The rise of large language models (LLMs) has attracted significant attention. Especially after the release of GPT models, people have been intrigued by how LLMs can impact their daily lives and work. It is essential to understand the background of LLMs and how they fit into the broader spectrum of AI.
Advancements in machine learning, alongside the computational power we’ve acquired over the years, have led to the creation of these large language models capable of processing huge amounts of data.
In this blog, we discuss LLMs and how they fall under the umbrella of AI and Machine learning. We then dive into the technical side of LLMs and finally present a practical, real-world example.
What Are Large Language Models?
Large Language Models are deep learning models that recognize, comprehend, and generate text, performing various other natural language processing (NLP) tasks. They are called “large” because they are typically trained on vast datasets, often containing billions of words. During training, LLMs learn statistical relationships within the text and can generate human-like responses on an endless range of topics.
How Do LLMs Work?
At its core, machine learning is about finding and learning patterns in data that can be used to make decisions. In supervised learning, we provide models with labeled examples, and this process is known as training the model.
We want to create a simple machine-learning model that can sum up two numbers. To do this, we provide the model with enough examples of summation to learn what we expect. Eventually, the model can take any two numbers and add them correctly.
Analogy: This training process is analogous to teaching a child how to add. For example, if we give the child a problem like adding five and nine, we know the answer is 14. The child begins by guessing. The closer their guess is to the actual answer, the more positive feedback (encouragement) they receive, and the farther they are from the correct answer, the less encouragement they get. Over time, this feedback teaches the child the direction they need to go to get the right answer.Â
Similarly, in machine learning, feedback comes as a loss value, and the model adjusts its parameters over many iterations to minimize this loss.
The underlying mechanism remains the same for more complex tasks, like predictions or classifications, but the model’s architecture becomes more sophisticated to capture complex patterns in the data. While Support Vector Machines (SVMs) or Regression Trees are commonly used for structured data, we turn to deep learning models for tasks like image recognition or text processing. These models better mimic the human brain with neurons and layers and can capture more complex patterns and relationships from the data.
Data isn’t always structured (numbers and tables); we often deal with unstructured data like text, images, videos, or audio. Text processing falls under the domain of NLP, where LLMs come into play.
Transformers: The Backbone of LLMs
One of the key advancements in deep learning is the transformer model, which introduced a game-changing mechanism known as self-attention. Self-attention allows the model to capture complex relationships in a text sequence using context, even if the text itself seems vague or loosely connected.
On a certain level, transformers can “understand” the meaning of words by associating them with concepts they’ve seen grouped millions or billions of times. This is the foundation of how LLMs like Chat GPT work.
What Are the Key Components of an LLM?
The general architecture of an LLM includes multiple layers, each designed to perform a specific role in processing and generating text:
Embedding Layer: This layer translates input tokens (words or subwords) into embeddings that the model can understand. It captures the semantic relationships between words.
Feed-Forward Layer: Processes the embeddings to detect patterns and relationships within the data.
Recurrent Layer: This layer captures sequential dependencies between tokens, helping the model understand the order and context of words, which is essential for language comprehension.
Attention Layer: The attention mechanism helps the model focus on the most relevant parts of the text by assigning higher weights to certain words. This is especially useful when processing longer inputs.
Neural Network Layers (NNs): These layers consist of input, hidden, and output layers that process and pass information step by step, allowing the model to generate coherent, human-like text.
LLM Training Phases
Pretraining: This initial phase is where LLMs are trained on vast datasets, typically gathered from various domains such as books, websites, and other text sources. At this stage, the model is general-purpose and capable of performing various tasks.
Fine Tuning: In this phase, the model is fine-tuned to perform specific tasks by training it on high-quality, task-specific datasets. This allows the LLM to respond more accurately to specialized instructions.
Reinforcement Learning: This phase is similar to fine-tuning but involves continuous optimization based on human feedback and quality criteria.
What Are the Different Types of LLM Models?
Zero-Shot Models: Capable of performing tasks without specific pretraining. For example, they can translate text from one language to another without training in translation examples.
Fine-Tuned Models: These are domain-specific models trained for particular tasks, such as customer support chatbots.
Language Representation Models: Fine-tuned to understand and generate natural language for various NLP tasks.
Multimodal Models: Handles multiple input forms, such as text, audio, video, and images.
Prompt Tuning
Prompt tuning involves adjusting the text instructions given to the model to guide it toward the desired output. This can be done using zero-shot learning (no examples provided) or few-shot learning (providing a few examples alongside the instructions). Being specific with your prompts helps achieve better results.
