Artificial intelligence (AI) is evolving quickly, and products that leverage generative AI to enhance functionality and user experience are hitting the market almost daily. The choice of approach is crucial for business leaders looking to integrate this technology into their products or services. Machine learning (ML) and large language models (LLMs) are distinct yet related techniques that can each be useful in different ways. But how do you know which one is right for your project? In this article, we’ll explore the differences between ML and multimodal LLM to help you make an informed decision and outline how Lamatic’s generative AI tech stack can help you get started on the right foot.
Lamatic’s generative AI tech stack is another valuable tool that can help you achieve your goal of understanding the key differences between foundation models and LLMs so that you can make informed, strategic decisions when selecting the right AI model for your specific needs.
What is the Difference Between ML and LLM?
Machine Learning
Machine Learning makes sense of vast amounts of data, seeking out patterns humans might miss and getting smarter over time. Instead of relying on explicit programming, ML algorithms use training data to create models that can accurately predict or classify unseen data.
As new data becomes available, the models can be retrained to improve performance and adapt to changes. ML encompasses many algorithms and models, from basic ones like Naive Bayes to more complex ones like Neural Networks.
Large Language Models
Large Language Models are a recent ML breakthrough, owing to concepts such as Neural Networks and back-propagation for training, which have revolutionized fields like computer vision, natural language processing (NLP), and reinforcement learning.
Neural networks' transformative potential wasn’t fully realized until about a decade ago, primarily due to limitations in data storage and computational power. These limitations were overcome with the widespread adoption of GPUs and affordable data storage and collection methods.
Understanding Traditional Machine Learning
Traditional ML models have long relied on feature extraction, a process crucial for various applications across industries like finance and healthcare. Techniques such as:
- Support Vector Machines
- Decision Trees
- Shallow Neural Networks
Which are foundational to LLMs rely heavily on the quality of feature engineering performed on available data. However, this approach has limitations due to humans' finite capacity to devise complex mathematical transformations.
Deep Learning vs. Traditional Machine Learning: Choosing the Right Approach for Your Task
Deep Neural Networks, particularly those employing Transformer and CNN architectures, represent a significant leap forward by automating and enhancing feature extraction. These models leverage self-supervised learning techniques to exploit vast amounts of unstructured data, reducing the need for extensive preprocessing.
While Deep Learning solutions excel in tasks like recommender systems and search, they may only sometimes be suitable for tasks requiring learning-to-rank techniques. In contrast, traditional ML solutions like Boosting Trees may be more appropriate.
How Natural Language Processing Works
Traditional text-processing techniques like TF-IDF and Bag of Words in NLP were instrumental in vectorizing text before the rise of models such as Word2Vec and FastText. Before models like BERT emerged, many NLP efforts focused on perfecting preprocessing steps.
Transformers, starting with BERT, paved the way for LLMs, trained on vast amounts of text data from the Internet. Owing to their extensive training data and large parameter sizes, these models excel in complex linguistic tasks like translation, question-answering, and summarization.
The Differences Between ML and LLMs
The distinction between ML and LLMs depends on the application's specific requirements. Due to their advanced capabilities, LLMs are often preferable for tasks demanding nuanced language understanding or Generative AI, like chatbots or text summarization. Traditional ML shines in scenarios where interpretability and computational efficiency are crucial, such as structured data analysis or resource-constrained environments like edge devices.
In certain areas, like sentiment analysis or recommendation systems, both ML and LLMs may offer viable solutions, each with unique advantages. These methods can be complementary rather than competitive depending on the specific use case. The following section will delve into implementation details and considerations for each technique, aiding in the decision-making process for various use cases.
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- How to Train Your Own LLM
A Closer Look at The Decision Matrix for ML vs. LLM
Large language models shine in generative tasks that require deep language understanding. Traditional machine learning, on the other hand, remains effective for more discriminative tasks due to its efficiency and lower resource demands. ML can be preferable for sentiment analysis or customer churn prediction, while LLMs are the go-to for complex tasks like code generation or text completion.
Comparing Different Approaches for Sentiment Analysis
To illustrate the differences between ML and LLMs in practical terms, let’s compare the approaches for building a sentiment analysis model that predicts whether reviews from an e-commerce website are positive or negative in terms of sentiment. We’ll explore three approaches:
- Machine learning with XGBoost
- Deep learning using TensorFlow
- Running sentiment analysis with a large language model from OpenAI.
Sentiment Analysis with Machine Learning
We’ll explore using XGBoost, a powerful and efficient machine learning algorithm, to perform sentiment analysis. This example showcases feature extraction from textual data, model training, and evaluation, emphasizing XGBoost’s effectiveness in handling structured data. The following code demonstrates a machine learning pipeline for sentiment analysis that:
- Leverages XGBoost
- Popular gradient boosting framework
- TF-IDF for text vectorization
The root idea here is to convert text into numerical vectors using TF-IDF. This method reflects the importance of words in a corpus and then applies XGBoost, an efficient and robust algorithm based on boosting trees employed on a binary classification task. This pipeline is particularly effective for structured datasets and is well-suited for scenarios where interpretability and computational efficiency are key.
We assume the text data fed to the pipeline is pristine. That is rarely the case. More often than not, the previous steps before running this model are text processing tasks such as removing stop-words, normalizing the text, etc.
