embeddings

Large language models (LLMs) are AI models that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. They are trained on massive amounts of text data, and they can learn to understand the nuances of human language.

In this blog, we will take a deep dive into LLMs, including their building blocks, such as embeddings, transformers, and attention. We will also discuss the different applications of LLMs, such as machine translation, question answering, and creative writing.

To test your knowledge, we have included a crossword or quiz at the end of the blog. So, what are you waiting for? Let’s crack the code of large language models!

 

Read more –>  40-hour LLM application roadmap

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.

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.

20 essential terms for crafting LLM-powered applications

 

1. Large language model (LLM)

Large language models (LLMs) are AI models that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. The building blocks of an LLM are embeddings, transformers, attention, and loss functions. Embeddings are vectors that represent the meaning of words or phrases. Transformers are a type of neural network that are well-suited for NLP tasks. Attention is a mechanism that allows the LLM to focus on specific parts of the input text. The loss function is used to measure the error between the LLM’s output and the desired output. The LLM is trained to minimize the loss function.

2. OpenAI

OpenAI is a non-profit research company that develops and deploys artificial general intelligence (AGI) in a safe and beneficial way. AGI is a type of artificial intelligence that can understand and reason like a human being. OpenAI has developed a number of LLMs, including GPT-3, Jurassic-1 Jumbo, and DALL-E 2.

GPT-3 is a large language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Jurassic-1 Jumbo is a larger language model that is still under development. It is designed to be more powerful and versatile than GPT-3. DALL-E 2 is a generative AI model that can create realistic images from text descriptions.

3. Generative AI

Generative AI is a type of AI that can create new content, such as text, images, or music. LLMs are a type of generative AI. They are trained on large datasets of text and code, which allows them to learn the patterns of human language. This allows them to generate text that is both coherent and grammatically correct.

Generative AI has a wide range of potential applications. It can be used to create new forms of art and entertainment, to develop new educational tools, and to improve the efficiency of businesses. It is still a relatively new field, but it is rapidly evolving.

4. ChatGPT

ChatGPT is a large language model (LLM) developed by OpenAI. It is designed to be used in chatbots. ChatGPT is trained on a massive dataset of text and code, which allows it to learn the patterns of human conversation. This allows it to hold conversations that are both natural and engaging. ChatGPT is also capable of answering questions, providing summaries of factual topics, and generating different creative text formats.

5. Bard

Bard is a large language model (LLM) developed by Google AI. It is still under development, but it has been shown to be capable of generating text, translating languages, and writing different kinds of creative content. Bard is trained on a massive dataset of text and code, which allows it to learn the patterns of human language. This allows it to generate text that is both coherent and grammatically correct. Bard is also capable of answering your questions in an informative way, even if they are open ended, challenging, or strange.

6. Foundation models

Foundation models are a family of large language models (LLMs) developed by Google AI. They are designed to be used as a starting point for developing other AI models. Foundation models are trained on massive datasets of text and code, which allows them to learn the patterns of human language. This allows them to be used to develop a wide range of AI applications, such as chatbots, machine translation, and question-answering systems.

7. LangChain

LangChain is a text-to-image diffusion model that can be used to generate images from text descriptions. It is based on the Transformer model and is trained on a massive dataset of text and images. LangChain is still under development, but it has the potential to be a powerful tool for creative expression and problem-solving.

8. Llama Index

Llama Index is a data framework for large language models (LLMs). It provides tools to ingest, structure, and access private or domain-specific data. LlamaIndex can be used to connect LLMs to a variety of data sources, including APIs, PDFs, documents, and SQL databases. It also provides tools to index and query data, so that LLMs can easily access the information they need.

Llama Index is a relatively new project, but it has already been used to build a number of interesting applications. For example, it has been used to create a chatbot that can answer questions about the stock market, and a system that can generate creative text formats, like poems, code, scripts, musical pieces, email, and letters.

9. Redis

Redis is an in-memory data store that can be used to store and retrieve data quickly. It is often used as a cache for web applications, but it can also be used for other purposes, such as storing embeddings. Redis is a popular choice for NLP applications because it is fast and scalable.

10. Streamlit

Streamlit is a framework for creating interactive web apps. It is easy to use and does not require any knowledge of web development. Streamlit is a popular choice for NLP applications because it allows you to quickly and easily build web apps that can be used to visualize and explore data.

11. Cohere

Cohere is a large language model (LLM) developed by Google AI. It is known for its ability to generate human-quality text. Cohere is trained on a massive dataset of text and code, which allows it to learn the patterns of human language. This allows it to generate text that is both coherent and grammatically correct. Cohere is also capable of translating languages, writing different kinds of creative content, and answering your questions in an informative way.

12. Hugging Face

Hugging Face is a company that develops tools and resources for NLP. It offers a number of popular open-source libraries, including Transformer models and datasets. Hugging Face also hosts a number of online communities where NLP practitioners can collaborate and share ideas.

 

 

13. Midjourney

Midjourney is a LLM developed by Midjourney. It is a text-to-image AI platform that uses a large language model (LLM) to generate images from natural language descriptions. The user provides a prompt to Midjourney, and the platform generates an image that matches the prompt. Midjourney is still under development, but it has the potential to be a powerful tool for creative expression and problem-solving.

14. Prompt Engineering

Prompt engineering is the process of crafting prompts that are used to generate text with LLMs. The prompt is a piece of text that provides the LLM with information about what kind of text to generate.

