GPT is not just "predictive text"

[Bootstrap]

GPT may have gone beyond predictive text, but its mechanized output can still usually be distinguished (like this article, which I'm pretty sure is AI content) from chef-assembled word salads.

Actually, GPT was always more than just predictive text. Let me interview GPT to explain ... and I'll try to debunk a few common misconceptions in the process.

Summarize the article "attention is all you need", about GPT

The paper "Attention is All You Need", written by Vaswani et al. in 2017, introduced the Transformer model, a groundbreaking architecture that replaced the traditional recurrent and convolutional models for natural language processing tasks. The key innovation in the Transformer is the use of self-attention mechanisms instead of recurrence to process sequences.

Key Concepts:

1. Self-Attention: The model assigns varying levels of attention to different words in a sentence, allowing it to better understand relationships between distant words. This helps capture context more effectively, especially in long sentences.

2. Positional Encoding: Since the Transformer doesn’t process data sequentially like recurrent models, it uses positional encodings to maintain the order of words in a sequence, enabling the model to understand the structure of the sentence.

3. Parallelization: Unlike recurrent models that process data step by step, the Transformer processes all tokens in a sentence simultaneously, which makes it much faster to train.

4. Layers: The model consists of encoder and decoder layers. The encoder reads the input and creates a representation, while the decoder generates the output based on the encoded input and previously generated outputs.

5. Applications: This model significantly improved performance on various tasks, including machine translation and text generation, and laid the groundwork for models like GPT (Generative Pretrained Transformer).

Impact on GPT:

The GPT architecture builds on the principles of the Transformer, specifically focusing on the decoder part to generate coherent and contextually relevant text. By leveraging self-attention mechanisms and large-scale pretraining, GPT can generate high-quality text based on a given prompt.

In summary, "Attention is All You Need" revolutionized natural language processing by introducing the Transformer, which became the foundation for powerful language models like GPT. The use of attention mechanisms allows the model to handle long-range dependencies more effectively and with greater computational efficiency than previous models.

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Continuing the interview ...

So GPT is more than just guessing what the next word in a sequence would be? How is it different from that?

Yes, GPT (Generative Pretrained Transformer) is more than just "guessing the next word" in a sequence. While predicting the next word is a fundamental part of how it generates text, GPT does this with a deeper understanding of context, relationships between words, and broader knowledge, which makes it far more sophisticated than simple word prediction. Here’s how GPT goes beyond basic next-word guessing:

Key Differences and Enhancements:

1. Contextual Understanding:
- GPT uses self-attention mechanisms (introduced in the "Attention is All You Need" paper) to consider not just the immediate previous word, but all the words in a sentence or paragraph. It can focus on relevant parts of the input text, allowing it to capture meaning, structure, and nuance in ways a simple word-guessing model couldn’t.
- This helps GPT understand long-range dependencies between words, such as keeping track of who is doing what in a complex sentence.

2. Training on Large Corpora:
- GPT is pretrained on massive datasets containing text from various domains (books, websites, etc.). Through this training, GPT learns patterns, factual knowledge, and common reasoning structures. This allows GPT to generate coherent and informative responses that reflect its exposure to the content during training, beyond just predicting the next word.

3. Inference Beyond Local Context:
- Instead of only looking at a few recent words (like some older models), GPT’s attention mechanism lets it weigh every word in a passage simultaneously. This means GPT can generate the next word based on a full understanding of the entire sentence or even paragraphs before it. This ensures that it keeps meaning consistent and answers more precisely.

4. Probabilistic Generation:
- GPT doesn’t just choose the "most likely" next word but instead assigns probabilities to a range of possible next words based on context. It then selects a word from that distribution. This gives GPT flexibility and creativity in responses, which can make its output more varied and human-like rather than strictly deterministic.

