Embedding

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Types:


AI Encoding & AI Embedding

The terms "AI encodings" and "AI embeddings" are sometimes used interchangeably, but there is a subtle difference between the two.

  • Encodings are a general term for any representation of data that is used by a Machine Learning (ML) model. This could be a one-hot encoding, a bag-of-words representation, or a more complex representation such as a word embedding.
  • Embeddings are a specific type of AI encoding that is learned from data. Embeddings are typically represented as vectors of real numbers, and they capture the meaning and context of the data they represent.


In other words, all embeddings are encodings, but not all encodings are embeddings. Here are some examples of AI encodings that are not embeddings:

  • One-hot Encoding is a simple way to represent categorical data as a vector. For example, the word "dog" would be represented as a vector of 100 zeros, with a single 1 at the index corresponding to the word "dog" in a vocabulary of 100 words.
  • Bag-of-words is a more sophisticated way to represent text data as a vector. This involves counting the number of times each word appears in a document, and then representing the document as a vector of these counts.


AI Embeddings are a type of representation of text that captures the meaning of the text. This can be used for tasks such as search, classification, and recommendation. allow the model to search in a “database” and return the best result. Here are some examples of AI Embeddings:

  • Word embeddings are a type of embedding that represents words as vectors of real numbers. These vectors are typically learned from a large corpus of text, and they capture the meaning and context of the words they represent.
  • Image embeddings are a type of embedding that represents images as vectors of real numbers. These vectors are typically learned from a large dataset of images, and they capture the visual features of the images they represent.

Embedding...

  • projecting an input into another more convenient representation space. For example we can project (embed) faces into a space in which face matching can be more reliable. | Chomba Bupe
  • a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Neural Network embeddings are useful because they can reduce the dimensionality of categorical variables and meaningfully represent categories in the transformed space. Neural Network Embeddings Explained | Will Koehrsen - Towards Data Science
  • a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do Machine Learning (ML) on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models. Embeddings | Machine Learning Crash Course
  • Search: Embeddings can be used to rank search results by relevance to a query string.
  • Clustering: Embeddings can be used to group text strings by similarity.
  • Recommendations: Embeddings can be used to recommend items that are related to a user's interests.
  • Anomaly detection: Embeddings can be used to identify outliers with little relatedness.
  • Diversity measurement: Embeddings can be used to analyze similarity distributions.
  • Classification: Embeddings can be used to classify text strings by their most similar label.


By employing techniques like Word Embeddings, Sentence Embeddings, or Contextual embedding, vector embeddings provide a compact and meaningful representation of textual data. Word embeddings, for instance, map words to fixed-length vectors, where words with similar meanings are positioned closer to one another in the vector space. This allows for efficient semantic search, information retrieval, and language understanding tasks.


Embeddings have 3 primary purposes:

  1. Finding nearest neighbors in the embedding space. These can be used to make recommendations based on user interests or cluster categories.
  2. As input to a Machine Learning (ML) model for a supervised task.
  3. For visualization of concepts and relations between categories.

Fine-tuning

Fine-tuning

A process of retraining a language model on a new dataset of data. This can be used to improve the model's performance on a specific task, such as generating text, translating languages, or answering questions. Fine-tuning is a way to add new knowledge to an existing AI model. It’s a simple upgrade that allows the model to learn new information.

Here are some more detailed information on fine-tuning:

  • Fine-tuning is a relatively simple process. The first step is to select a pre-trained language model. There are many pre-trained language models available, such as GPT-3, RoBERTa, and XLNet. Once you have selected a pre-trained language model, you need to gather a dataset of data for fine-tuning. This dataset should be relevant to the task that you want the model to perform. For example, if you want to fine-tune a language model for question answering, you would need to gather a dataset of questions and answers.
  • The next step is to fine-tune the language model on the dataset of data. This is done by using a technique called supervised learning. In supervised learning, the model is given a set of labeled examples. In the case of fine-tuning, the labels are the answers to the questions in the dataset. The model is then trained to predict the labels for the unlabeled examples in the dataset.
  • Fine-tuning can be a time-consuming process, but it can significantly improve the performance of a language model on a specific task. For example, fine-tuning a language model on a dataset of question and answers can improve the model's ability to answer new questions.


Here are some examples of fine-tuning:

  • Fine-tuning OpenAI's base models such as Davinc, Curie, Babbage, and Ada to improve their performance on a variety of tasks, such as generating text, translating languages, and answering questions.
  • Fine-tuning a binary classifier to rate each completion for truthfulness based on expert-labeled examples.
  • Incorporating proprietary content into a language model to improve its ability to provide relevant answers to questions.

Fine-tuning is a powerful technique that can be used to improve the performance of language models on a variety of tasks. If you are looking to improve the performance of a language model on a specific task, fine-tuning is a good option to consider.

Prompting vs AI Model Fine-Tuning vs AI Embeddings

Prompting vs AI Model Fine-Tuning vs AI Embeddings

Beyond simple prompt engineering, there are two design approaches to consider: building an embedding database of all proprietary content and dynamically searching for relevant information at runtime, or sending the content to the AI provider to fine-tune the model.

Feature AI Model Fine-tuning AI Embeddings
Purpose Improve the performance of a language model on a specific task Capture the meaning of text
Process Retrain the language model on a new dataset of data Calculate a numerical representation of the text
Applications Text generation, translation, question answering Search, classification, recommendation
Advantages Can improve the performance of a language model significantly Efficient and easy to use
Disadvantages Can be time-consuming and expensive May not be as accurate as fine-tuning


The best technique to use will depend on the specific task you want to perform and the resources you have available


  • Prompting: is the simplest technique. It involves providing the LLM with a text prompt that describes the task you want it to perform. The LLM then generates text that is consistent with the prompt. This is a very efficient technique, as it does not require any retraining of the LLM. However, it can be less accurate than fine-tuning or embeddings, as the LLM may not be able to understand the prompt perfectly.
  • Fine-tuning: is a more powerful technique than prompting. It involves retraining the LLM on a dataset of examples for the specific task you want it to perform. This can improve the accuracy of the LLM, but it also requires more training data and compute resources.
  • Embeddings: are a middle ground between prompting and fine-tuning. They involve using a small model to learn a representation of the input text. This representation is then used to initialize the LLM, which can then be fine-tuned on a dataset of examples for the specific task you want it to perform. This can improve the accuracy of the LLM over prompting, without requiring as much training data or compute resources as fine-tuning.


Technique Performance Efficiency Flexibility
Prompting Low High High
Fine-tuning High Low Low
Embeddings Medium Medium Medium


OpenAI Note

Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text. Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of the cost. An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

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