. URL: https://www.geeksforgeeks.org/metrics-for-machine-learning-model/, Accessed October 4, 2023
[7] Medium.com. My Other Lawyer is a Robot. URL: https://medium.com/syncedreview/my-other-lawyer-is-a-robot-lawgeex-automates-contract-review-eef4e2247114. Accessed October 4, 2023
[8] Translate. Google.com. URL: https://translate.google.com/. Accessed October 4, 2023
[9] Help.salesforce.com. Understand How Einstein Generative AI Creates Sales Emails. URL: https://sforce.co/3ttiUa4. Accessed October 4, 2023

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