LLM Embeddings Explained

The strength of Large Language Models (LLMs) is rooted in their structure and the way information flows through their components. The initial step in this process is tokenization, where the input data, whether it’s text, image, video, or image search is divided into smaller units or tokens.

For instance, when text is used as input, tokens could be a complete phrase, a word, a part of a word, a symbol, or even a single character, depending on the tokenization process. In the sentence “Good Morning, Fred!”, the tokens could be [“Good”, “Morning”, “,”, “Fred”, “!”] based on the tokenization method used. Similarly, when images are used as input, tokens are formed by dividing the original image into pixel groups, each of which can be considered a token.

For example, an image of a landscape could be broken down into tokens representing different sections of the image, such as the sky, trees, and ground. When a tokenizer processes the input data, it encodes it according to a specific scheme and produces vectors that the LLM can comprehend. The encoding scheme is largely dependent on the LLM. Different methods can be used for tokenization. For instance, OpenAI GPT’s Byte-Pair Encoding (BPE) is a widely used tokenizer for text processing. Conversely, Vision Transformer (ViT) and BERT for Image Transformers (BEiT), are popular tokenizer for visual processing.

If tokens are vector representations of the input data, embeddings are tokens with semantic context. They convey the meaning, context, and relationships of the tokens. An embedding model generates embeddings in the form of a high-dimensional vector if tokens are encoded or decoded by a tokenizer. Embeddings enable LLMs to comprehend the context, nuances, and subtle meanings of the input data. They are the product of the model learning from vast amounts of data and encode not just the identity of a token but its relationships with other tokens.

Embeddings can be Uni-modal or Multi-modal. Uni-modal embeddings are generated from a single type or dimension of input data, such as text, images, or videos. They capture the semantic context of the data within its modality.
On the other hand, multimodal embedding models are generated from multiple types of input data. They capture the semantic context across different modalities, enabling the model to understand the relationships and interactions between different types of data.

The Role of the Attention Mechanism in LLMs

The attention mechanism is a key part of LLMs. As the LLM model interprets the input data, not all components hold the same importance for comprehending the context or meaning. Some components are more significant than others. This is where the attention mechanism steps in. It allocates varying weights to the embeddings of different tokens, depending on their relevance to the context.

For instance, in the phrase “The captain, against the suggestions of his crew, chose to save the pirate because he was touched by his tale”, the words “captain”, “save”, and “pirate” are key to understanding the overall meaning. The attention mechanism would allocate higher weights to the embeddings of these words.

Enhancing Sequential Models with Attention

In a traditional sequential model, by the time the model processes “save”, the “memory” of the “captain” might have diminished. However, the attention mechanism overcomes this by considering all words simultaneously and allocating weights based on their relevance, irrespective of their position in the phrase. This enables the model to understand that it was the “captain” who decided to “save” the pirate, leading to a more precise representation, and understanding of the phrase.

Similarly, in a video, the attention mechanism plays a crucial role in understanding and interpreting the content. A video is a complex combination of numerous frames, each containing multiple elements. These elements could be objects, people, actions, or even subtle changes in lighting and color. Not all these elements are equally important for understanding the context or the narrative of the video.

Attention in Video Interpretation

The attention mechanism, in this case, assigns different weights to the embeddings of different tokens, which could represent various elements within the video frames. For instance, in a video of a bustling cityscape, the attention mechanism might assign higher weights to the tokens representing the main subjects of the video, such as a prominent building, a moving car, or a person interacting with others.

At the same time, it might assign lower weights to the tokens representing the background or less significant elements, like the sky, stationary objects, or the general crowd. In a traditional sequential model, by the time the model processes the later frames of the video, the “memory” of the earlier frames might have faded.

However, the attention mechanism overcomes this by considering all frames at once and assigning weights based on their relevance, regardless of their position in the sequence. This allows the embedding model to understand the continuity and relationship between different parts of the video, such as the movement of the car from one frame to another or the interaction of the person throughout the video.

By doing so, the attention mechanism leads to a more accurate representation and understanding of the video. It enables the model to grasp the narrative of the video, understand the significance of different elements, and recognize the relationships and interactions between them, thereby enhancing the overall interpretation of the video content.

Pre-Training and Transfer Learning in LLMs

Pre-training and transfer learning are two pivotal stages in the training of Large Language Models (LLMs) for text, image, and video.

In the pre-training phase, the model is trained on a vast corpus of data. This could be a diverse collection of books, websites, image files, videos, and other multimedia content. The goal of this phase is to learn the general patterns and structures of the data.