Sentiment Analysis with Deep Learning
In the next example, we’ll demonstrate a deep-learning approach using TensorFlow. Here, we build a simple (shallow) neural network to process text data to see how deep learning can capture complex patterns in language through layers of neural networks. The following example uses TensorFlow with Keras:
- This code snippet shifts to a deep learning technique using TensorFlow, a powerful framework for building neural network-based models.
- The core of this deep learning example is the embedding layer, a concept popularized notably by Word2Vec. This layer transforms words into dense vectors in a high-dimensional space, capturing semantic relationships in a way that simple numerical vectorization cannot.
- Unlike the earlier TF-IDF and XGBoost approaches, this deep learning model learns representations of words in context, allowing it to capture nuances in language use. The model uses a simple neural network architecture with an embedding layer, a pooling layer to reduce dimensionality, and a dense layer for classification.
For simplicity, note that the implemented neural network contains only one hidden layer. To take advantage of DNNs' feature extraction capabilities, we’d need a neural network with more layers and complexity.
This approach is potent for large, complex datasets where capturing nuanced linguistic patterns is crucial. It’s a prime example of how deep learning can automate and enhance feature extraction, which traditionally requires extensive manual effort and domain expertise.
Sentiment Analysis with a Large Language Model
Let’s turn to an example utilizing a large language model, GPT-3, demonstrating how these advanced models can be leveraged for sentiment analysis with minimal setup, albeit with dependencies on external APIs and resources. The following example uses OpenAI’s GPT-3 API for sentiment analysis:
- This final snippet illustrates a different approach to sentiment analysis, leveraging OpenAI’s GPT-3 (Davinci model), a state-of-the-art large language model (LLM).
- The complexity of model training and feature extraction is abstracted away, as you take a shortcut using a pre-trained model. Unlike the previous examples where models were trained on specific datasets for the task at hand, GPT-3 has been trained on massive, diverse datasets, enabling it to understand and generate human-like text.
- The key advantage of this approach lies in its simplicity and versatility. With only a few lines of code and some prompt engineering, you can harness the power of GPT models to perform a wide range of tasks, including sentiment analysis, without extensive data preprocessing or model training.
This snippet sends a text to the GPT-3 API and receives an assessment of its sentiment, showcasing how LLMs can be served ‘out of the box’ for immediate use. It’s a testament to the advancements in natural language processing, where the complexity of language understanding is embedded within the model itself, trained on extensive data, making it exceptionally powerful and user-friendly for various applications.
Diving into Technical Considerations
Exploring the technical landscape of large language models requires navigating technical debt and cost considerations. As shown in the examples above, these models simplify deployment and reduce complexities but introduce financial implications.
This shift from technical to financial challenges prompts a closer look at the trade-offs between technical efficiency and the tangible costs of deploying and maintaining LLMs.
Technical Debt and Cost
Reflecting on the earlier examples, it’s clear that while LLMs like GPT-4 or Llambda offer streamlined processing and ease of use, they also present challenges in terms of costs.
These models, capable of understanding and responding to a wide range of prompts, significantly simplify the deployment process and reduce the complexities traditionally associated with model development and maintenance. This starkly contrasts ML methods like XGBoost, which require more hands-on effort in feature engineering and model tuning.
Simplifying Machine Learning Complexity at a Financial Cost
When considering applications of LLMs, you can view them as a method to convert the complexities and technical challenges of constructing machine learning and deep learning pipelines into a financial cost. This is because transformers, the underlying architecture of these models, handle the intricate task of feature extraction, which traditionally requires significant computational resources and expertise.
This convenience comes with a trade-off in the form of increased reliance on powerful graphics processing units. These GPUs are either a direct cost, if one hosts their LLM, such as Llambda, or this expense is factored into the service fee when using a managed service, like with OpenAI models. The burden of technical complexity is shifted to a financial one, making the technology accessible but at a price.
Latency and Task Nature
The latency of AI models is critical in user-facing applications. Latency is the speed at which a model can process and respond to an input. Traditional ML models, known for their faster processing capabilities, are well-suited for high-speed, real-time applications such as executing financial trading algorithm recommendations or managing emergency response systems where split-second decisions are imperative.For years, academia and industry have invested considerable effort in optimizing and scaling the computational costs of predicting and training ML models.
Balancing Response Time and Quality in LLM-Powered Applications
The scenario shifts when dealing with LLMs. Consider a virtual assistant in a customer support application. While immediate responses are essential, the comprehensive language understanding of LLMs can significantly enhance the quality and depth of interactions, justifying a slight response delay.
This nuanced trade-off is also evident in content generation tasks, where the richness and coherence of generated text or images from LLMs can outweigh the need for instantaneous results that we (the users) are familiar with in other applications.
Choosing Between ML Models and LLMs: Tailoring Solutions for Specific Needs
The choice between traditional ML models and LLMs involves meticulously evaluating the specific nature and urgency of the tasks. The balance between latency and language understanding becomes crucial in determining the optimal solution for a given technical context, showcasing the need for tailored approaches and acknowledging that different applications demand different considerations. Continuous effort is currently employed to optimize the computational effort these LLMs require, enabling faster responses on a larger scale.
Related Reading
- How to Fine Tune LLM
- How to Build Your Own LLM
- LLM Function Calling
- LLM Prompting
- What LLM Does Copilot Use
- LLM Evaluation Metrics
- LLM Use Cases
- LLM Sentiment Analysis
- LLM Evaluation Framework
- LLM Benchmarks
- Best LLM for Coding
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