word embeddings

In the ever-evolving landscape of natural language processing (NLP), embedding techniques have played a pivotal role in enhancing the capabilities of language models.

The birth of word embeddings

Before venturing into the large number of embedding techniques that have emerged in the past few years, we must first understand the problem that led to the creation of such techniques.

Word embeddings were created to address the absence of efficient text representations for NLP models. Since NLP techniques operate on textual data, which inherently cannot be directly integrated into machine learning models designed to process numerical inputs, a fundamental question arose: how can we convert text into a format compatible with these models?

Basic approaches like one-hot encoding and Bag-of-Words (BoW) were employed in the initial phases of NLP development. However, these methods were eventually discarded due to their evident shortcomings in capturing the contextual and semantic nuances of language. Each word was treated as an isolated unit, without understanding its relationship with other words or its usage in different contexts.

Word2Vec 

In 2013, Google presented a new technique to overcome the shortcomings of the previous word embedding techniques, called Word2Vec. It represents words in a continuous vector space, better known as an embedding space, where semantically similar words are located close to each other.

This contrasted with traditional methods, like one-hot encoding, which represents words as sparse, high-dimensional vectors. The dense vector representations generated by Word2Vec had several advantages, including the ability to capture semantic relationships, support vector arithmetic (e.g., “king” – “man” + “woman” = “queen”), and improve the performance of various NLP tasks like language modeling, sentiment analysis, and machine translation.

Transition to GloVe and FastText

The success of Word2Vec paved the way for further innovations in the realm of word embeddings. The Global Vectors for Word Representation (GloVe) model, introduced by Stanford researchers in 2014, aimed to leverage global statistical information about word co-occurrences.

GloVe demonstrated improved performance over Word2Vec in capturing semantic relationships. Unlike Word2Vec, GloVe considers the entire corpus when learning word vectors, leading to a more global understanding of word relationships.

Fast forward to 2016, Facebook’s FastText introduced a significant shift by considering sub-word information. Unlike traditional word embeddings, FastText represented words as bags of character n-grams. This sub-word information allowed FastText to capture morphological and semantic relationships in a more detailed manner, especially for languages with rich morphology and complex word formations. This approach was particularly beneficial for handling out-of-vocabulary words and improving the representation of rare words.

The rise of transformer models 

The real game-changer in the evolution of embedding techniques came with the advent of the Transformer architecture. Introduced by researchers at Google in the form of the Attention is All You Need paper in 2017, Transformers demonstrated remarkable efficiency in capturing long-range dependencies in sequences.

The architecture laid the foundation for state-of-the-art models like OpenAI’s GPT (Generative Pre-trained Transformer) series and BERT (Bidirectional Encoder Representations from Transformers). Hence, the traditional understanding of embedding techniques is revamped with new solutions.

Impact of embedding techniques on language models

The embedding techniques mentioned above have significantly impacted the performance and capabilities of LLMs. Pre-trained models like GPT-3 and BERT leverage these embeddings to understand natural language context, semantics, and syntactic structures. The ability to capture context allows these models to excel in a wide range of NLP tasks, including sentiment analysis, text summarization, and question-answering.

Imagine the sentence: “The movie was not what I expected, but the plot twist at the end made it incredible.”

Traditional models might struggle with the negation of “not what I expected.” Word embeddings could capture some sentiment but might miss the subtle shift in sentiment caused by the positive turn of events in the latter part of the sentence.

In contrast, LLMs with contextualized embeddings can consider the entire sentence and comprehend the nuanced interplay of positive and negative sentiments. They grasp that the initial negativity is later counteracted by the positive twist, resulting in a more accurate sentiment analysis.

Advantages of embeddings in LLMs

• Contextual Understanding: LLMs equipped with embeddings comprehend the context in which words appear, allowing for a more nuanced interpretation of sentiment in complex sentences.

• Semantic Relationships: Word embeddings capture semantic relationships between words, enabling the model to understand the subtleties and nuances of language. 

• Handling Ambiguity: Contextual embeddings help LLMs handle ambiguous language constructs, such as negations or sarcasm, contributing to improved accuracy in sentiment analysis.

• Transfer Learning: The pre-training of LLMs with embeddings on vast datasets allows them to generalize well to various downstream tasks, including sentiment analysis, with minimal task-specific data.

