A Simple Example ChatGPT Used to Explain Transformer (2023)

Paul Xiong



6 min read


1 day ago


Certainly! Let’s consider a simple example to explain how the Transformer architecture works. Suppose we have a sentence: “I love pizza.”

1. Tokenization: The first step is to tokenize the input sentence into individual words or subwords. In this case, the sentence might be tokenized into [“I”, “love”, “pizza”].

2. Embedding: Each token is then converted into a vector representation called an embedding. These embeddings capture the meaning and context of the words. For example, “I” might be represented as [0.2, 0.4, -0.1], “love” as [0.5, 0.9, 0.3], and “pizza” as [0.8, 0.2, 0.6].

3. Positional Encoding: To account for the order of the words in the sentence, positional encoding is added to the embeddings. This helps the model understand the sequential nature of the input.

4. Self-Attention: The self-attention mechanism allows the model to weigh the importance of different words in the sentence when understanding the context. It calculates attention scores between each word and every other word in the sentence. These attention scores represent how much each word should focus on other words.

The attention scores in the self-attention mechanism of the Transformer architecture can be represented as a matrix or a set of matrices, depending on the specific implementation. Let’s consider an example with the sentence “I love pizza” to illustrate how the attention scores might look.

For simplicity, let’s assume we have three words in the sentence: [“I”, “love”, “pizza”]. We’ll denote the embeddings of these words as E, and the attention scores as A.

In the self-attention mechanism, attention scores are calculated by comparing each word with every other word in the sentence. The attention scores determine the relevance or importance of each word with respect to others.

Let’s assume the attention scores are represented as a matrix A with dimensions (3x3) for our example. The entry A(i, j) in the matrix represents the attention score of the i-th word attending to the j-th word.

For our example sentence “I love pizza,” the attention scores matrix A might look like:

A = | A(1,1) A(1,2) A(1,3) |
| A(2,1) A(2,2) A(2,3) |
| A(3,1) A(3,2) A(3,3) |

Each entry in the attention scores matrix represents the attention weight or importance of a word attending to another word. Higher attention scores indicate greater relevance or importance.

In practice, attention scores are often calculated using a softmax function over a compatibility or similarity measure between pairs of words. The softmax function ensures that the attention scores sum to 1 along each row, representing a valid attention distribution.

It’s important to note that the specific values of the attention scores will depend on the input sentence and the learned parameters of the Transformer model. The model learns to assign attention scores based on the context and the relationships between words in the input sequence.

5. Contextual Representation: Using the attention scores, the model generates a weighted sum of the embeddings of all the words in the sentence. This produces a contextually rich representation for each word, capturing its relationship with other words in the sentence.

In step 5 of the Transformer architecture, the contextual representation is obtained by applying the attention scores to the embeddings of the words in the input sentence. This process generates a weighted sum of the embeddings, where the weights are determined by the attention scores. Here’s how the contextual representation might look based on the example sentence “I love pizza”:

Let’s denote the contextual representations as C, the embeddings as E, and the attention scores as A.

  • Compute the attention weights: The attention scores matrix A determines how much each word should attend to other words in the sentence. Using the attention scores, we calculate the attention weights for each word.
  • Calculate the contextual representation: The contextual representation of a word is obtained by taking a weighted sum of the embeddings of all the words in the sentence, where the weights are determined by the attention scores.

For example, let’s assume the embeddings E for the words [“I”, “love”, “pizza”] are as follows:

E(I) = [0.2, 0.4, -0.1]
E(love) = [0.5, 0.9, 0.3]
E(pizza) = [0.8, 0.2, 0.6]

And the attention scores A are as follows:

A = | A(1,1) A(1,2) A(1,3) |
| A(2,1) A(2,2) A(2,3) |
| A(3,1) A(3,2) A(3,3) |

To compute the contextual representation C for each word, we multiply the attention weights by the respective embeddings and sum them up. The contextual representation C(i) for the i-th word can be calculated as:

C(i) = A(i,1) * E(I) + A(i,2) * E(love) + A(i,3) * E(pizza

So, for example, the contextual representation C(1) for the word “I” would be:

C(1) = A(1,1) * E(I) + A(1,2) * E(love) + A(1,3) * E(pizza)

The same process applies to calculate C(2) and C(3) for the words “love” and “pizza” respectively.

