LLMs- Not just the next word

Someone remarked to me:

Questioner:
It is hard to believe that LLMs can create such rich conversation from an algorithm that just tries to predict the next word.

Are we any different? Doesn't all of our behavior come from what we're going to do next?

Yes, sometimes we plan further than that and prepare. But let's look at that.

Maybe in order to say the next word, I have to first plan the next sentence. And in order to do that, I need to have point. And to have a point, I need an intention and/or a perspective. So, yes, maybe having the perspective leads to intentions leads to a point which leads to a sentence which tells me what my next word is. But maybe all the motivation for everything behind the next word is a natural adaptation of a machine that's built to generate the next word.

Questioner:
I prefer to believe that we don’t really understand how the human mind works and we don’t really understand how AI/LLMs produce such amazing output.

In college, my second year I roomed with people who struggled to get Cs. In one conversation someone said, "No one really understands infinity. It's just too big." The other 5 agreed. They couldn't internalize "no limit." But that doesn't mean "we" don't really understand it.

We don't really understand how electric charge works either, how things at a distance attract each other. We name this influence "a field," but that doesn't explain it. On the other hand, we call "things with opposite charge attract each other" an understanding. We've made sense of it.

Maybe saying it a bit differently will help:
To me, "predicting the next word" based on training makes sense. A "word" isn't in the system. The system organizes itself to model the word and its expected contexts. A neural net is based on "predicting the next word" is probably actually capable of "predicting the next thing" where a "thing" is something a set of its nodes model. So it's capable of modeling phrases, sentences, ideas, tables, themes, etc, if it has enough nodes.

That's the understanding that I've concocted. I don't know if it's accurate, or if others (the "we") have a similar understanding. But it makes sense to me. It has some similarities to templates in programming languages (the things) and to recursion (e.g. in algorithms and in fractals) and to my fledgling understandings of neural nets, the brain, and self-organizing structures in general.

Another way of thinking about it is that if it produced text when you asked for a table, that would violate its input, so it wouldn't do that.

But I asked ChatGPT which has probably read a lot about AI... Huh, it agree with me! But I asked it pretty leading questions.

At the start it said:

In Q1: The network consists of multiple layers of interconnected nodes that process and transform the input data.

In Q2: these layers work collectively to capture and model the complex patterns and structures present in the training data. ... The higher layers of an LLM capture more abstract and contextual information... higher layers learn to represent more abstract concepts, syntactic structures, and even semantic relationships

In Q3: In essence, although the layers of an LLM do not have a one-to-one correspondence with linguistic units, they collectively learn to capture the patterns and structures inherent in the training data, which can indeed align with the patterns observed in phrases, sentences, paragraphs, and larger linguistic units.

Q1: Rand: Hey, Chatty:
Explain to me how LLMs, or AIs in general, can create phrases, sentences, paragraphs, stories and explanations based on an algorithm of "predicting the next word."

ChatGPT:
Language models like GPT-3.5, which includes large language models (LLMs) and other AI models, are designed to understand and generate human-like text. They can create phrases, sentences, paragraphs, stories, and explanations by leveraging a powerful algorithm known as "predicting the next word." Here's an explanation of how this algorithm works:

Training Data: LLMs are trained on a vast amount of text data from the internet. This data includes books, articles, websites, and various other sources. During training, the model learns to recognize patterns, syntax, grammar, and semantic relationships within the text.

Input and Tokenization: When you provide a prompt or a partial sentence to an LLM, it tokenizes the input. Tokenization is the process of breaking down the text into smaller units called tokens. These tokens can be words, punctuation marks, or even subword units like prefixes or suffixes.

Context and Prediction: Once the input is tokenized, the LLM analyzes the context and predicts the most probable next token based on the patterns it has learned during training. It uses a sophisticated neural network architecture to make these predictions. The network consists of multiple layers of interconnected nodes that process and transform the input data.

Probability Distribution: The LLM calculates a probability distribution over the entire vocabulary of possible next tokens. This distribution assigns a probability score to each token, representing the likelihood of it being the next word given the context. The model selects the token with the highest probability as the predicted next word.

Sampling and Diversity: Instead of always choosing the most probable word, LLMs can employ various techniques to introduce randomness and diversity in their output. For example, they can use a sampling approach where the next word is selected randomly based on the probability distribution. This adds creativity and variability to the generated text.

Iterative Process: The prediction of the next word becomes the input for the next step, and the process is repeated iteratively to generate longer pieces of text. The model incorporates the previously generated words as part of the context, allowing it to generate coherent and meaningful sentences, paragraphs, or stories.