Hyperparameter Tuning
Hyperparameter tuning is optimizing the parameters of an LLM during training to achieve the best possible results. Common methods include random search, grid search, and Bayesian search. Here are some key hyperparameters:
Number of Epochs: How often does the model see the full dataset? Too many epochs can lead to overfitting, while too few result in underfitting.
Learning Rate: Controls how quickly the model updates its parameters. A higher learning rate speeds up training but can cause instability, while a lower rate leads to slower but more stable learning.
Batch Size: Determines how much data the model processes per step. Larger batch sizes can speed up training but may reduce generalization.
Top-K: Limits the model’s output to the top-k most probable tokens, helping reduce incoherent or irrelevant output.
Top-P: Filters tokens based on cumulative probability, allowing for more diverse output while avoiding low-probability results.
Max Output Tokens: Controls the maximum number of tokens the model can generate in its output.
Temperature: Controls the model’s creativity. Lower values make the output more predictable, while higher values allow for more diversity.
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What Are the Applications of LLMs?
LLMs are used for a wide variety of tasks. Let’s explore some of the most common ones:
Information Retrieval: Every time we use a search engine, we rely on an LLM to retrieve information, summarize it, and communicate it back as a response to the query.
Classification & Categorization: LLMs can classify and categorize different pieces of text information into meaningful groups, helping with organization and quick access.
Question Answering: LLMs are commonly used for answering questions and retrieving information, both with and without the explicit context being provided.
Summarization: They can take large amounts of text and distill it into a shorter summary, making it easier to digest key information.
Translation: LLMs excel at translating text between different languages, improving global communication.
Prediction: Based on the context and instructions provided, LLMs can reason and make predictions about outcomes or future events.
Content Generation & Rewriting: LLMs can generate new content or rewrite existing text based on specific goals or requirements in a prompt.
Sentiment Analysis: They can analyze text to detect the underlying sentiment or intent, which is valuable in understanding customer feedback or public opinion.
Chatbots & Conversational AI: LLMs are especially useful in customer support as chatbots, providing quick responses to user queries or even acting as conversational partners on various topics.
Code Generation: Some LLMs are designed to generate high-quality code across different programming languages, assisting developers.
Transcription: LLMs can transcribe audio or video content, such as webinars or customer service calls, to extract insights, perform sentiment analysis, and create derivative content.
Content Editing & Enhancement: LLMs can edit or enhance text to meet specific requirements, improving readability and minimizing errors.
LLMs Across Industries
LLMs are used across nearly every industry, offering practical and innovative solutions to various challenges. Here are a few common examples across different industries:
Tech: LLMs have made significant advances in tech by improving search engines’ response to queries, helping them better understand user intent and deliver more relevant results. Developers benefit from tools like GitHub Copilot, which assist with code suggestions, saving time and effort.
Healthcare and Science: In healthcare and science, LLMs analyze complex biological data, such as proteins, molecules, DNA, and RNA, driving breakthroughs in drug discovery and vaccine development. They also support patient care by powering medical chatbots that handle patient intake and provide basic diagnoses.
Customer Service: LLMs are used in customer service through chatbots and conversational AI. Businesses use them to handle customer queries efficiently, offering 24/7 support across industries. They provide personalized responses, enhancing the customer experience.
Marketing: LLMs help marketing teams perform tasks like sentiment analysis and gauging customer reactions to products or campaigns. They can also generate content quickly, allowing teams to develop strategies or draft marketing materials.
Legal: In the legal industry, LLMs streamline tasks by rapidly processing massive amounts of legal texts and documents. They can assist legal professionals by drafting legal documents or even generating contracts.
Banking and Finance: In banking and finance, LLMs help detect fraud. By analyzing transaction data, they can more effectively identify anomalies and alert systems to potential fraud. Additionally, LLMs assist in automating routine tasks like loan application processing and customer service interactions.
Advantages and Challenges with LLMs
Modern LLMs bring a wide range of advantages but also have certain challenges. Let’s explore both sides:
Advantages
Extensibility: LLMs are highly flexible and adaptable, making them easy to fine-tune for specific use cases. Organizations can customize them with minimal effort to meet new requirements or changes.
Efficiency: LLMs help employees complete routine tasks more quickly, increasing productivity and efficiency.
Performance: Modern LLMs provide high-performance results, offering low-latency, real-time responses.
Accuracy: Due to their increased complexity, a vast number of parameters, and ability to train on massive datasets, LLMs deliver highly accurate outcomes in a wide variety of tasks.
