4 ms·

This feels like a limited and perhaps naive perspective on LLMs. If you looked at computers as adding machines in the 60s/70s then you'd be missing most of what

This feels like a limited and perhaps naive perspective on LLMs. If you looked at computers as adding machines in the 60s/70s then you'd be missing most of what was interesting about computers. And if you look at LLMs as a question answering service now, you are also missing a lot. It's hard to compare trust of LLMs to other computing, because many of the things that LLMs get wrong and right were previously intractable. You could ask a search engine, but it's certainly no more trustworthy than an LLM, gameable in its own way. The closest might be a knowledge graph or database, which can formally represent some portion of what an LLM represents. To be fair the relational systems can and will give "no answer" when an LLM (like a search engine) always gives some answer. Certainly an issue! But this is all in the realm of coming up with answers in a closed system, hardly the only way LLMs can be used. LLMs can also come up with questions, for instance creating queries for a database. Are these trustworthy? Not entirely, but the closest alternative supportive tool is perhaps some query builder...? I have seen expert humans come up with untrustworthy queries as well... misinterpretation of data is easy and common. That's just one example of how an LLM can be used. If you use an LLM for something that you can directly compare to a non-LLM system, such as speech recognition or intent parsing, it's clear that the LLM is more trustworthy. It can and does do real error correction! That is, you can get higher quality data out of an LLM than you put in. This is not unheard of in computing, but it is uncommon. Internet networking, which Kay refers to, might be an analog... creating reliable connections on top of unreliable connections. What we don't have right now is systematic approaches to computing with LLMs.

r00fus 3y ago
> If you use an LLM for something that you can directly compare to a non-LLM system, such as speech recognition or intent parsing, it's clear that the LLM is more trustworthy Except for the hallucination problem, sure. How do you ensure the answer you get from an LLM is not a hallucination? It sure doesn't.
- pixl97 3y ago
  How do you ensure any bits of data? You going to fist fight your uncle at the next family reunion when he promises you pigs can actually fly? There are plenty of bits of data that can be hard references to datasets or actual physics to assign their probability value. And there are other things that can't.
  - r00fus 3y ago
    Completely inefficient to chain logic through LLMs if they aren't baseline reliable.
- ianbicking 3y ago
  "How do you ensure the answer you get from an LLM is not a hallucination?" Intent parsing and speech recognition both frequently return inaccurate responses. It is true that with an LLM it can return something that is sensible but invented, where other systems typically return less inventive wrong answers. With any of these systems you want to balance the probability of an incorrect interpretation against the impact of the action being taken, and get confirmation based on that. That's pretty normal engineering and UX.
pixl97 3y ago
“All models are wrong, some are useful.” What's kind of funny is almost all the people that complain about accuracy of LLMs would gladly answer the question of "What's the chance for rain today" without giving giving you a 5 minute lecture on what forecasts actually mean.
- soperj 3y ago
  "I'm unable to provide real-time weather updates as my data is not current. To find out the chances of rain today, you can check your local weather forecast through a reliable weather website or app, or tune in to a local news station for the most up-to-date information." - chatgtp
  - brookst 3y ago
    "Seattle is known for its rainy weather, especially during the spring months. According to historical data, March typically sees an average of 17 rainy days in Seattle. Given that we're in the middle of March, there's a fairly high likelihood of rain on any given day. Without access to real-time weather data, I'd estimate that there's approximately a 50-60% chance of rain in Seattle on March 20th, based on historical averages. However, for the most accurate forecast, I'd recommend checking a reliable weather website or app closer to the date." -- chatgpt (prompted with the current date, location, an admonition to estimate, and a promise that I understand it will likely be wrong)
- andrewflnr 3y ago
  Yeah because most everyone you talk to knows how to treat weather forecasts, but LLMs are still widely misunderstood. The right idea seems to be getting out, but in no small part due to the "complaining" you mock.
alexvoda 3y ago
There was a time when a search engine would return no results if it didn't find anything instead of returning increasingly irrelevant results. It was a conscious decision by corporations to implement the dropping of constraints and search terms when too few results would have been returned. Today the search operators are a joke.

r00fus 3y ago

> If you use an LLM for something that you can directly compare to a non-LLM system, such as speech recognition or intent parsing, it's clear that the LLM is more trustworthy Except for the hallucination problem, sure. How do you ensure the answer you get from an LLM is not a hallucination? It sure doesn't.

pixl97 3y ago
How do you ensure any bits of data? You going to fist fight your uncle at the next family reunion when he promises you pigs can actually fly? There are plenty of bits of data that can be hard references to datasets or actual physics to assign their probability value. And there are other things that can't.
- r00fus 3y ago
  Completely inefficient to chain logic through LLMs if they aren't baseline reliable.
ianbicking 3y ago
"How do you ensure the answer you get from an LLM is not a hallucination?" Intent parsing and speech recognition both frequently return inaccurate responses. It is true that with an LLM it can return something that is sensible but invented, where other systems typically return less inventive wrong answers. With any of these systems you want to balance the probability of an incorrect interpretation against the impact of the action being taken, and get confirmation based on that. That's pretty normal engineering and UX.

pixl97 3y ago

“All models are wrong, some are useful.” What's kind of funny is almost all the people that complain about accuracy of LLMs would gladly answer the question of "What's the chance for rain today" without giving giving you a 5 minute lecture on what forecasts actually mean.

soperj 3y ago
"I'm unable to provide real-time weather updates as my data is not current. To find out the chances of rain today, you can check your local weather forecast through a reliable weather website or app, or tune in to a local news station for the most up-to-date information." - chatgtp
- brookst 3y ago
  "Seattle is known for its rainy weather, especially during the spring months. According to historical data, March typically sees an average of 17 rainy days in Seattle. Given that we're in the middle of March, there's a fairly high likelihood of rain on any given day. Without access to real-time weather data, I'd estimate that there's approximately a 50-60% chance of rain in Seattle on March 20th, based on historical averages. However, for the most accurate forecast, I'd recommend checking a reliable weather website or app closer to the date." -- chatgpt (prompted with the current date, location, an admonition to estimate, and a promise that I understand it will likely be wrong)
andrewflnr 3y ago
Yeah because most everyone you talk to knows how to treat weather forecasts, but LLMs are still widely misunderstood. The right idea seems to be getting out, but in no small part due to the "complaining" you mock.

alexvoda 3y ago

There was a time when a search engine would return no results if it didn't find anything instead of returning increasingly irrelevant results. It was a conscious decision by corporations to implement the dropping of constraints and search terms when too few results would have been returned. Today the search operators are a joke.