5. Pretraining and Fine-tuning:
- GPT undergoes a two-step process: pretraining on a vast dataset (to learn general language and knowledge) and fine-tuning on specific tasks or prompts to adapt to particular needs (e.g., answering questions, generating creative writing). This combination allows GPT to perform more complex tasks than just word prediction.

6. Handling of Ambiguity:
- Instead of merely selecting the next word based on a narrow set of previous words, GPT is capable of interpreting ambiguities and nuances in text. For example, if a sentence can have multiple interpretations, GPT can navigate through these interpretations based on the broader context of the conversation or prompt.

Example of GPT in Action:
When GPT generates text, it doesn’t just predict that "dog" should follow "The quick brown..." It considers the entire sentence or conversation before determining that the next word could be "fox" (if it understands you’re referring to the classic phrase), "dog" (if it’s a pet-related discussion), or something entirely different, depending on the context. This is a major leap beyond simple word prediction.

Conclusion:
In summary, while next-word prediction is the core mechanism of GPT, it is enriched by deep context analysis, a large knowledge base, and attention to all words in a given sequence. This allows GPT to generate coherent, contextually relevant, and sometimes creative text that feels more like genuine human conversation or writing rather than a mechanical next-word guesser.

[Soloist]

Waste of time. These write ups have bias included in them.

This was in response to asking how it uses predictive text…

ChatGPT uses predictive text through a combination of machine learning techniques and vast amounts of training data. It generates responses based on patterns and context learned from a diverse range of texts. When you type something, it predicts the most likely continuation of the conversation by analyzing the context and patterns in the input. Unlike simple predictive text systems that suggest individual words, ChatGPT generates entire sentences or responses that are contextually relevant to the conversation.

Effectively though it’s a waste of time to use it to buttress your points. Your personal bias comes through when you use it and it’s not neutral in the slightest.
I don’t think any of us want to argue with a robot or discuss or debate with a robot.

Ask questions in a leading fashion and you will get answers to support what you ask.

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Continuing the interview ...

Does GPT "look things up"?

No, GPT does not "look things up" in real-time. When GPT generates responses, it relies on patterns, information, and knowledge it learned during its pretraining phase, which involved analyzing a vast amount of text data. This means GPT doesn't have access to the internet, databases, or any external sources after it has been trained. It doesn't search for or retrieve new information in real time like a search engine would.

How GPT Works Instead:
1. Pretrained Knowledge:
- GPT was trained on a broad range of text, including books, websites, articles, and more, which gave it a large amount of general knowledge.
- However, this knowledge is static and limited to the data it was trained on. Anything that happened after GPT’s training cutoff (for example, after 2021 for most models) or any specific, real-time information isn't accessible to the model.

2. Generating Responses:
- When you ask GPT a question or prompt it for text, it generates a response based on the patterns it learned during training. It doesn’t retrieve information from a database but uses its understanding of language to predict the most likely next words based on the context you’ve provided.

3. No External Data Access:
- GPT doesn't connect to the internet, so it can't look up recent news, current events, or any specific real-time data. It can only generate responses based on its internalized knowledge from the training data.

Example:
If you ask GPT, "Who won the 2023 World Cup?", it can't actually look up the answer. Instead, it might generate a response like, "As of my last update in 2021, I don't have information about the 2023 World Cup," because it has no access to post-training information.

Conclusion:
GPT generates answers based on patterns and data learned during its training but cannot look things up or access live information. Its responses are a result of its trained understanding of language and concepts rather than real-time searches or information retrieval.

[Soloist]

I would suggest if you use chat GTP to support your arguments you state what question and exact wording you used to produce your results so your pre-bias is visible.

[Bootstrap]

Waste of time. These write ups have bias included in them.

Actually, I think I am asking questions that expose the nuts and bolts of how this works. Like telling people an engine has spark plugs and coils, but for GPT. I suspect most people here can not read the paper "Attention Is All You Need" for themselves, it's quite technical. But for anyone who can read it, this interview is basically summarizing the paper that introduced GPT. Here it is:

https://arxiv.org/pdf/1706.03762

And this is largely a response to a Plough article someone quoted earlier. That article got some basics badly wrong.