For text, the model learns to predict the next word in a sentence; for images, it might learn to identify common shapes or colors; and for videos, it could learn to predict the next frame or sequence. This helps the model understand the context and semantics of various elements, whether they are words, visual components, or video sequences. At the end of pre-training, the model has a broad understanding of the data but is not yet specialized in any task.

Fine-Tuning Through Transfer Learning

This is where transfer learning comes in. After pre-training, the model is further trained on a smaller, task-specific dataset. This could be a collection of customer service interactions for a chatbot, a set of facial expressions for a facial recognition system, or a series of medical videos for a surgical training tool. The model is fine-tuned on this dataset, which means it adjusts the weights and biases it learned during pre-training based on the new data.

The key idea behind transfer learning is that the knowledge the model gained during pre-training can be “transferred” and adapted to a specific task. The general patterns, values, and structures the model learned during pre-training provide a good starting point, and the model only needs to adjust this knowledge slightly to perform well on the specific task.

This process is much more efficient than training a model from scratch on the task-specific data. It requires less data and computational resources, and it often results in better performance, especially when the task-specific data is limited.

In summary, pre-training gives the model a broad understanding of the data, whether it’s text, image, or video, and transfer learning fine-tunes this knowledge for a specific task. This two-stage process is a key reason for the impressive LLM performance on a wide range set of tasks across different data types.

Embeddings are a powerful tool in text, image, and video processing, enabling “data objects” with dimensions and similar “meanings” to have similar representations.
Next, we will present cases of the most popular embedding techniques in the context of text data.

Let’s start with basic embedding techniques like one-hot encoding and frequency-based methods such as TF-IDF (Term Frequency-Inverse Document Frequency) and count vectors. In one-hot encoding, each word in the vocabulary is represented as a unique vector in a high-dimensional space, the size and length of which is equal to the vocabulary size.

For instance, if we have a vocabulary of three words: “king”, “queen”, and “building”, the one-hot encoding might look like this: “king”: [1, 0, 0], “queen”: [0, 1, 0], and “building”: [0, 0, 1]. Each word is represented as a binary vector with a “1” at its corresponding index and “0” elsewhere. Even though each word is perfectly distinguishable from others (perpendicular vector representations in the 3D space), this method fails to capture any semantic or syntactic relationships between words. For example, “king” and “queen”, which are semantically related, are as different as “king” and “building” in this representation.

Exploring Frequency-Based Methods: TF-IDF

TF-IDF, a frequency-based method, represents words based on their frequency in a document compared to their frequency across all documents. While it captures some semantic information, it falls short in capturing syntactic relationships and context.

Let’s assume that in our corpus, “king” and “queen” often appear together in documents about royalty, while “building” appears in a different context. The TF-IDF scores might look something like “king”: 0.8, “queen”: 0.8, and “building”: 0.3. Here, “king” and “queen” have similar TF-IDF scores because they appear frequently in the same documents, while “building” has a lower score because it appears in a different context. However, these scores still don’t capture the semantic relationship between “king” and “queen”, or the syntactic relationships between words in a sentence.

Advanced embedding techniques like Word2Vec, GloVe, and FastText have significantly enhanced word representation. Word2Vec captures semantic and syntactic relationships based on word co-occurrence. GloVe combines co-occurrence information with direct context prediction, while FastText, an extension of Word2Vec, captures the meaning of shorter words and affixes effectively. Let’s consider the case of GloVe and assume that the GloVe embeddings are two-dimensional for simplicity.

The Power of Context in GloVe Embeddings

The embeddings for “king”, “queen”, and “building” might look something like “king”: [1.2, 0.9], “queen”: [1.1, 0.95], and “building”: [0.3, -0.2].

Here, “king” and “queen” have similar embeddings because they often co-occur in the same context, indicating a close semantic relationship. On the other hand, “building” has a different embedding because it appears in a different context and does not co-occur frequently with “king” or “queen”. This shows how GloVe can capture both semantic and syntactic relationships, providing a much richer representation of language compared to TF-IDF.

The latest advancements in embeddings come from transformer-based models like BERT, BART, XLNet, Longformer, and GPT. These models generate context-aware embeddings, where the representation of a word depends on its context, not just its identity.

Contextualization with Transformer-Based Models

GPT, for instance, infuses its embeddings with positional encodings, ensuring the order of words is preserved. As the input text passes through the transformer layers of GPT, the embeddings evolve, absorbing more context and enriching their representation. By the end, what emerges is a vector representation for each token that is deeply contextualized, reflecting not just the token itself but its relationship with every other token in the sequence.