How are enterprises using embeddings in their LLM processes?

In light of recent advancements, enterprises are keen on harnessing the robust capabilities of Large Language Models (LLMs) to construct comprehensive Software as a Service (SAAS) solutions. Nevertheless, LLMs come pre-trained on extensive datasets, and to tailor them to specific use cases, fine-tuning on proprietary data becomes essential.

This process can be laborious. To streamline this intricate task, the widely embraced Retrieval Augmented Generation (RAG) technique comes into play. RAG involves retrieving pertinent information from an external source, transforming it to a format suitable for LLM comprehension, and then inputting it into the LLM to generate textual output.

This innovative approach enables the fine-tuning of LLMs with knowledge beyond their original training scope. In this process, you need an efficient way to store, retrieve, and ingest data into your LLMs to use it accurately for your given use case.

One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are ‘most similar’ to the embedded query.  Hence, without embedding techniques, your RAG approach will be impossible.

Understanding the creation of embeddings

Much like a machine learning model, an embedding model undergoes training on extensive datasets. Various models available can generate embeddings for you, and each model is distinct. You can find the top embedding models here.

It is unclear what makes an embedding model perform better than others. However, a common way to select one for your use case is to evaluate how many words a model can take in without breaking down. There’s a limit to how many tokens a model can handle at once, so you’ll need to split your data into chunks that fit within the limit. Hence, choosing a suitable model is a good starting point for your use case.

Creating embeddings with Azure OpenAI is a matter of a few lines of code. To create embeddings of a simple sentence like The food was delicious and the waiter…, you can execute the following code blocks:

• First, import AzureOpenAI from OpenAI

• Load in your environment variables

• Create your Azure OpenAI client.

• Create your embeddings

And you’re done! It’s really that simple to generate embeddings for your data. If you want to generate embeddings for an entire dataset, you can follow along with the great notebook provided by OpenAI itself here.

To sum it up!

The evolution of embedding techniques has revolutionized natural language processing, empowering language models with a deeper understanding of context and semantics. From Word2Vec to Transformer models, each advancement has enriched LLM capabilities, enabling them to excel in various NLP tasks.

Enterprises leverage techniques like Retrieval Augmented Generation, facilitated by embeddings, to tailor LLMs for specific use cases. Platforms like Azure OpenAI offer straightforward solutions for generating embeddings, underscoring their importance in NLP development. As we forge ahead, embeddings will remain pivotal in driving innovation and expanding the horizons of language understanding.

Vector embeddings refer to numerical representations of data in a continuous vector space. The data points in the three-dimensional space can capture the semantic relationships and contextual information associated with them.  

With the advent of generative AI, the complexity of data makes vector embeddings a crucial aspect of modern-day processing and handling of information. They ensure efficient representation of multi-dimensional databases that are easier for AI algorithms to process. 

Key roles of vector embeddings in generative AI 

Generative AI relies on vector embeddings to understand the structure and semantics of input data. Let’s look at some key roles of embedded vectors in generative AI to ensure their functionality. 

• Improved data representation 
  Vector embeddings present a three-dimensional representation of data, making it more meaningful and compact. Similar data items are presented by similar vector representations, creating greater coherence in outputs that leverage semantic relationships in the data. They are also used to capture latent representations in input data.
   
• Multimodal data handling 
  Vector space allows multimodal creativity since generative AI is not restricted to a single form of data. Vector embeddings are representative of different data types, including text, image, audio, and time. Hence, generative AI can generate creative outputs in different forms using of embedded vectors.
   
• Contextual representation
  

  

  Generative AI uses vector embeddings to control the style and content of outputs. The vector representations in latent spaces are manipulated to produce specific outputs that are representative of the contextual information in the input data. It ensures the production of more relevant and coherent data output for AI algorithms.

   

• Transfer learning 
  Transfer learning in vector embeddings enable their training on large datasets. These pre-trained embeddings are then transferred to specific generative tasks. It allows AI algorithms to leverage existing knowledge to improve their performance.
   
• Noise tolerance and generalizability 
  Data is often marked by noise and missing information. In three-dimensional vector spaces, the continuous space can generate meaningful outputs even with incomplete information. Encoding vector embeddings cater to the noise in data, leading to the building of robust models. It enables generalizability when dealing with uncertain data to generate diverse and meaningful outputs. 

