How To Overcome the Limitations of Large Language Models
with Fine-Tuning
As businesses and organizations increasingly rely on Large Language Models (LLMs) to process and analyze vast amounts of data, they often face a daunting set of challenges that hinder their adoption:
- Privacy: Sensitive data cannot be shared on the cloud or sent to openAI, posing a significant risk to customer relationships.
- Cost: Generic LLM model’s cost becomes prohibitively expensive when processing millions of requests a month, making it a significant financial burden. The cost will become even higher if you incorporate examples in the prompt.
- Accuracy: Generic models struggle to deliver accurate results for highly complex data, leading to subpar performance.
- Hallucinations
- Consistency: Outputs vary from one request to another, causing format changes that break downstream applications.
- Inference Time: Large GPT models have significant latency, resulting in dissatisfied customers due to slow inference times.
In this article, we will introduce fine-tuning techniques to address the pressing issues that arise when working with LLMs. By fine-tuning these models, organizations can unlock their full potential, ensuring better accuracy, consistency, and performance, while also maintaining data privacy and reducing costs. We will cover the following topics:
- Benefits of fine-tuning: Understanding the advantages of customizing LLMs for specific use cases.
- Use cases and applications: Examining real-world scenarios where fine-tuning can be applied.
- Implementation and integration: Providing guidance on integrating fine-tuning into existing workflows.
- Introducing UbiAI LLM Fine-tuning
By the end of this article, you will have a comprehensive understanding of fine-tuning and its potential to successfully deploy reliable LLM in production.
What is Fine-tuning?
Fine-tuning is a crucial step in unlocking the full potential of LLMs. It involves customizing pre-trained LLMs to perform specific tasks, such as Named Entity Recognition (NER), Relationship Extraction, Document Classification, summarization, or Text Generation.
From a technical standpoint, fine-tuning involves modifying the weights of the pre-trained LLM’s neural network to adapt to the specific task at hand. This is typically done by adding a new layer on top of the pre-trained model, which is trained on a small dataset of labeled examples. The new layer is responsible for learning the task-specific features and relationships between the input and output.
There are several techniques of LLM fine-tuning, including:
- Full Fine-tuning: This method involves updating all model parameters to optimize performance for a specific domain or task. It offers optimal results and recognizes intricate patterns unique to the domain, but requires significant computational resources
- Transfer Learning: This involves using the pre-trained LLM as a starting point, and fine-tuning it on a small dataset of labeled examples. This approach is useful when the pre-trained model has a broad knowledge base, and the task at hand is similar to the tasks the model was trained on.
- Parameter-Efficient Fine-Tuning (PEFT): PEFT methods, such as Low-Rank Adaptation (LoRA), focus on optimizing the number of parameters updated during training. This approach reduces computational costs while maintaining high performance, making it suitable for resource-constrained environments
- Instruction Tuning: This technique involves training the model on input-output pairs augmented with natural language instructions. It helps the model learn to perform various tasks from human instructions prior to being fine-tuned on a specific task, improving its ability to follow directions and generalize to new tasks
Use Cases of Fine-tuning LLMs
In this section, we’ll explore some practical use cases and applications of fine-tuning, highlighting its potential to improve performance and efficiency in different areas.
Recommendation Systems
Fine-tuning can be applied to recommendation systems, enabling the adaptation of pre-trained models to specific user preferences and behaviors. For example:
- User profiling: Fine-tuning a pre-trained user profiling model on user demographics or purchase history, to improve recommendation accuracy and personalization.
- Item recommendation: Fine-tuning a pre-trained item recommendation model on product features or user ratings, to adapt to the unique characteristics and preferences of the users.
Medical Diagnosis and Treatment
For example, ChatDoctor, a fine-tuned Meta’s LLaMA model on 100,000 patient-doctor dialogues, incorporated self-directed information retrieval from online sources and medical databases. The fine-tuned model significantly improved the understanding of patient needs and the accuracy of medical advice
Credit Risk Assessment
CrediFlow AI fine-tuned a language model to analyze credit reports, loan applications, and financial statements. The fine-tuned LLM achieves an accuracy rate superior to the average financial analyst’s accuracy of 53%.
Investment Research
BlackRock reported improved precision in their text-based investment analysis using fine-tuned LLMs.
Claims Processing
DeepOpinion fine-tuned an LLM model that can process over 35,000 document layouts with 98.3% accuracy. This model extracts relevant data from various document formats, including medical reports and repair estimates, significantly reducing processing time and human error in claims handling.
Legal
LLMs fine-tuned with deep legal expertise helped mimic the reasoning and decision-making processes of experienced lawyers, which is particularly useful in complex areas like financial crime investigation.
These are just a few examples that demonstrate the potential of fine-tuning LLMs in various industries. In general, fine-tuning is critical for any task that requires high accuracy, predictability and efficiency.
