LLM (Large Language Model) has undoubtedly been one of the most buzzing topics over the past two years, since the release of ChatGPT by OpenAI.
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Large Language Models are essentially sophisticated AI systems designed to understand and generate human-like text. What makes them large” is the sheer volume of data they’re trained on and the billions of parameters they use to capture the nuances of human language. But remember, while they can generate human-like text, machines don’t “understand” language in the way humans do. Instead, they process data as numbers, thanks to a technique called Natural Language Processing (NLP).
Today, we’ll cover the key NLP techniques used to prepare text data into a machine-readable form for use in LLMs, starting with text pre-processing.
๐๐ฒ๐ ๐ฆ๐๐ฒ๐ฝ๐ ๐ถ๐ป ๐ง๐ฒ๐
๐ ๐ฃ๐ฟ๐ฒ-๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด:
1๏ธโฃ ๐ง๐ผ๐ธ๐ฒ๐ป๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
Tokenization is where it all begins. The model breaks down text into smaller units called tokens, which could be words or even sub-words. For example, the sentence “Working with NLP is tricky” becomes [“Working”, “with”, “NLP”, “is”, “tricky”, “.”]. This step is crucial because it allows the model to understand input text in a structured way that can be processed numerically.
2๏ธโฃ ๐ฆ๐๐ผ๐ฝ ๐๐ผ๐ฟ๐ฑ ๐ฟ๐ฒ๐บ๐ผ๐๐ฎ๐น
Not every word in a sentence carries significant meaning. Stop words like “with” and “is” are common across many sentences but add little to the meaning. By removing these, the model can focus on the more meaningful parts of the text, enhancing efficiency and accuracy.
3๏ธโฃ ๐๐ฒ๐บ๐บ๐ฎ๐๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
Lemmatization simplifies words to their base form, making it easier for the model to understand the context without getting bogged down by variations. For instance, words like “talking”, “talked”, and “talk” all get reduced to their root form “talk.
We are then ready for the next step, which is to change the text into a form the computer can understand.
Embeddings & Vectors
Vector embeddings are one of the most fascinating and useful concepts in machine learning. They are central to many NLP, recommendation, and search algorithms.
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Word embeddings are a type of representation that allows words with similar meanings to have similar representations. Think of them as vectors in a high-dimensional space where each dimension captures a different aspect of the word’s meaning.
Simply put, words possessing similar meanings or often occuring together in similar contexts, will have a similar vector representation, based on how โcloseโ or โfar apartโ those words are in their meanings.
๐ ๐ณ๐ฎ๐บ๐ผ๐๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ
Consider the equation: “king” โ โmanโ + โwomenโ = “queen”. This example illustrates how word embeddings can capture complex semantic relationships. The vector operations translate semantic similarity as perceived by humans into proximity in a vector space.
๐๐ฒ๐๐ผ๐ป๐ฑ ๐ช๐ผ๐ฟ๐ฑ๐: ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด๐
In other words, when we represent real-world objects and concepts such as images, audio recordings, news articles, user profiles, weather patterns, and political views as vector embeddings, the semantic similarity of these objects and concepts can be quantified by how close they are to each other as points in vector spaces. Vector embedding representations are thus suitable for common machine learning tasks such as clustering, recommendation, and classification.
๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐
Once you have these embeddings, you need a way to store and query them efficiently. This is where Vector Databases come in. Vector databases are designed to handle high-dimensional data, making them perfect for storing and retrieving word embeddings.
Fine-Tuning vs. Pre-Training
๐ฏ What is Fine-Tuning?
Think of fine-tuning as the process of specializing in a specific domain, like a college student focusing on medicine. It builds upon the foundation of pre-trained models to adapt them for specific tasks.
๐๏ธโโ๏ธ Overcoming “Largeness” Challenges
LLMs are powerful but come with challenges like high computational costs, extensive training time, and the need for vast amounts of high-quality data. Fine-tuning helps overcome these obstacles by:
1๏ธโฃ Reducing computational power requirements
2๏ธโฃ Shortening training time from weeks or months to hours or days
3๏ธโฃ Requiring less data, typically only a few hundred megabytes to a few gigabytes
๐ง Fine-Tuning vs. Pre-Training
While pre-training demands thousands of CPUs and GPUs, fine-tuning can be done with just a single CPU and GPU. Plus, fine-tuning takes significantly less time and data compared to pre-training!
