Unpacking the Query, Key, and Value of Transformers: An Analogy to Database Operations

Main Points

• Introduction
• Understanding the Query, Key, and Value Concept in Transformers
• Intuitive NLP example
• An Analogy to Database Operations
• Applications of the Query, Key, and Value Concept in Transformers

Introduction

Transformers have become one of the most influential models in the field of natural language processing (NLP) in recent years.

Generative AI, also known as artificial creativity, has been revolutionized by the use of transformers. GPT (Generative Pre-trained Transformer) is a prime example of this, as it can generate human-like text and has been used in chatbots and other conversational AI applications. The ability of transformers to understand context and dependencies between words makes them highly effective in generating coherent and meaningful text. The query, key, and value concept plays a crucial role in this, as it allows the model to focus on the most important parts of the input and generate output that is relevant and coherent.

Their ability to assign weights to each word in a sentence based on its importance called attention, has revolutionized the field. However, the potential of transformers goes beyond NLP. In this article, we will explore how the query, key, and value concept in transformers can be thought of as similar to database operations and the range of applications beyond NLP.

Understanding the Query, Key, and Value Concept in Transformers

To understand the query, key, and value concept in transformers, let's first understand how attention works in these models. Attention is a mechanism that assigns weights to each word in a sentence based on its importance. The weighted sum of these words is then used to compute the output of the model. However, attention is not just a simple sum of the words. It takes into account the context and dependencies between the words. The query, key, and value concept is used to perform this operation.

In transformers, the query is the information that is being looked for, the key is the context or reference, and the value is the content that is being searched. The query and the key are multiplied together to produce the attention scores, which are then used to compute the weighted sum of the values. This weighted sum is then used to compute the output of the model.

Intuitive NLP example

Consider the sentence "The dog chased the cat across the street", and We’re trying to Translate this Sentence to any other language.

In a transformer model, When The query is the word "Dog" that might mean I’m looking for verbs, adjectives related to me. (What am I looking for in other words?)

The key in this case is every word in the sentence, and every word is maybe putting out: I’m a noun, an adjective or a verb (What am I? What features do I posses in relation to the sentence?)

The value of each word in the sentence, is the meaning of this word in general not specifically for this sentence (What’re my embeddings? What’s the semantic information I posses?)

Let’s try to do the Self-Attention Operation in this case but only when we’re trying to Translate the word Dog:

1. Dog (Query Vector) will be multiplied with all other words (Keys) to get an Attention Map
2. The Attention Map will represent the importance of every other word related to Dog
3. Chased and Cat will be a very important word related to the Dog when we’re translating
4. across the street won’t be a very important word in this case
5. This attention map will be multiplied by the Embeddings of the Sentence words (Values), and produce a weighted sum of the embeddings based on the relevancy of the words

An Analogy to Database Operations

The query, key, and value concepts in transformers can be thought of as similar to database operations. In a database, the query represents the search term, the key represents the column or field being searched, and the value represents the content being searched for. The similarity between the two concepts is that both operations involve searching for specific information based on certain criteria.

For example, when you search for videos on Youtube, the search engine will map your?query?(text in the search bar) against a set of?keys?(video title, description, etc.) associated with candidate videos in their database, then present you the best-matched videos (values).

As mentioned in the paper (Neural Machine Translation by Jointly Learning to Align and Translate), attention by definition is just a weighted average of values,

C = sum(??? * h)

When:

?Sum(??) = 1

This means that??? is a one-hot encoded vector, with only one value is equal to one.

?? = [0, 0, 0, 0, 1, 0]

This operation becomes the same as retrieving from a set of elements?h?with index???i.

With the restriction removed, the attention operation can be thought of as doing "proportional retrieval" according to the probability vector???.

It should be clear that?h?in this context is the?value.

Benefits of Using the Query, Key, and Value Concept in Transformers

Using the query, key, and value concept in transformers has several benefits, including:

1. Capturing Context and Dependencies: The use of the query, key, and value concept allows transformers to capture context and dependencies between words. This makes them highly effective in a wide range of applications, from NLP to image recognition.
2. Improving Accuracy: The use of attention in transformers allows them to focus on the most important parts of the input, which improves their accuracy.
3. Scaling to Large Datasets: The query, key, and value concept can be used to scale transformers to large datasets, making them ideal for applications that require processing large amounts of data.

Applications of the Query, Key, and Value Concept beyond NLP

The query, key, and value concepts in transformers can be used in various applications beyond NLP. Some of these applications include:

1. Recommendation Systems
2. Image Recognition
3. Time-Series Analysis

Let’s take the Image Recognition part as an example:

Image recognition involves identifying specific features or objects in an image. The query, key, and value concepts can be used in image recognition to identify specific features or objects in an image. In traditional image recognition systems, the image is represented as a matrix of pixels, and the model looks for specific patterns or shapes in this matrix to identify the object.

Attention mechanisms can help in image recognition by allowing the model to selectively focus on specific parts of the image, such as the object of interest while ignoring irrelevant background noise also by using attention the model is able to break free from the locality assumption of ConvNets and able to relate objects to each other in various parts of the image. This can help improve the accuracy of image recognition models by ensuring that the model focuses on the most important features.

The use of attention mechanisms in Google’s ViT model for example has been shown to improve the accuracy of image recognition models, particularly for large-scale image datasets.