Use cases of vector embeddings in generative AI 

There are different applications of vector embeddings in generative AI. While their use encompasses several domains, following are some important use cases of embedded vectors: 

Image generation 

It involves Generative Adversarial Networks (GANs) that use embedded vectors to generate realistic images. They can manipulate the style, color, and content of images. Vector embeddings also ensure easy transfer of artistic style from one image to the other. 

Following are some common image embeddings: 

• CNNs
  They are known as Convolutional Neural Networks (CNNs) that extract image embeddings for different tasks like object detection and image classification. The dense vector embeddings are passed through CNN layers to create a hierarchical visual feature from images.
   
• Autoencoders 
  These are trained neural network models that are used to generate vector embeddings. It uses these embeddings to encode and decode images. 

Data augmentation 

Vector embeddings integrate different types of data that can generate more robust and contextually relevant AI models. A common use of augmentation is the combination of image and text embeddings. These are primarily used in chatbots and content creation tools as they engage with multimedia content that requires enhanced creativity. 

Music composition 

Musical notes and patterns are represented by vector embeddings that the models can use to create new melodies. The audio embeddings allow the numerical representation of the acoustic features of any instrument for differentiation in the music composition process. 

Some commonly used audio embeddings include: 

• MFCCs 
  It stands for Mel Frequency Cepstral Coefficients. It creates vector embeddings using the calculation of spectral features of an audio. It uses these embeddings to represent the sound content.
   
• CRNNs 
  These are Convolutional Recurrent Neural Networks. As the name suggests, they deal with the convolutional and recurrent layers of neural networks. CRNNs allow the integration of the two layers to focus on spectral features and contextual sequencing of the audio representations produced. 

Natural language processing (NLP) 

NLP uses vector embeddings in language models to generate coherent and contextual text. The embeddings are also capable of. Detecting the underlying sentiment of words and phrases and ensuring the final output is representative of it. They can capture the semantic meaning of words and their relationship within a language. 

Some common text embeddings used in NLP include: 

• Word2Vec
  It represents words as a dense vector representation that trains a neural network to capture the semantic relationship of words. Using the distributional hypothesis enables the network to predict words in a context.
   
• GloVe 
  It stands for Global Vectors for Word Representation. It integrates global and local contextual information to improve NLP tasks. It particularly assists in sentiment analysis and machine translation.
   
• BERT 
  It means Bidirectional Encoder Representations from Transformers. They are used to pre-train transformer models to predict words in sentences. It is used to create context-rich embeddings. 

Video game development 

Another important use of vector embeddings is in video game development. Generative AI uses embeddings to create game environments, characters, and other assets. These embedded vectors also help ensure that the various elements are linked to the game’s theme and context. 

Challenges and considerations in vector embeddings for generative AI 

Vector embeddings are crucial in improving the capabilities of generative AI. However, it is important to understand the challenges associated with their use and relevant considerations to minimize the difficulties. Here are some of the major challenges and considerations: 

• Data quality and quantity
  The quality and quantity of data used to learn the vector embeddings and train models determine the performance of generative AI. Missing or incomplete data can negatively impact the trained models and final outputs.
  It is crucial to carefully preprocess the data for any outliers or missing information to ensure the embedded vectors are learned efficiently. Moreover, the dataset must represent various scenarios to provide comprehensive results.
   
• Ethical concerns and data biases 
  Since vector embeddings encode the available information, any biases in training data are included and represented in the generative models, producing unfair results that can lead to ethical issues.
  It is essential to be careful in data collection and model training processes. The use of fairness-aware embeddings can remove data bias. Regular audits of model outputs can also ensure fair results.
   
• Computation-intensive processing 
  Model training with vector embeddings can be a computation-intensive process. The computational demand is particularly high for large or high-dimensional embeddings. Hence. It is important to consider the available resources and use distributed training techniques to fast processing. 

Future of vector embeddings in generative AI 

In the coming future, the link between vector embeddings and generative AI is expected to strengthen. The reliance on three-dimensional data representations can cater to the growing complexity of generative AI. As AI technology progresses, efficient data representations through vector embeddings will also become necessary for smooth operation. 