The resulting contextual representations capture the contextual information of each word in the input sentence, taking into account the relationships between words as determined by the attention scores. These representations serve as inputs for further layers in the Transformer model, such as the feed-forward network, to make predictions or perform downstream tasks.

6. Feed-Forward Network: The contextual representations of the words are then passed through a feed-forward neural network, which applies non-linear transformations to further refine the representations.

In step 6 of the Transformer architecture, the input to the feed-forward network is the contextual representation obtained from step 5. Let’s denote the input to the feed-forward network as X_in and the output as X_out. Here’s how the input and output might look based on the example sentence “I love pizza”:

Assuming the contextual representations C for the words [“I”, “love”, “pizza”] are as follows:

C(I) = [0.3, 0.1, 0.5]
C(love) = [0.7, 0.2, 0.4]
C(pizza) = [0.4, 0.6, 0.9]

The input X_in to the feed-forward network is the concatenation of these contextual representations:

X_in = [C(I), C(love), C(pizza)] = [0.3, 0.1, 0.5, 0.7, 0.2, 0.4, 0.4, 0.6, 0.9]

The feed-forward network applies non-linear transformations to this input representation to further process and refine the information. It typically consists of multiple layers with intermediate activation functions.

The output X_out of the feed-forward network will depend on the specific architecture and parameters of the model. The output may undergo additional transformations or be used for various downstream tasks, such as sentiment analysis, language translation, or text generation.

It’s important to note that the specific values of the input and output will depend on the learned parameters of the Transformer model and the input sentence. The model learns to transform the input based on the context and the relationships between words, capturing higher-level representations and patterns in the data.

7. Output: Finally, the output of the transformer model can be used for various tasks such as language translation, sentiment analysis, or text generation.

The key idea behind the Transformer architecture is the self-attention mechanism, which allows the model to capture dependencies between words without relying on recurrent or convolutional structures. This enables Transformers to handle long-range dependencies and process input in parallel, leading to improved performance in various natural language processing tasks.

Please note that this example is a simplified explanation of the Transformer architecture, and the actual implementation can involve more complex components and additional layers.

Here’s a text diagram summarizing the total steps of the Transformer architecture explained above, using the example sentence “I love pizza”:

| Input |
| Sentence: "I love pizza" |
| Tokenization |
| Tokens: ["I", "love", "pizza"] |
| Embedding |
| Embeddings: |
| "I": [0.2, 0.4, -0.1] |
| "love": [0.5, 0.9, 0.3] |
| "pizza": [0.8, 0.2, 0.6] |
| Positional Encoding |
| Encoded Embeddings: |
| "I": [0.2, 0.4, -0.1] |
| "love": [0.5, 0.9, 0.3] |
| "pizza": [0.8, 0.2, 0.6] |
| Self-Attention |
| Attention Scores: |
| A(1,1) A(1,2) A(1,3). [0.8, 0.2, 0.7] |
| A(2,1) A(2,2) A(2,3) |
| A(3,1) A(3,2) A(3,3) |
| Contextual Representation |
| Contextual Representations: |
| "I": [C(I)] [0.2, 0.4, -0.1] |
| "love": [C(love)] [0.7, 0.2, 0.4] |
| "pizza": [C(pizza)] [0.4, 0.6, 0.9] |
| Feed-Forward Network |
| Input: [X_in] [0.3, 0.1, 0.5, 0.7, 0.2, 0.4, 0.4, 0.6, 0.9] |
| Output: [X_out] |
| Output |
| Prediction, Translation|
| Sentiment Analysis, etc|


Does ChatGPT give correct answers? ›

No, ChatGPT does not give the exact same answer and wording to everyone who asks the same question. While it may generate similar responses for identical or similar queries, it can also produce different responses based on the specific context, phrasing, and quality of input provided by each user.

What transformer was used in the ChatGPT? ›

ChatGPT is a member of the generative pre-trained transformer (GPT) family of language models. It was fine-tuned over an improved version of OpenAI's GPT-3 known as "GPT-3.5".