Fine-Tuning & transfer learning
Fine-tuning a pre-trained LLM involves training it on a smaller, task-specific dataset to boost performance. But what happens when labeled data is scarce? Enter zero-shot, few-shot, and multi-shot learningโcollectively known as N-shot learning techniques.
๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ฒ๐ฟ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
These techniques fall under the umbrella of transfer learning. Just like skills from piano lessons can be applied to learning guitar, transfer learning involves leveraging knowledge from one task to enhance performance on a related task. For LLMs, this means fine-tuning on new tasks with varying amounts of task-specific data.
โ๐ Zero-Shot Learning
Zero-shot learning enables LLMs to tackle tasks they haven’t been explicitly trained on. Imagine a child identifying a zebra based on descriptions and knowledge of horses. LLMs use zero-shot learning to generalize knowledge to new situations without needing specific examples.
๐ Few-Shot Learning
Few-shot learning allows models to learn new tasks with minimal examples. Think of a student answering a new exam question based on prior knowledge from lectures. When only one example is used, it’s called one-shot learning.
๐๐๐ Multi-Shot Learning
Multi-shot learning is similar to few-shot learning but requires more examples. It’s like showing a model several pictures of a Golden Retriever to help it recognize the breed and generalize to similar breeds with additional examples.
These techniques make LLM more adaptable and efficient even with limited data ๐ก
Transformers
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Introduced in the paper “Attention Is All You Need” 7 years ago, transformers emphasize long-range relationships between words to generate accurate and coherent text.
๐๐ผ๐ ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ ๐ช๐ผ๐ฟ๐ธ
Let’s consider an example sentence: “Jane, who lives in New York and works as a software engineer, loves exploring new restaurants in the city.”
1๏ธโฃText Pre-processing:
The transformer breaks down the sentence into tokens (e.g., “Jane,” “who,” “lives,” etc.) and converts them into numerical form using word embeddings.
2๏ธโฃPositional Encoding:
Adds information about the position of each word in the sequence, helping the model understand the context and relationships between distant words.
3๏ธโฃEncoders:
Use attention mechanisms and neural networks to encode the sentence, focusing on specific words and their relationships.
4๏ธโฃDecoders:
Process the encoded input to generate the final output, such as predicting the next word in the sequence. Why Transformers are Special
๐๐ผ๐ป๐ด-๐ฅ๐ฎ๐ป๐ด๐ฒ ๐๐ฒ๐ฝ๐ฒ๐ป๐ฑ๐ฒ๐ป๐ฐ๐ถ๐ฒ๐
Transformers excel at capturing relationships between distant words. For instance, they can understand the connection between “Jane” and “loves exploring new restaurants,” even though these words are far apart in the sentence.
๐ฆ๐ถ๐บ๐๐น๐๐ฎ๐ป๐ฒ๐ผ๐๐ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด
Unlike traditional models that process one word at a time, transformers can handle multiple parts of the input text simultaneously. This speeds up the process of understanding and generating text.
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Fostering and enhancing impactful collaborations with a diverse array of AI partners, spanning Independent Software Vendors (ISVs), Startups, Managed Service Providers (MSPs), Global System Integrators (GSIs), subject matter experts, and more, to deliver added value to our joint customers.
๐ CAREER JOURNEY โ Got a Master degree in IT in 2007 and started career as an IT Consultant
In 2010, had the opportunity to develop business in the early days of the new Cloud Computing era.
In 2016, started to witness the POWER of Partnerships & Alliances to fuel business growth, especially as we became the main go-to MSP partner for healthcare French projects from our hyperscaler.
Decided to double down on this approach through various partner-centric positions: as a managed service provider or as a cloud provider, for Channels or for Tech alliances.
โก๏ธ Now happy to lead an ECOSYSTEM of AI players each bringing high value to our joint customers.