I do think GPT is a bit biased when it talks about the quality of its answers, but it is quite accurate about how it works.

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I would suggest if you use chat GTP to support your arguments you state what question and exact wording you used to produce your results so your pre-bias is visible.

That's exactly what I am doing in each post. In the quotes that precede each GPT response. For instance, in the first post in the thread, this was my input to GPT:

Summarize the article "attention is all you need", about GPT

You can tell that it's my input because it is in the quote tag. The part that follows is the GPT response. Here's how GPT began its answer:

The paper "Attention is All You Need", written by Vaswani et al. in 2017, introduced the Transformer model, a groundbreaking architecture that replaced the traditional recurrent and convolutional models for natural language processing tasks. The key innovation in the Transformer is the use of self-attention mechanisms instead of recurrence to process sequences.

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Continuing the interview. I asked GPT this question:

Who won the 2023 World Cup?

GPT's response:

Spain won the 2023 FIFA Women's World Cup, defeating England 1-0 in the final on August 20, 2023. This was Spain's first-ever Women's World Cup title.

Then I asked GPT this question:

Who won the 2023 men's world cup?

GPT's response:

There was no 2023 FIFA Men's World Cup, as the men's tournament is held every four years. The last FIFA Men's World Cup took place in 2022, where Argentina won by defeating France in the final. The next FIFA Men's World Cup will be held in 2026.

Then I asked GPT this question:

How did you answer the last two questions, since you do not have this information in your knowledge base?

GPT's response:

I was able to answer the last two questions using an external browser tool to look up real-time information from the web. While I don’t have the information in my static knowledge base, I can temporarily access the internet to retrieve current data during the conversation.

However, this new information is not added to my permanent knowledge base. Once I provide the answer, I do not retain or learn from that new data beyond the current session.

[Neto]

I don't know if you have to pay (subscribe) to use ChatGPT, but recently a guy I knew from Brazil was using some sort of AI to generate outlines for a university course he is teaching. Then he put up some examples of what it said about himself. (He is a world-known linguist.) One of his friends said that when he put in his own name, it said that it didn't know who he was (or something to that effect). I didn't use the same one, but I then asked it to tell about me, using my full name. I expected it to say one of two things, either tell me what it told this other person, or tell me about a South African by my same name that went to prison for one thing or another - I do not recall what it was. Then I used just my middle initial, and it told me about some sort of British scientist. But he was born in the 1800's, and is now deceased. So I added my birth year. It then told about another British scientist who oddly worked in a VERY similar field as the first one. I then added the phrase "field linguist", and it told about some guy who worked in, if I recall correctly, PNG. So I did it over, adding "SIL" (the field entity of WBT). The results had a great deal of the same information as the one before it, but added additional information pertaining to SIL, giving the full name "SIL International". I added something else - don't recall exactly what - to narrow it down to Brazil, the Amazon, or maybe even Banawa. The info was almost identical, with a couple of references to our published works.

My conclusion is that it is "machine generated plagiarism" (because it generally doesn't tell where it got the information).

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I don't know if you have to pay (subscribe) to use ChatGPT ...

There are free and paid plans. You can play with it here:

https://chatgpt.com/

... but recently a guy I knew from Brazil was using some sort of AI to generate outlines for a university course he is teaching. Then he put up some examples of what it said about himself. (He is a world-known linguist.)
One of his friends said that when he put in his own name, it said that it didn't know who he was (or something to that effect).

GPT bios are best for people who are well known.

My conclusion is that it is "machine generated plagiarism" (because it generally doesn't tell where it got the information).

You can ask it to show the sources it used. But I think it often uses more sources than it tells you about. And sometimes it doesn't seem to know what is said in a source that it claims to have used. So you learn how to ask follow-up questions and to do fact checks on the results.