How does ChatGPT work in simple terms? ›

ChatGPT is an app built by OpenAI. Using the GPT language models, it can answer your questions, write copy, draft emails, hold a conversation, explain code in different programming languages, translate natural language to code, and more—or at least try to—all based on the natural language prompts you feed it.

How does ChatGPT transformer work? ›

To generate responses, ChatGPT uses a multi-layer transformer network, which is a type of deep learning architecture that has proven to be effective at processing natural language. The model takes an input sentence, processes it using its internal knowledge, and then generates a response that is relevant to the input.

How do you get the best answer on ChatGPT? ›

Assign a Role

One way to get the best results from ChatGPT is to assign it a role. This is a fantastic method of getting more appropriate responses to your prompts. Asking ChatGPT a question will always produce a response of some sort, but its relevance might not be suited to your requirements.

How often does ChatGPT give wrong answers? ›

Very often. It cannot know the correct answer. It just generates text that conforms to what an answer might look like. There was one user who posted two answers contradicting each other.

Which Transformer was a cassette? ›

Soundwave is a fictional character appearing in various Transformers continuity lines within the Transformers franchises. His most well-known disguise that is of a micro cassette recorder.

What Transformer was a Dodge truck? ›

Transformers Alternators Nemesis Prime transforms from a Dodge Ram SRT-10 to robot and back and integrates its weaponry into its transformation adding an all new level of innovation to your Transformers experience!

What was the strongest Transformer? ›

Prior to his defection to the Decepticon ranks, Sentinel Prime remains one of the strongest Autobots out there. Possessing the name of Prime, Sentinel Prime possesses incredible strength, speed, durability, and reflexes. Moreover, having mentored Optimus, Sentinel Prime possesses all the leader's skills.

How do I get started with ChatGPT? ›

How can I access ChatGPT? You can access ChatGPT by going to chat.openai.com and logging in. If you're on OpenAI's website, you can log in to your account, then scroll down until you see ChatGTP on the bottom left corner of the page, and click on it to start chatting.

How many layers are in ChatGPT? ›

The original GPT model had 12 layers, but subsequent versions, such as GPT-2 and GPT-3, have many more layers and let it uses up to 175 billion parameters in GPT-3 that is currently one of the largest neural networks ever created.

Does ChatGPT use the internet? ›

ChatGPT is not connected to the internet, and it can occasionally produce incorrect answers.

How does a transformer work for dummies? ›

The core of the transformer works to direct the path of the magnetic field between the primary and secondary coils to prevent wasted energy. Once the magnetic field reaches the secondary coil, it forces the electrons within it to move, creating an electric current via electromotive force (EMF).

What is the technical detail of ChatGPT? ›

ChatGPT is a large language model based on the Generative Pre-Trained Transformer (GPT) architecture developed by OpenAI. It is a powerful AI system that can process natural language data and generate human-like responses to user input.

How does ChatGPT collect data? ›

So, where does ChatGPT get its data from? Web scraping: ChatGPT uses web scraping to gather data from various sources on the Internet. Web scraping involves extracting data from websites by using automated tools. The chatbot scans the web for relevant information and stores it in its database.

How do you answer the best answer on a question? ›

Answering Tough Questions in the Moment
  1. Listen to the Question. Sounds simple, but with so many things calling for our attention, it's easy to be distracted and not hear what the question really is. ...
  2. Pause. ...
  3. Repeat the Question. ...
  4. Respond Honestly. ...
  5. Know When to Stop.

Can ChatGPT solve multiple choice questions? ›

ChatGPT scored 100% at answering multiple choice questions (MCQ) past exam questions: So far so good.

How do you ask questions in ChatGPT? ›

To ask a question on GPT chat, simply type your prompt into the chat box and hit send. ChatGPT will respond with an answer or a follow-up question.

Where does ChatGPT gets its answers from? ›

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

Is ChatGPT 100 percent correct? ›

While ChatGPT's responses may not be 100% original in every case, it is designed to provide original and informative answers to user queries.

Why does ChatGPT give incorrect answers? ›

Instead, it constructs a sentence word by word, selecting the most likely "token" that should come next based on its training. In other words, ChatGPT arrives at an answer by making a series of guesses, which is part of why it can argue wrong answers as if they were completely true.

