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.
I’m not sure the 300-bit → 1,000-bit framing applies in all instances. The 300 bits may be a compressed cue to a much fuller idea. The AI can combine that cue with its prior knowledge to help reconstruct what the prompter was trying to express, with the prompter then verifying whether it’s right. Without the relevant prior knowledge for reconstruction, or the prompter for verification, it becomes much harder to know whether you’ve reconstructed the intended idea.
Unless that 700 bit was transferred on a separate occasion the inferred 700 bits is not true information, anyone could have reconstructed it from the 300 bits.
That assumes each bit is a coinflip, doesn't it?
Even Markov chain autocorrect tools do better than 50% odds*, and even GPT-2 was significantly better than that kind of autocorrect.
* at the word level; IDK how redundant/efficient language is when it comes to bits-worth-of-fact-claims-per-word. But "your cat is sitting on my" -> [mat, laundry, roof, head, belly, laptop, microwave, …] clearly has many bits of information, and a Markov chain will encode the most likely next word even if the user doesn't know what the most likely next word is. Verifying where the cat is sitting is also very easy, as is correction.
Far more than 1 bit for most attempts: they need that just to be able to write coherent sentences, and a lot more to be coherent sentences on the right topic.
Some specific conclusions would be far less than 1 bit.
The average will depend on both the question and the AI.
LLM is not a random symbol generator (hint: training data is not random), and no reasonable person is going to just prompt an LLM and send its output without giving it at least cursory check (at the very least so that blatantly stupid hallucinations don't paint the sender as inconsiderate or incompetent).
That check alone can add bits to the final signal.
> and no reasonable person is going to just prompt an LLM and send its output without giving it at least cursory check
I'm reminded of an old quote:
The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man.
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.
zefalt · · focus · HN ↗
shiandow · · focus · HN ↗
TeMPOraL · · focus · HN ↗
shiandow · · focus · HN ↗
ben_w · · focus · HN ↗
Even Markov chain autocorrect tools do better than 50% odds*, and even GPT-2 was significantly better than that kind of autocorrect.
* at the word level; IDK how redundant/efficient language is when it comes to bits-worth-of-fact-claims-per-word. But "your cat is sitting on my" -> [mat, laundry, roof, head, belly, laptop, microwave, …] clearly has many bits of information, and a Markov chain will encode the most likely next word even if the user doesn't know what the most likely next word is. Verifying where the cat is sitting is also very easy, as is correction.
shiandow · · focus · HN ↗
ben_w · · focus · HN ↗
Some specific conclusions would be far less than 1 bit.
The average will depend on both the question and the AI.
shiandow · · focus · HN ↗
A LLM adds noise, not information. At least in this framing.
TeMPOraL · · focus · HN ↗
LLM is not a random symbol generator (hint: training data is not random), and no reasonable person is going to just prompt an LLM and send its output without giving it at least cursory check (at the very least so that blatantly stupid hallucinations don't paint the sender as inconsiderate or incompetent).
That check alone can add bits to the final signal.
ben_w · · focus · HN ↗
I'm reminded of an old quote:
TeMPOraL · · focus · HN ↗