You do not understand how GPT models work. It does more than 'importance weighting' and an absolutely massive amount of knowledge about the world is encoded in those weights.
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I asked ChatGPT to help you better understand how it works:
There are a few common misconceptions in that comment regarding how large language models (LLMs) like GPT-4 actually work, so let's clarify those:
Markov Chain Comparison:
LLMs are not based on Markov chains, though they might seem similar at a high level due to their ability to predict the next word in a sequence.
Markov chains rely on simple probabilistic transitions between states, often based on a very limited "memory" of previous states (e.g., the previous word or two). LLMs, on the other hand, use a transformer architecture, which allows them to consider long-term dependencies and relationships in text. This means they can account for the context of many preceding words, sentences, or even paragraphs when generating responses.
No "Understanding":
While it’s true that LLMs do not have consciousness, self-awareness, or human-like understanding, the term “understanding” can be misleading. They operate by modeling patterns in language, but in a highly sophisticated way.
LLMs capture a deep representation of the relationships between words, sentences, and broader concepts through billions of parameters, giving them a kind of statistical "understanding" of language. This enables them to generate coherent and contextually appropriate responses, even if it’s not the same as human comprehension.
Importance Weighting and Search:
LLMs do not search through predefined sets of phrases or apply “importance-weighting” to words in the way described. They generate text dynamically by using the probabilities derived from the training data they’ve seen.
The model calculates probabilities for each possible next word in the sequence, taking into account the entire context (not just key terms), and selects the next word based on these probabilities. This process is not about tagging words as important but about predicting the next most likely word or phrase given the context.
Not Just "Anti-Nonsense Filtering":
The quality of LLM output doesn’t arise from filtering out nonsense but from the underlying model’s ability to capture the complexity of human language through its learned representations. While there's a certain degree of training that discourages incoherent outputs, the coherent responses you see are mostly due to the model's training on vast, high-quality datasets.
"Thoroughly Stupid":
It's more accurate to say that LLMs are highly specialized in a particular domain: the patterns of human language. They excel at generating contextually relevant responses based on their training data. While they lack human-style cognition, calling them "stupid" overlooks the complexity of what they achieve within their domain.
In summary, LLMs use advanced neural networks to predict and generate language, capturing sophisticated patterns across large datasets. They don't "understand" in a human sense, but their ability to model language goes far beyond simple mechanisms like Markov chains or weighted searches.
---
I asked ChatGPT to help you better understand how it works:
There are a few common misconceptions in that comment regarding how large language models (LLMs) like GPT-4 actually work, so let's clarify those:
Markov Chain Comparison:
LLMs are not based on Markov chains, though they might seem similar at a high level due to their ability to predict the next word in a sequence. Markov chains rely on simple probabilistic transitions between states, often based on a very limited "memory" of previous states (e.g., the previous word or two). LLMs, on the other hand, use a transformer architecture, which allows them to consider long-term dependencies and relationships in text. This means they can account for the context of many preceding words, sentences, or even paragraphs when generating responses. No "Understanding":
While it’s true that LLMs do not have consciousness, self-awareness, or human-like understanding, the term “understanding” can be misleading. They operate by modeling patterns in language, but in a highly sophisticated way. LLMs capture a deep representation of the relationships between words, sentences, and broader concepts through billions of parameters, giving them a kind of statistical "understanding" of language. This enables them to generate coherent and contextually appropriate responses, even if it’s not the same as human comprehension. Importance Weighting and Search:
LLMs do not search through predefined sets of phrases or apply “importance-weighting” to words in the way described. They generate text dynamically by using the probabilities derived from the training data they’ve seen. The model calculates probabilities for each possible next word in the sequence, taking into account the entire context (not just key terms), and selects the next word based on these probabilities. This process is not about tagging words as important but about predicting the next most likely word or phrase given the context. Not Just "Anti-Nonsense Filtering":
The quality of LLM output doesn’t arise from filtering out nonsense but from the underlying model’s ability to capture the complexity of human language through its learned representations. While there's a certain degree of training that discourages incoherent outputs, the coherent responses you see are mostly due to the model's training on vast, high-quality datasets. "Thoroughly Stupid":
It's more accurate to say that LLMs are highly specialized in a particular domain: the patterns of human language. They excel at generating contextually relevant responses based on their training data. While they lack human-style cognition, calling them "stupid" overlooks the complexity of what they achieve within their domain. In summary, LLMs use advanced neural networks to predict and generate language, capturing sophisticated patterns across large datasets. They don't "understand" in a human sense, but their ability to model language goes far beyond simple mechanisms like Markov chains or weighted searches.