Moreover, vector embeddings offer improved interpretability of information by integrating human-readable data with computational algorithms. The features of these embeddings offer enhanced visualization that ensures a better understanding of complex information and relationships in data, enhancing representation, processing, and analysis. 

Hence, the future of generative AI puts vector embeddings at the center of its progress and development. 

Embeddings are a key building block of large language models. For the unversed, large language models (LLMs) are composed of several key building blocks that enable them to efficiently process and understand natural language data.

A large language model (LLM) is a type of artificial intelligence model that is trained on a massive dataset of text. This dataset can be anything from books and articles to websites and social media posts. The LLM learns the statistical relationships between words, phrases, and sentences in the dataset, which allows it to generate text that is similar to the text it was trained on.

How is a large language model built?

LLMs are typically built using a transformer architecture. Transformers are a type of neural network that are well-suited for natural language processing tasks. They are able to learn long-range dependencies between words, which is essential for understanding the nuances of human language.

LLMs are so large that they cannot be run on a single computer. They are typically trained on clusters of computers or even on cloud computing platforms. The training process can take weeks or even months, depending on the size of the dataset and the complexity of the model.

Key building blocks of large language model

1. Embeddings

Embeddings are continuous vector representations of words or tokens that capture their semantic meanings in a high-dimensional space. They allow the model to convert discrete tokens into a format that can be processed by the neural network. LLMs learn embeddings during training to capture relationships between words, like synonyms or analogies.

2. Tokenization

Tokenization is the process of converting a sequence of text into individual words, subwords, or tokens that the model can understand. LLMs use subword algorithms like BPE or wordpiece to split text into smaller units that capture common and uncommon words. This approach helps to limit the model’s vocabulary size while maintaining its ability to represent any text sequence.

3. Attention

Attention mechanisms in LLMs, particularly the self-attention mechanism used in transformers, allow the model to weigh the importance of different words or phrases. By assigning different weights to the tokens in the input sequence, the model can focus on the most relevant information while ignoring less important details. This ability to selectively focus on specific parts of the input is crucial for capturing long-range dependencies and understanding the nuances of natural language.

4. Pre-training

Pre-training is the process of training an LLM on a large dataset, usually unsupervised or self-supervised, before fine-tuning it for a specific task. During pretraining, the model learns general language patterns, relationships between words, and other foundational knowledge.

The process creates a pretrained model that can be fine-tuned using a smaller dataset for specific tasks. This reduces the need for labeled data and training time while achieving good results in natural language processing tasks (NLP).

5. Transfer learning

Transfer learning is the technique of leveraging the knowledge gained during pretraining and applying it to a new, related task. In the context of LLMs, transfer learning involves fine-tuning a pretrained model on a smaller, task-specific dataset to achieve high performance on that task. The benefit of transfer learning is that it allows the model to benefit from the vast amount of general language knowledge learned during pretraining, reducing the need for large labeled datasets and extensive training for each new task.

Understanding embeddings

Embeddings are used to represent words as vectors of numbers, which can then be used by machine learning models to understand the meaning of text. Embeddings have evolved over time from the simplest one-hot encoding approach to more recent semantic embedding approaches.

Types of embeddings

Classic approaches to embeddings

In the early days of natural language processing (NLP), embeddings were simply one-hot encoded. Zero vector represents each word with a single one at the index that matches its position in the vocabulary.

1. One-hot encoding

One-hot encoding is the simplest approach to embedding words. It represents each word as a vector of zeros, with a single one at the index corresponding to the word’s position in the vocabulary. For example, if we have a vocabulary of 10,000 words, then the word “cat” would be represented as a vector of 10,000 zeros, with a single one at index 0.

One-hot encoding is a simple and efficient way to represent words as vectors of numbers. However, it does not take into account the context in which words are used. This can be a limitation for tasks such as text classification and sentiment analysis, where the context of a word can be important for determining its meaning.

For example, the word “cat” can have multiple meanings, such as “a small furry mammal” or “to hit someone with a closed fist.” In one-hot encoding, these two meanings would be represented by the same vector. This can make it difficult for machine learning models to learn the correct meaning of words.

2. TF-IDF

TF-IDF (term frequency-inverse document frequency) is a statistical measure that is used to quantify the importanceThe process creates a pretrained model that can be fine-tuned using a smaller dataset for specific tasks. This reduces the need for labeled data and training time while achieving good results in natural language processing tasks (NLP). of a word in a document. It is a widely used technique in natural language processing (NLP) for tasks such as text classification, information retrieval, and machine translation.