Fine-tuning and Control: LLMs can also be fine-tuned on specific datasets to specialize in certain tasks or follow specific guidelines. This enables more controlled generation of text by conditioning the model on specific prompts or providing additional constraints.

By utilizing the "predicting the next word" algorithm, LLMs can generate human-like text that is coherent, contextually relevant, and demonstrates a strong understanding of language patterns. However, it's important to note that while LLMs are incredibly powerful, they are not infallible and may occasionally produce incorrect or nonsensical outputs.

Q2: Rand:
Again, explain to me how LLMs, or AIs in general, can create phrases, sentences, paragraphs, stories and explanations based on an algorithm of "predicting the next word." Previously, you said, "The network consists of multiple layers of interconnected nodes that process and transform the input data." What do you mean by "layers"? Do higher layers model phrases, sentences, or other higher-order kinds of things or meaning?

I apologize for any confusion caused. When I mention "layers" in the context of LLMs or neural networks, I am referring to the structure of the model architecture. A neural network consists of multiple layers of interconnected nodes, also known as neurons. Each layer performs specific computations and transformations on the input data.

In the case of LLMs, these layers typically comprise transformer architectures, which have proven to be effective in natural language processing tasks. The transformer architecture consists of several layers of self-attention and feed-forward neural networks.

To clarify the role of layers in the context of predicting the next word, here's a simplified explanation of how an LLM generates text:

Input Encoding: The initial layer encodes the input tokens into numerical representations, capturing their semantic and syntactic properties.

Self-Attention: The subsequent layers employ self-attention mechanisms that allow the model to analyze the relationships between different tokens in the input sequence. Self-attention helps the model understand the context and dependencies between words.

Contextual Embeddings: As the input flows through the layers, the model updates the embeddings of each token based on the contextual information from the surrounding tokens. This contextual information enables the model to capture higher-order dependencies and meaning.

Predicting the Next Word: The final layer(s) of the model use the contextualized embeddings to predict the next word or token in the sequence. This prediction is based on the learned patterns and relationships from the training data. The model calculates the probability distribution over the vocabulary of possible next tokens and selects the most likely one as the predicted word.

Regarding your question about higher layers modeling phrases, sentences, or higher-order meaning, it's important to note that the layers of an LLM do not have a direct correspondence to specific linguistic units like phrases or sentences. Instead, these layers work collectively to capture and model the complex patterns and structures present in the training data.

The higher layers of an LLM capture more abstract and contextual information. While lower layers may capture local dependencies and word-level patterns, higher layers learn to represent more abstract concepts, syntactic structures, and even semantic relationships. This enables the model to generate coherent and meaningful text by incorporating information from the broader context.

In summary, the layers in an LLM play a crucial role in processing and transforming the input data, capturing contextual information, and predicting the next word or token based on learned patterns and relationships. While higher layers do capture more abstract information, it's important to note that the concept of layers in LLMs is more about computational units than direct linguistic units like phrases or sentences.

Q3: Rand:
You said, "it's important to note that the layers of an LLM do not have a direct correspondence to specific linguistic units like phrases or sentences. Instead, these layers work collectively to capture and model the complex patterns and structures present in the training data." But aren't these "complex patterns and structures" roughly similar to some of our model that are captured by phrases, sentences, and paragraphs?

ChatGPT
Yes, you are correct. While the layers in an LLM do not have a direct correspondence to specific linguistic units like phrases or sentences, they do collectively capture and model the complex patterns and structures present in the training data. These patterns and structures can indeed be similar to those captured by phrases, sentences, and paragraphs.

As the LLM goes through the layers, it learns to recognize and represent various linguistic patterns, such as word co-occurrences, syntactic relationships, and semantic associations. These patterns and structures can span different levels of granularity, including individual words, phrases, sentences, and even larger units like paragraphs or stories.

While specific layers may not be explicitly dedicated to modeling phrases, sentences, or paragraphs, the hierarchical nature of the neural network allows the model to capture information at different levels of abstraction. The lower layers may learn to identify and represent basic lexical or syntactic patterns, while the higher layers can capture more complex compositional structures that resemble phrases, sentences, or paragraphs.

In essence, although the layers of an LLM do not have a one-to-one correspondence with linguistic units, they collectively learn to capture the patterns and structures inherent in the training data, which can indeed align with the patterns observed in phrases, sentences, paragraphs, and larger linguistic units.

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