Which Transformer killed Ratchet? ›

Despite Ratchet's pleas for mercy team leader James Savoy refused and Lockdown fired on him as the unit crippled him. Ratchet was then interrogated by Lockdown for Optimus' location but refused and Lockdown killed him.

What Transformer looks like a helicopter? ›

Jetfire appears in Transformers: Revenge of the Fallen and transforms into a Lockheed SR-71 Blackbird. He is portrayed as a former Decepticon who becomes an Autobot because he did not believe in all the destruction and violence the Decepticons were dedicated to.

What is the first Transformer called? ›

The first ever Transformer to appear on the series was Wheeljack, episode The Transformers: More Than Meets the Eye: Part 1 (1984).

Which Transformers turn into garbage trucks? ›

Trash Crash is an Autobot drone from the Cyberverse continuity family. The Trash Crash is a garbage truck that, once transformed into mighty Spark Armor, helps clean Decepticon scum off the streets.

What transformer was a black truck? ›

Ironhide (voiced by Jess Harnell in the films, Mark Ryan in Transformers: The Game) is the Autobots' English-accented weapons specialist who transforms into a modified black 2007 GMC TopKick C4500 medium-duty truck.

What transformer was a charger? ›

Charger is a Decepticon Action Master from the Generation 1 continuity family. More like "Charge of the BRIGHT Brigade" am-i-rite!?! Charger's function of "Flamethrower" suits him well.

Who is the oldest Transformer ever? ›

Personality. Alpha Trion is the oldest and wisest living Transformer and the one who rebuilt Orion Pax into Optimus Prime and Ariel to Elita One. Though he was given the Matrix of Leadership by Sentinel Prime, Alpha did not choose to bear it or become the next leader of the Autobots.

What's the weakest Transformer? ›

The 15 Most Useless Transformers
Feb 21, 2017

What is the rarest Transformer? ›

The Lucky Draw series has possibly the rarest toys of any Transformers line. Given away in magazine promotions, special DVD sets, and conventions, only a handful of each model was ever made. Case in point, Convoy, or “Crayola Convoy” as fans have dubbed it.

Will ChatGPT replace programmers? ›

The short answer is no, ChatGPT will not replace programmers entirely. However, it has the potential to automate some aspects of programming, such as code generation, bug fixing, and documentation. ChatGPT can learn from vast amounts of code and data, making it possible to generate new code similar to existing code.

Does ChatGPT have a word limit? ›

ChatGPT is a completely mind-blowing tool. What makes it particularly great is that the AI bot can remember information from previous conversations. But, this does have a limit! According to OpenAI, Chat GPT has a maximum text length of 3000 words.

What is GBT Chat? ›

Chat gbt is an Ai tool that can answer any question for you in a matter of seconds using Ai technology. This tool is run by openai What is open ai how to use openai.

Does ChatGPT train on user input? ›

The answer to that is, yes, ChatGPT learns from user input—but not in the way that most people think. Here's an in-depth guide explaining why ChatGPT tracks conversations, how it uses them, and whether your security is compromised.

How much will ChatGPT cost? ›

ChatGPT Plus was announced at the end of January 2023 and in relation to ChatGPT Pro would be one of the lower-cost plans. You can get access to this paid version of ChatGPT for a monthly subscription of $20.

How many dimensions does ChatGPT have? ›

Number of attention heads: 96. Dimensions of its hidden layers: 12288. Sequence length: 2048. Number of parameters: 175B.

Can I use ChatGPT without a phone number? ›

In some countries, you can verify your account using WhatsApp instead of a phone number. You can use ChatGPT without signing up by using Bing Chat, Snapchat, or Discord. Use a ChatGPT alternative, like Bard or Perplexity, with no phone verification.

Can ChatGPT write code for a website? ›

ChatGPT website development is possible to some extent. The AI model can help users write lines of code to form web pages, give design suggestions, and create web content.

Does ChatGPT have an API? ›

Yes, Chat GPT 3 has an API that can be accessed by developers who have an API key from Open AI. The API allows developers to generate human-like responses to text input, making it ideal for building conversational AI applications.

