Large language models (LLMs) use natural language processing and generative AI capabilities to understand and generate human-like text with remarkable proficiency. Still, their general-purpose design fails to capture the nuance needed for many domain-specific tasks.
This is where LLM fine-tuning comes into play. It trains a general-purpose LLM to be a specialist.
According to this paper, fine-tuning can dramatically enhance the performance of an LLM on specialized tasks. Increasing the amount of training data, along with other factors such as model size, creates a multiplier effect on performance. This relationship follows a power law, meaning small changes can lead to significant improvements when factors are increased together, rather than individually.
Large language models are a type of artificial intelligence specifically focused on processing and generating human language. They are based on neural networks with many layers, which is why they are often referred to as "deep" learning models.
The "large" aspect refers to the number of parameters (internal variables) that these models have. For instance, some of the most advanced LLMs have billions of parameters, which are adjusted during training to learn language patterns and structures.
Fine-tuning improves the performance of LLMs for specific tasks or specialized domains. To understand how, let’s use a human analogy. Imagine a newly-minted doctor with a broad knowledge of medicine. This is our general-knowledge LLM.
Now, imagine this doctor decides to specialize in cardiology. They don't forget all their general medical knowledge. Instead, they build upon that foundation, diving deep into heart-related topics, techniques, and treatments. They read specialized journals, attend cardiology conferences, and gain hands-on experience with heart patients.
After extensive specialized study, the doctor is now a cardiologist. Their generalized medical knowledge has been “fine-tuned” to the subspecialty of cardiology.
Fine-tuning trains an LLM on a smaller, task-specific dataset—usually labeled data. The process produces models that outperform general-purpose LLMs in specific tasks. Organizations can adapt pre-trained models to their unique requirements, terminology, and style.
The fine-tuning process involves six main stages:
The training process starts with dataset preparation relevant to the specific task. This dataset should be carefully curated and preprocessed to provide high quality and relevance.
For example, a model for medical diagnostics requires a dataset of medical texts, clinical notes, and other related documents. The team then preprocesses the dataset to make it as intelligible as possible to the model.
Choose a pre-trained model that serves as the base. Models such as GPT-4 or BERT have already been trained on massive datasets and have acquired a broad understanding of language.
Hyperparameters, such as learning rate, batch size, and epochs, are important parts of the fine-tuning process. These parameters need to be adjusted to balance the trade-off between learning efficiency and model performance. A learning rate that is too high can lead to overfitting, while one that is too low can slow down the learning process.
The pre-trained language model is further trained on your task-specific dataset. This involves feeding the model batches of data, calculating the loss, and updating the model weights using backpropagation.
The training continues until the model's performance on validation can no longer improve, which indicates that it has learned to generalize well from the specific dataset.
After training, the model is evaluated using a separate validation dataset to ensure it performs well on unseen data. Metrics such as accuracy, precision, recall, and F1 score are used to measure performance.
Based on these results, the model might undergo several iterations of fine-tuning, where hyperparameters are adjusted and training is repeated to achieve optimal performance.
Once the model has been fine-tuned and evaluated, it is deployed in a production environment. To maintain fine-tuned model performance over time, developers must monitor it continuously. This involves tracking the model's outputs and making periodic adjustments as new data becomes available or as the task requirements evolve.
There are many reasons why enterprises opt for a fine-tuned LLM for their operational needs:
If you want to experience the benefits of fine-tuning LLMs, give us a call! Here at Talbot West, we can lead you to AI implementations that drive real business value.
A fine-tuned LLM can supercharge your organizational efficiency in any sector, especially in the following industries and use cases.
Fine-tuned LLMs offer major advantages over general language models in healthcare applications. By training on specialized medical datasets, these models develop a nuanced understanding of medical terminology, procedures, and best practices that general LLMs lack.
A recent paper demonstrated “the superiority of…fine-tuned models over their corresponding base models across all medical benchmark datasets.” Med42, a fine-tuned LLM, demonstrated the following capabilities.
We predict a major trend of highly-specialized fine-tuned LLMs for a broad range of niche healthcare use cases. Expect to see a lot more specialized implementations.
Unlike general large language models, fine-tuned LLMs can accurately interpret complex legal jargon, cite relevant case law and statutes without fabrication, and generate jurisdiction-specific legal documents.
They excel at tasks such as contract analysis, predicting case outcomes based on historical data, and developing legal strategies—capabilities that general LLMs often struggle with. This specialized knowledge allows fine-tuned LLMs to provide more reliable and actionable legal insights, reducing the risk of errors in high-stakes legal work.