TF-IDF is calculated by multiplying the term frequency (TF) of a word in a document by its inverse document frequency (IDF). TF measures the number of times a word appears in a document, while IDF measures how rare a word is in a corpus of documents.

The TF-IDF score for a word is high when the word appears frequently in a document and when the word is rare in the corpus. This means that TF-IDF scores can be used to identify words that are important in a document, even if they do not appear very often.

Understanding TF-IDF with example

Here is an example of how TF-IDF can be used to create word embeddings. Let’s say we have a corpus of documents about cats. We can calculate the TF-IDF scores for all of the words in the corpus. The words with the highest TF-IDF scores will be the words that are most important in the corpus, such as “cat,” “dog,” “fur,” and “meow.”

We can then create a vector for each word, where each element of the vector represents the TF-IDF score for that word. The TF-IDF vector for the word “cat” would be high, while the TF-IDF vector for the word “dog” would also be high, but not as high as the TF-IDF vector for the word “cat.”

The TF-IDF word embeddings can then be used by a machine-learning model to classify documents about cats. The model would first create a vector representation of a new document. Then, it would compare the vector representation of the new document to the TF-IDF word embeddings. The document would be classified as a “cat” document if its vector representation is most similar to the TF-IDF word embeddings for “cat.”

Count-based and TF-IDF 

To address the limitations of one-hot encoding, count-based and TF-IDF techniques were developed. These techniques take into account the frequency of words in a document or corpus.

Count-based techniques simply count the number of times each word appears in a document. TF-IDF techniques take into account both the frequency of a word and its inverse document frequency.

Count-based and TF-IDF techniques are more effective than one-hot encoding at capturing the context in which words are used. However, they still do not capture the semantic meaning of words.

Capturing local context with N-grams

To capture the semantic meaning of words, n-grams can be used. N-grams are sequences of n-words. For example, a 2-gram is a sequence of two words.

N-grams can be used to create a vector representation of a word. The vector representation is based on the frequencies of the n-grams that contain the word.

N-grams are a more effective way to capture the semantic meaning of words than count-based or TF-IDF techniques. However, they still have some limitations. For example, they are not able to capture long-distance dependencies between words.

Semantic encoding techniques

Semantic encoding techniques are the most recent approach to embedding words. These techniques use neural networks to learn vector representations of words that capture their semantic meaning.

One of the most popular semantic encoding techniques is Word2Vec. Word2Vec uses a neural network to predict the surrounding words in a sentence. The network learns to associate words that are semantically similar with similar vector representations.

Semantic encoding techniques are the most effective way to capture the semantic meaning of words. They are able to capture long-distance dependencies between words and they are able to learn the meaning of words even if they have never been seen before. Here are some other semantic encoding techniques:

1. ELMo: Embeddings from language models

ELMo is a type of word embedding that incorporates both word-level characteristics and contextual semantics. It is created by taking the outputs of all layers of a deep bidirectional language model (bi-LSTM) and combining them in a weighted fashion. This allows ELMo to capture the meaning of a word in its context, as well as its own inherent properties.

The intuition behind ELMo is that the higher layers of the bi-LSTM capture context, while the lower layers capture syntax. This is supported by empirical results, which show that ELMo outperforms other word embeddings on tasks such as POS tagging and word sense disambiguation.

ELMo is trained to predict the next word in a sequence of words, a task called language modeling. This means that it has a good understanding of the relationships between words. When assigning an embedding to a word, ELMo takes into account the words that surround it in the sentence. This allows it to generate different embeddings for the same word depending on its context.

Understanding ELMo with example

For example, the word “play” can have multiple meanings, such as “to perform” or “a game.” In standard word embeddings, each instance of the word “play” would have the same representation. However, ELMo can distinguish between these different meanings by taking into account the context in which the word appears. In the sentence “The Broadway play premiered yesterday,” for example, ELMo would assign the word “play” an embedding that reflects its meaning as a theater production.

ELMo has been shown to be effective for a variety of natural language processing tasks, including sentiment analysis, question answering, and machine translation. It is a powerful tool that can be used to improve the performance of NLP models.