How To Fine-Tune and Evaluate Fine-tuned LLMs with UbiAI
Fine-tuning LLM is a crucial step in achieving high accuracy and relevance for your specific business needs. However, fine-tuning requires careful consideration of several factors to ensure that the model is optimized for the task at hand. In this section, we will guide you through the process of fine-tuning and evaluating fine-tuned LLMs using UbiAI.
Select Model Task
The first step is to select the task we want to fine-tune the model on:
Named Entity Recognition (NER): Extract key information from documents and train your AI model to automatically recognize and extract key information such as company names and addresses.
Relationship Extraction: Train models to categorize entity relationships and unlock deeper insights by automatically detecting both entities and relationships in your data.
Document Classification: Fine-tune models for document classification tasks and streamline entity recognition and relationship extraction to reveal hidden patterns and actionable intelligence.
Text Generation: Fine-tune models for various text generation tasks, including text-to-SQL, summarization, reasoning, and question answering.
In UbiAI, simply create a new model and select the Model Category to specify the task.
Prepare Your Data
Before fine-tuning an LLM, you need to prepare your data by annotating and labeling it accurately. UbiAI’s intuitive labeling interface and AI-assisted labeling features make it easy to create high-quality training data. You can label any type of document, including PDFs and images, with unparalleled accuracy using Optical Character Recognition (OCR) technology.
Fine-Tune Your LLM
Once you have prepared your data, you can start the fine-tuning process. UbiAI optimizes all the hyper-parameters to get the highest accuracy without any code required. This feature allows you to customize LLMs on specific tasks, such as NER, REL, Summarization, text generation, and more.
You have the option to train open-source LLMs on your own data such as Llama 3.1 8B, Mistral 7B, or Solar-mini.
To launch the training, simply validate your dataset and click “Start Model Training”.
During training, you can monitor the loss, F-1, precision, and recall for each iteration. Ideally, the loss should decrease gradually with the number of iterations until it plateaus as shown below:
Evaluating Your Fine-Tuned LLM
After fine-tuning your LLM, it’s essential to evaluate its performance to ensure that it meets your business needs. To evaluate the model, UbiAI splits the data into a training set, used for training a model, and a test set to evaluate the model. The evaluation metrics, include:
F1-score, Precision, and Recall:
Evaluate the model’s ability to correctly classify text. UbiAI provides F-1, precision, and recall at the model level as well as at the entity level (specific for predictive tasks such as NER, Classification or Relation Extraction).
Precision measures the accuracy of positive predictions made by the model. It is defined as the ratio of true positives (correctly predicted positive instances) to the total number of predicted positives (both true positives and false positives)
Recall, also known as sensitivity or true positive rate, measures the ability of a classification model to identify all relevant positive instances. It is defined as the ratio of true positives to the sum of true positives and false negatives (missed positive instances).
Confusion Matrix:
A table that summarizes the performance of the model by comparing predicted values against actual labeled values for a specific dataset. It provides a visual representation of the model’s accuracy and errors, making it easier to understand the types of mistakes the classifier is making. For example, any non-diagonal values are considered incorrect predictions by the model and should be looked at carefully.
In addition, UbiAI provides guidance and recommendations on how to improve the model even further.
By following these steps and using UbiAI’s fine-tuning and evaluation features, you can fully fine-tune and evaluate your LLM to achieve unmatched accuracy for your unique business needs.
Deploy Your LLM
The final step is to test the model on unseen data and deploy it in production. UbiAI provides a ready-to-use API endpoint to integrate the model into your application with just a few clicks.
import requests
import json
url ="INFERENCE_URL"
my_token = "YOUR_TOKEN"
data = {
"inputs" : [
"put your text here",
"put more text here"
]
}
response = requests.post(url+ my_token,json= data)
print(response.status_code)
res = json.loads(response.content.decode("utf-8"))
print(res)
Conclusion:
In this article, we have explored the limitations of LLMs and introduced fine-tuning as a crucial step in unlocking their full potential. By fine-tuning pre-trained LLMs, organizations can achieve unmatched accuracy, consistency, and performance, while also maintaining data privacy and reducing costs. We have discussed various techniques of LLM fine-tuning, including full fine-tuning, transfer learning, parameter-efficient fine-tuning, and instruction tuning. While fine-tuning offers numerous benefits, it is not without limitations. The process requires significant computational resources, and the quality of the fine-tuned model depends heavily on the quality of the training data.
For organizations looking to leverage the benefits of fine-tuning, we recommend starting with a clear understanding of your specific business needs and investing in high-quality training data, leveraging the expertise of subject matter experts in your organization to ensure the success of fine-tuning projects.
If you are curious about fine-tuning, UbiAI offers free trials to get started without any code required following this link.
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