In finance, fine-tuned LLMs interpret complex financial instruments, regulatory requirements, and market dynamics with high accuracy. They analyze historical data to predict trends, assess credit risk precisely, and detect subtle fraud patterns. These models offer tailored investment advice based on individual risk profiles and financial goals.
Fine-tuned LLMs provide accurate financial forecasts and compliance checks, minimizing errors in critical financial decisions. They can navigate intricate financial regulations across different jurisdictions and financial products without hallucinating or misinterpreting crucial legal or compliance details that general LLMs lack.
Fine-tuned LLMs education understand curriculum standards, pedagogical methods, and student learning patterns. They provide personalized feedback based on individual learning histories and adapt explanations to a student's comprehension level.
These models accurately grade complex assignments, considering nuanced rubrics and subject-specific criteria. They generate custom learning materials tailored to individual needs, learning styles, and curriculum requirements.
Unlike general LLMs, fine-tuned models can track a student's progress across multiple subjects and recommend targeted interventions without conflating educational standards from different regions or grade levels.
Fine-tuned HR LLMs understand company-specific policies, job requirements, and organizational culture. They screen resumes with precision, matching candidates to job descriptions based on nuanced criteria. These models conduct preliminary interviews, adapting questions based on candidate responses.
While general LLMs might provide broad HR advice, fine-tuned models navigate complex employment laws and company-specific HR practices without misinterpreting crucial legal or policy details.
LLMs can be fine-tuned to understand game mechanics, narrative structures, and player behavior patterns. They generate dynamic storylines and character dialogues that adapt to player choices. These models create personalized gaming experiences by adjusting difficulty levels and in-game events.
Regular LLMs might create generic game content, but fine-tuned models maintain consistency in complex game lore and character development without introducing plot holes or contradicting established game rules.
Fine-tuning pre-trained language models requires a strategic approach for optimal results. Here are the best practices for effective LLM fine-tuning:
Let’s look at some of the challenges that can arise in the fine-tuning process.
LLMs might learn and perpetuate biases present in the training data, which leads to skewed or unfair outcomes. Bias mitigation requires rigorous auditing and curation of datasets for diversity and fairness, implementation of algorithms to identify biases, and continuous monitoring and updates to address emerging biases.
Overfitting happens when a model becomes too specialized to its training data. It's like memorizing exam answers instead of understanding the subject. The model performs great on familiar data but struggles with new scenarios. To prevent this, use techniques that help the model learn general patterns, not just specific examples.
Fine-tuning requires balancing generalization and specialization. Too much specialization leads to overfitting, where the model excels on training data but fails on new scenarios. Too little specialization results in subpar task performance. Achieving the right balance involves careful data selection, diverse testing, and continuous refinement based on performance metrics and feedback.
Fine-tuning LLMs on proprietary or sensitive data poses risks related to data privacy and security. Implementation of robust security measures, including tools to protect the LLM and applications from potential threats and attacks, becomes essential.
Fine-tuning demands intensive computation and can increase costs. New techniques such as Parameter-Efficient Fine-Tuning (PEFT) and prompt engineering can mitigate resource demands while maintaining performance improvements.
Fine-tuning and prompt engineering are two different approaches to the optimization of an LLM for specific tasks.
The tl;dr: fine-tuning gives better results and is more costly and cumbersome to spin up. Prompt engineering provides quick, flexible solutions that are not as tailored to your use case.
LLM fine-tuning and retrieval-augmented generation (RAG) offer distinct approaches to driving specialization in AI. These two methodologies can be combined for the ultimate specialized AI system.
See our article on RAG vs fine-tuning for an in-depth look at the differences between these approaches.
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Fine-tuning LLMs can be costly. Expenses vary based on model size, dataset complexity, and fine-tuning duration. While less expensive than training from scratch, it still demands significant resources. Cost-effective options, like parameter-efficient fine-tuning, exist for organizations with budget constraints.
The number of samples for LLM fine-tuning depends on the task complexity and desired performance. Generally, a few hundred to several thousand high-quality, diverse samples suffice. More samples often yield better results, but the benefits diminish beyond a certain point.
Fine-tuning is a form of transfer learning. It adapts a pre-trained model to specific tasks, while transfer learning broadly refers to using knowledge from one domain in another. Fine-tuning offers more targeted improvements for specific applications, but both techniques have their place in AI development.
LLM fine-tuning duration varies widely based on model size, dataset size, and available computational resources. It can range from a few hours for small models to several days or weeks for large models. Efficient techniques and hardware acceleration can significantly reduce fine-tuning time.
Domain fine-tuning adapts an LLM to a specific field or industry. It involves training the model on domain-specific data to improve its performance in tasks related to that area. This process results in a model with specialized knowledge and improved capabilities within the targeted domain.
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