2. GloVe

GloVe is a statistical method for learning word embeddings from a corpus of text. GloVe is similar to Word2Vec, but it uses a different approach to learning the vector representations of words.

How GloVe works

GloVe works by creating a co-occurrence matrix. The co-occurrence matrix is a table that shows how often two words appear together in a corpus of text. For example, the co-occurrence matrix for the words “cat” and “dog” would show how often the words “cat” and “dog” appear together in a corpus of text.

GloVe then uses a machine learning algorithm to learn the vector representations of words from the co-occurrence matrix. The machine learning algorithm learns to associate words that appear together frequently with similar vector representations.

3. Word2Vec

Word2Vec is a semantic encoding technique that is used to learn vector representations of words. Word vectors represent word meaning and can enhance machine learning models for tasks like text classification, sentiment analysis, and machine translation.

Word2Vec works by training a neural network on a corpus of text. The neural network is trained to predict the surrounding words in a sentence. The network learns to associate words that are semantically similar with similar vector representations.

There are two main variants of Word2Vec:

• Continuous Bag-of-Words (CBOW): The CBOW model predicts the surrounding words in a sentence based on the current word. For example, the model might be trained to predict the words “the” and “dog” given the word “cat”.
• Skip-gram: The skip-gram model predicts the current word based on the surrounding words in a sentence. For example, the model might be trained to predict the word “cat” given the words “the” and “dog”.

Word2Vec has been shown to be effective for a variety of tasks, including:

• Text classification: Word2Vec can be used to train a classifier to classify text into different categories, such as news articles, product reviews, and social media posts.
• Sentiment analysis: Word2Vec can be used to train a classifier to determine the sentiment of text, such as whether it is positive, negative, or neutral.
• Machine translation: Word2Vec can be used to train a machine translation model to translate text from one language to another.

Word2Vec vs Dense word embeddings

Word2Vec is a neural network model that learns to represent words as vectors of numbers. Word2Vec is trained on a large corpus of text, and it learns to predict the surrounding words in a sentence.

Word2Vec can be used to create dense word embeddings. Dense word embeddings are vectors that have a fixed size, regardless of the size of the vocabulary. This makes them easy to use with machine learning models.

Dense word embeddings have been shown to be effective in a variety of NLP tasks, such as text classification, sentiment analysis, and machine translation.

Read more –> Top vector databases in the market – Guide to embeddings and VC pipeline

Will the embeddings of the same text be the same?

Embeddings of the same text generated by a model will typically be the same if the embedding process is deterministic.

This means every time you input the same text into the model, it will produce the same embedding vector.

Most traditional embedding models like Word2Vec, GloVe, or fastText operate deterministically.

However, embeddings might not be the same in the following cases:

1. Random Initialization: Some models might include layers or components that have randomly initialized weights that aren’t set to a fixed value or re-used across sessions. If these weights impact the generation of embeddings, the output could differ each time.
2. Contextual Embeddings: Models like BERT or GPT generate contextual embeddings, meaning that the embedding for the same word or phrase can differ based on its surrounding context. If you input the phrase in different contexts, the embeddings will vary.
3. Non-deterministic Settings: Some neural network configurations or training settings can introduce non-determinism. For example, if dropout (randomly dropping units during training to prevent overfitting) is applied during the embedding generation, it could lead to variations in the embeddings.
4. Model Updates: If the model itself is updated or retrained, even with the same architecture and training data, slight differences in training dynamics (like changes in batch ordering or hardware differences) can lead to different model parameters and thus different embeddings.
5. Floating-Point Precision: Differences in floating-point precision, which can vary based on the hardware (like CPU vs. GPU), can also lead to slight variations in the computed embeddings.

So, while many embedding models are deterministic, several factors can lead to differences in the embeddings of the same text under different conditions or configurations.

Conclusion

Semantic encoding techniques are the most recent approach to embedding words and are the most effective way to capture their semantic meaning. They are able to capture long-distance dependencies between words and they are able to learn the meaning of words even if they have never been seen before.

Safe to say, embeddings are a powerful tool that can be used to improve the performance of machine learning models for a variety of tasks, such as text classification, sentiment analysis, and machine translation. As research in NLP continues to evolve, we can expect to see even more sophisticated embeddings that can capture even more of the nuances of human language.