For example, if we consider two-dimensional embeddings, the embeddings for “king”, “queen”, and “building” might look like this:

“king” in the context of “The king and queen”: [1.2, 0.9], “queen” in the context of “The king and queen”: [1.1, 0.95], and “building” in combination with the context of “The falling building”: [0.3, -0.2].

Here, “king” and “queen” have similar embeddings because they appear in a similar context and are semantically related, while “building” has a different embedding because it appears in a different context and is not related to “king” or “queen”.

This demonstrates how transformer-based models can capture semantic and syntactic relationships between words, providing a much richer representation of language compared to one-hot encoding.

These transformer-based models have set new performance benchmarks on a range of NLP tasks, demonstrating the power of these advanced embedding techniques.

Embeddings are a cornerstone in the application and implementation of Generative AI, serving as the foundation for a wide array of tasks across text, audio, image search, and video domains.

In the text domain, embeddings are used in a variety of Natural Language Processing (NLP) tasks. For instance, in text classification tasks such as sentiment analysis or spam detection, embeddings are used to convert the input text into a form that can be processed by the model. The model then learns to associate certain patterns in the embeddings with certain classes (e.g., positive, or negative sentiment, spam or not spam).

In text summarization, embeddings are used to capture the semantic content of the text. The model learns to generate a shorter version of the text that preserves the main points, based on the patterns it recognizes in the embeddings.

In machine translation, embeddings play a crucial role in capturing the semantic and syntactic relationships between words in different languages, allowing the model to translate text accurately. In text generation tasks, such as answer generation in response to a user request, embeddings are used to generate new text that is semantically and syntactically coherent with the user request.

Embeddings Across Audio and Video Processing

In the audio domain, embeddings are used in tasks like speech recognition, music classification, and audio generation. In speech recognition, the audio input is converted into a spectrogram, which is then transformed into embeddings. These embeddings capture the unique characteristics of the speaker’s voice and the words they’re saying, allowing the model to transcribe the audio accurately.

In music classification, embeddings can capture the features of different musical notes and sequences, enabling the model to classify the music into different genres. In audio generation, embeddings can capture the features of different sounds, allowing the model to generate new sounds or create them that are consistent with the existing ones.

In the video domain, embeddings are used in tasks like object detection, action recognition, and video generation. In object detection, embeddings can capture the features of different objects in the video, allowing the model to identify and locate these objects.

In action recognition, embeddings can capture the features of different actions, enabling the model to recognize and classify these actions. In video generation, embeddings can capture the features of different frames, allowing the model to generate new frames that are consistent with the previous ones, resulting in a coherent video.

In all these applications, embeddings serve as the bridge between the raw data and the model, transforming the data into a form that the model can understand and learn from. This enables the model to recognize patterns in the data and generate new data that follows these patterns, thereby achieving the desired task.

It is important to note that not all input data is the same, and as such, the methods for embeddings differ remarkably even within the same data type. For instance, the techniques used for text processing are not the same as those used for video processing. This is because the nature of the data and the information it carries varies, necessitating different approaches to effectively capture and represent this information.

Moreover, there is always a tradeoff between precision, memory, and CPU consumption/cost. High precision often comes at the expense of increased memory usage and CPU consumption. This is because more complex models that can capture finer details and nuances in the data require more computational resources.

For instance, transformer-based models like BERT and GPT, which generate highly contextualized embeddings, are more resource-intensive than simpler models like one-hot encoding or TF-IDF. However, the increased precision they offer often justifies the additional cost.

Looking ahead, the field of embeddings is ripe for further exploration and innovation. As we continue to develop more sophisticated models and techniques, we can expect to see improvements in precision, efficiency, and the ability to handle different types of data. This will enable us to tackle more complex language processing tasks and extract deeper insights from our data.

Large Foundational Models (LFMs) and Large Language Models (LLMs) are driving advancements in AI, particularly in natural language processing, image recognition, and audio processing. The strength of these models lies in their structure and the way information flows through their components, including tokenization, embeddings, attention mechanisms, pre-training, and transfer learning.

Embeddings play a crucial role in these models, providing a way to represent input data in a form that the model can understand and learn from. They capture the semantic and syntactic relationships between tokens, enabling the model to comprehend the context and nuances of the data. The field of embeddings is continually evolving, with advanced techniques like transformer-based models offering highly contextualized embeddings.

As we continue to work together to innovate in this field, we can expect to see improvements in precision, efficiency, and the ability to handle different types of data, opening new possibilities for complex language processing tasks and deeper data insights.

Book a free AI demo to experience Aisera’s enterprise LLM today!