What is the transformer simple answer? ›

What is a transformer? A transformer is a device that transfers electric energy from one alternating-current circuit to one or more other circuits, either increasing (stepping up) or reducing (stepping down) the voltage.

What is transformer in simple words? ›

A transformer is an inductive electrical device for changing the voltage of alternating current.

Can ChatGPT replace Google? ›

In conclusion, while ChatGPT is a powerful language model with the ability to generate human-like responses, it is not yet capable of replacing Google.

Why is ChatGPT so popular? ›

ChatGPT's popularity can be attributed to its versatility, intelligence, and ability to engage in human-like conversations. It is an exciting example of how artificial intelligence is advancing and becoming more accessible to the general public.

What kind of data does ChatGPT use? ›

The model was trained using text databases from the internet. This included a whopping 570GB of data obtained from books, webtexts, Wikipedia, articles and other pieces of writing on the internet.

Does ChatGPT track your browsing history? ›

Your Device Information

ChatGPT also retrieves your device's name and operating system. OpenAI uses cookies to track your browsing activity both in the chat window and on its site. It claims to use this information for analytics and to find out exactly how you interact with ChatGPT.

What are the capabilities of ChatGPT? ›

There are three key capabilities of ChatGPT:
  • The ability to remember and reference what was said earlier in a chat session.
  • Allow the use of follow up prompts to further define responses.
  • It is trained to decline inappropriate responses.

Is ChatGPT a reliable source of information? ›

Is ChatGPT a credible source? No, ChatGPT is not a credible source of factual information and can't be cited for this purpose in academic writing. While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Why is ChatGPT not always accurate? ›

ChatGPT's training data may have underlying biases that could affect its predictions. The accuracy and reliability of ChatGPT's predictions need careful evaluation given recent reports that it has repeated disinformation. No single model or algorithm can predict financial market movements with complete accuracy.

What can I use ChatGPT for? ›

ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with the chatbot. The language model can answer questions and assist you with tasks, such as composing emails, essays, and code.

What is the controversy with ChatGPT? ›

Meanwhile, OpenAI's flagship product, ChatGPT, is facing controversy in multiple countries over data concerns and claims the AI model has released false information on real individuals.

Can universities detect ChatGPT? ›

As it currently stands, your university may be able to detect ChatGPT. Despite the model being relatively new, some AI detection software has already caught up. GPTZero is probably the most common detection tool around and boasts impressive accuracy.

What are the disadvantages of ChatGPT? ›

The Disadvantages of ChatGPT. One of the biggest cons of ChatGPT or any other AI chatbot is that it cannot be used as an authoritative source of information. At the time of writing (April 2023), the technology still relies on content from the internet, as in 2021.

Does ChatGPT get caught by Turnitin? ›

Yes, Turnitin software can detect ChatGPT writing.

It provides a Similarity index and originality report to support its identification. Educators and lecturers can use this AI-writing detector to check students' work.

Can we use ChatGPT for free? ›

Is ChatGPT free? Yes, you can use ChatGPT for free -- for now.

What is the accuracy rate of ChatGPT? ›

We found that ChatGPT performed at or near the passing threshold of 60% accuracy. Being the first to achieve this benchmark, this marks a notable milestone in AI maturation. Impressively, ChatGPT was able to achieve this result without specialized input from human trainers.

Is ChatGPT not an AI? ›

Meta chief AI scientist Yann LeCun has said that OpenAI's ChatGPT represents a product, not actual research and development in the field of artificial intelligence (AI). According to him, OpenAI has not made any actual scientific breakthroughs and ChatGPT is 'not particularly innovative'.

Is ChatGPT better than Google? ›

ChatGPT is insanely good at generating human-like responses to queries and solving problems through textual conversations. On the other hand, Google is really good at finding information, videos, images, products, and almost anything on the internet.

Where can I get all answers? ›

  • Answers.com. User-powered question and answer platform. ...
  • Ask a Librarian. Online reference desk service from the Library of Congress. ...
  • Brainly. Post questions to a community of millions of students and teachers. ...
  • Chegg Study. ...
  • Dummies. ...
  • eHow. ...
  • PolitiFact. ...
  • Quora.


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