Natural-language messages between LLM agents are an architectural anti-pattern
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Re: Natural-language messages between LLM agents are an architectural anti-pattern
#2[flagged]
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#3The post is AI-written, so I did not read it. But based on title and abstract I'll have to disagree.
The native content LLMs understand is text. They were literally trained on it. They much prefer it to any arbitrary structure you could come up with.
We're used to think computers prefer content that is structured and binary etc; but with LLMs that changed.
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#4The post is AI-written, so I did not read it. But based on title and abstract I'll have to disagree. The native content LLMs understand is text. They were literally trained on it. They much prefer it to any arbitrary structure you could come up with. We're used to think computers prefer content that is structured and binary etc; but with LLMs that changed.
Their native content is semantic vectors. They had to be trained for a long time to convert between text and semantic vectors, and the conversion is very lossy. Seahorse emoji demonstrates this nicely, the LLM internally holds a semantic vector for seahorse+emoji but the output translation layer can't match it.
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#5The post is AI-written, so I did not read it. But based on title and abstract I'll have to disagree. The native content LLMs understand is text. They were literally trained on it. They much prefer it to any arbitrary structure you could come up with. We're used to think computers prefer content that is structured and binary etc; but with LLMs that changed.
Their native content is semantic vectors. They had to be trained for a long time to convert between text and semantic vectors, and the conversion is very lossy. Seahorse emoji demonstrates this nicely, the LLM internally holds a semantic vector for seahorse+emoji but the output translation layer can't match it.
> Seahorse emoji demonstrates this nicely, the LLM internally holds a semantic vector for seahorse+emoji but the output translation layer can't match it.
I am curious about this, how can the LLM hold the embedding for seahorse+emoji if it doesn’t exist? How did it end up like this? Perhaps the dataset had discussions from people about new potential emojis?
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#6Earlier quoted context omitted.
Their native content is semantic vectors. They had to be trained for a long time to convert between text and semantic vectors, and the conversion is very lossy. Seahorse emoji demonstrates this nicely, the LLM internally holds a semantic vector for seahorse+emoji but the output translation layer can't match it.
> Seahorse emoji demonstrates this nicely, the LLM internally holds a semantic vector for seahorse+emoji but the output translation layer can't match it. I am curious about this, how can the LLM hold the embedding for seahorse+emoji if it doesn’t exist? How did it end up like this? Perhaps the dataset had discussions from people about new potential emojis?
Because it's just the embedding for a seahorse plus the embedding for an emoji symbol output.
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#7> It makes individual agents untestable because their inputs and outputs are strings
Strings can't be valid test vectors? Large language model are highly non-deterministic by design, no matter what.
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#8> It makes individual agents untestable because their inputs and outputs are strings Strings can't be valid test vectors? Large language model are highly non-deterministic by design, no matter what.
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Re: Natural-language messages between LLM agents are an architectural anti-pattern
#9The post is AI-written, so I did not read it. But based on title and abstract I'll have to disagree. The native content LLMs understand is text. They were literally trained on it. They much prefer it to any arbitrary structure you could come up with. We're used to think computers prefer content that is structured and binary etc; but with LLMs that changed.
[dead]
Re: Natural-language messages between LLM agents are an architectural anti-pattern
#10The post is AI-written, so I did not read it. But based on title and abstract I'll have to disagree. The native content LLMs understand is text. They were literally trained on it. They much prefer it to any arbitrary structure you could come up with. We're used to think computers prefer content that is structured and binary etc; but with LLMs that changed.
If only we had spent any time as an industry coming up with structured formats for text...