Ease of Training: LLMs are relatively easy to fine-tune since they are pre-trained on large, unlabelled datasets. Fine-tuning for specific tasks doesn’t require much additional data.
Fast Learning: They excel at in-context learning, requiring only a few examples to perform well on a given task.
Versatility: LLMs can perform various tasks across different industries, meaning almost every business can benefit from their capabilities.
Challenges
Costs: Developing, training, and maintaining an LLM can be expensive, with operational costs running into millions of dollars due to the need for high-end GPUs and vast datasets.
Ethical Considerations: LLMs often raise ethical concerns regarding data privacy and intellectual property. Since they are trained on large, publicly available datasets without consent, they may replicate content without proper authorization, raising privacy and copyright issues.
Bias: There’s always a risk of bias, as LLMs may learn from a biased dataset, leading to unfair results.
Complexity: The billions of parameters that make up LLMs also make them highly complex, making debugging and troubleshooting difficult.
Explainability: The complex architecture of LLMs makes it challenging to explain how a specific result was generated, posing issues for transparency.
Hallucinations: LLMs can sometimes produce false or nonsense results (hallucinations), particularly when data quality or context is lacking.
Security Risks: LLMs carry risks, including the possibility of generating false information or being exploited for phishing and other malicious attacks.
Environmental Impact: The computational power required to train and run LLMs has a significant environmental impact, contributing to greenhouse gas emissions.
Real Example of LLMs in Action
In the previous section, we explored how LLMs can significantly streamline compliance tasks and help detect fraud through their information retrieval and analysis capabilities. Now, let’s dive into a real-world use case we worked on for one of our clients, where we leveraged these LLM capabilities to build a fully automated pipeline for information retrieval and fraud detection. This solution provided immense value to their compliance department, particularly in identifying potential bribes within invoices.
Invoicing Use Case
Our work centered on a compliance-focused use case involving the detection of potential bribes in invoices. The goal was to develop a fully automated pipeline that could take an invoice (whether in PDF, image, or other formats), extract relevant information from it, and ultimately predict whether the invoice might contain any indication of a bribe. The challenge was heightened because the invoices could be in various languages and formats. However, with the capabilities of LLMs, we had the tools to overcome these complexities.
LLMs Based Solution
Our pipeline incorporated multiple models, including OCR and translation, to handle diverse invoice formats and languages. However, two key models deserve particular attention for their roles in information extraction and fraud prediction:
Mixtral 8x7b ModelÂ
This model is highly versatile and fine-tuned for various tasks, including information extraction. In our pipeline, we pass the raw OCR-extracted text to the Mixtral LLM, which processes the data and returns a well-structured JSON output. This JSON contains item-level details extracted from the invoice. We include a pre-defined JSON schema within the prompt to ensure consistency and accuracy, helping the model produce the most relevant and structured response.
Mistral Large Model
The Mistral Large model is a cutting-edge LLM that plays a critical role in making the final predictions. After the information is extracted, we pass it through the Mistral model along with clearly defined instructions in the prompt. The model evaluates the data and predicts whether the invoice contains any elements indicative of a potential bribe. Importantly, it also provides an explanation and reasoning behind its prediction, adding a layer of transparency to the process.
Building this automated LLM-based pipeline has greatly reduced the time and effort required by the compliance team. Previously, they had to manually process each invoice, handling tasks like translation, information extraction, and analyzing for signs of bribery. Now, with this automated system in place, they can process invoices within seconds without any human intervention. The solution enhances efficiency and ensures more consistent and accurate results, allowing the compliance team to focus on higher-level tasks.
What is the Future Outlook of LLMs?
The future of LLMs looks bright, and they will likely continue to surpass human capabilities in many areas. As hardware improves and research into LLM optimization progresses, future models will become even more efficient, accurate, and reliable.
We’ll likely see more advanced multimodal LLMs that can seamlessly process and generate text, images, audio, and video, opening up new possibilities in various industries. These models will be capable of handling routine tasks, similar to how robots revolutionized manufacturing. Conversational AI and virtual assistants will play increasingly significant roles in our daily lives, helping with everything from customer support to personal tasks.
Overall, the potential of LLMs is vast, and their applications will only continue to grow, making them an integral part of modern business and technology.
If you’re considering or ready to implement LLMs into your business, phData can help! With experience from conception to production across multiple verticals, phData can help fulfill your AI ambitions. Attend one of our free generative AI workshops to learn more!