Prompt engineering is important because it can help to improve the performance of LLMs. By providing the LLM with a well-crafted prompt, you can help the model to generate more accurate and creative text. Prompt engineering can also be used to control the output of the LLM. For example, you can use prompt engineering to generate text that is similar to a particular style of writing, or to generate text that is relevant to a particular topic.

When crafting prompts for LLMs, it is important to be specific, use keywords, provide examples, and be patient. Being specific helps the LLM to generate the desired output, but being too specific can limit creativity.

Using keywords helps the LLM focus on the right topic, and providing examples helps the LLM learn what you are looking for. It may take some trial and error to find the right prompt, so don’t give up if you don’t get the desired output the first time.

Read more –> How to become a prompt engineer?

15. Embeddings

Embeddings are a type of vector representation of words or phrases. They are used to represent the meaning of words in a way that can be understood by computers. LLMs use embeddings to learn the relationships between words. Embeddings are important because they can help LLMs to better understand the meaning of words and phrases, which can lead to more accurate and creative text generation. Embeddings can also be used to improve the performance of other NLP tasks, such as natural language understanding and machine translation.

Read more –> Embeddings: The foundation of large language models

16. Fine-tuning

Fine-tuning is the process of adjusting the parameters of a large language model (LLM) to improve its performance on a specific task. Fine-tuning is typically done by feeding the LLM a dataset of text that is relevant to the task.

For example, if you want to fine-tune an LLM to generate text about cats, you would feed the LLM a dataset of text that contains information about cats. The LLM will then learn to generate text that is more relevant to the task of generating text about cats.

Fine-tuning can be a very effective way to improve the performance of an LLM on a specific task. However, it can also be a time-consuming and computationally expensive process.

17. Vector databases

Vector databases are a type of database that is optimized for storing and querying vector data. Vector data is data that is represented as a vector of numbers. For example, an embedding is a vector that represents the meaning of a word or phrase.

Vector databases are often used to store embeddings because they can efficiently store and retrieve large amounts of vector data. This makes them well-suited for tasks such as natural language processing (NLP), where embeddings are often used to represent words and phrases.

Vector databases can be used to improve the performance of fine-tuning by providing a way to store and retrieve large datasets of text that are relevant to the task. This can help to speed up the fine-tuning process and improve the accuracy of the results.

18. Natural Language Processing (NLP)

Natural Language Processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP tasks include text analysis, machine translation, and question answering. LLMs are a powerful tool for NLP. NLP is a complex field that covers a wide range of tasks. Some of the most common NLP tasks include:

• Text analysis: This involves extracting information from text, such as the sentiment of a piece of text or the entities that are mentioned in the text.
  • For example, an NLP model could be used to determine whether a piece of text is positive or negative, or to identify the people, places, and things that are mentioned in the text.
• Machine translation: This involves translating text from one language to another.
  • For example, an NLP model could be used to translate a news article from English to Spanish.
• Question answering: This involves answering questions about text.
  • For example, an NLP model could be used to answer questions about the plot of a movie or the meaning of a word.
• Speech recognition: This involves converting speech into text.
  • For example, an NLP model could be used to transcribe a voicemail message.
• Text generation: This involves generating text, such as news articles or poems.
  • For example, an NLP model could be used to generate a creative poem or a news article about a current event.

19. Tokenization

Tokenization is the process of breaking down a piece of text into smaller units, such as words or subwords. Tokenization is a necessary step before LLMs can be used to process text. When text is tokenized, each word or subword is assigned a unique identifier. This allows the LLM to track the relationships between words and phrases.

There are many different ways to tokenize text. The most common way is to use word boundaries. This means that each word is a token. However, some LLMs can also handle subwords, which are smaller units of text that can be combined to form words.

For example, the word “cat” could be tokenized as two subwords: “c” and “at”. This would allow the LLM to better understand the relationships between words, such as the fact that “cat” is related to “dog” and “mouse”.

20. Transformer models

Transformer models are a type of neural network that are well-suited for NLP tasks. They are able to learn long-range dependencies between words, which is essential for understanding the nuances of human language. Transformer models work by first creating a representation of each word in the text. This representation is then used to calculate the relationship between each word and the other words in the text.

The Transformer model is a powerful tool for NLP because it can learn the complex relationships between words and phrases. This allows it to perform NLP tasks with a high degree of accuracy. For example, a Transformer model could be used to translate a sentence from English to Spanish while preserving the meaning of the sentence.

 

Read more –> Transformer Models: The future of Natural Language Processing

 

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

Type of embedding	Description	Use-cases
Word embeddings	Represent individual words as vectors of numbers.	Text classification, text summarization, question answering, machine translation
Sentence embeddings	Represent entire sentences as vectors of numbers.	Text classification, text summarization, question answering, machine translation
Bag-of-words (BoW) embeddings	Represent text as a bag of words, where each word is assigned a unique ID.	Text classification, text summarization
TF-IDF embeddings	Represent text as a bag of words, where each word is assigned a weight based on its frequency and inverse document frequency.	Text classification, text summarization
GloVe embeddings	Learn word embeddings from a corpus of text by using global co-occurrence statistics.	Text classification, text summarization, question answering, machine translation
Word2Vec embeddings	Learn word embeddings from a corpus of text by predicting the surrounding words in a sentence.	Text classification, text summarization, question answering, machine translation

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

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.