Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
> If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are.
LLMs do inference or computation among other things, so the remaining 700 bits can be something like that. The hidden implication in your claim is that computation adds no information content, which leads to an interesting philosophical discussion.
So for example, if I ask an LLM to give a proof or derive a new theorem from a set of axioms, according to your assumption, if it answers correctly, then I haven't learned anything new.
I am not really sure how to resolve this paradox in information theory.
If you input 300 bits into an LLM, and it outputs an additional 700bits of new information, I’d still rather you tell me those 1000bits than read ann llm’s output of 1000bits.
The primary reason is that human language is becoming a proof of work, that speaking out loud or writing directly indicates that the idea is important enough for a human to express. This is more costly than llm output, which is often just botspam.
From a purely information-theory perspective, the simplest solution is to say that yes, any content derived from existing information carries no information itself.
From a realistic perspective in the context of people copy-pasting LLM output, my thoughts are that asking an LLM to research for you is more defensible, but it's still better to read the LLM's research results and write the important parts in your own words (partly because the LLM probably used way more words than necessary for the context).
hatthew · · focus · HN ↗
Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
js8 · · focus · HN ↗
LLMs do inference or computation among other things, so the remaining 700 bits can be something like that. The hidden implication in your claim is that computation adds no information content, which leads to an interesting philosophical discussion.
So for example, if I ask an LLM to give a proof or derive a new theorem from a set of axioms, according to your assumption, if it answers correctly, then I haven't learned anything new.
I am not really sure how to resolve this paradox in information theory.
bitmasher9 · · focus · HN ↗
The primary reason is that human language is becoming a proof of work, that speaking out loud or writing directly indicates that the idea is important enough for a human to express. This is more costly than llm output, which is often just botspam.
js8 · · focus · HN ↗
hatthew · · focus · HN ↗
From a realistic perspective in the context of people copy-pasting LLM output, my thoughts are that asking an LLM to research for you is more defensible, but it's still better to read the LLM's research results and write the important parts in your own words (partly because the LLM probably used way more words than necessary for the context).