11–20 of 32 posts
“…the outcomes of Large Language Models are not designed to be true — they are merely designed to be statistically likely.” yep!
A search engine does not return a true answer. It returns a set of possibilities ranked by likelihood. An LLM is just an aggregation of search results.
Modern LLMs are empirically a great way to compress vast amounts of text. Being able to ask one of the better open tunes a question I would normally ask Google makes it possible to work from anywhere in a way that hasn’t been true since I was a kid. Big, overfunded, ethically and legally dubious data-vacuum black box APIs are and should be controversial. Stack Overflow and much else in 30-70Gb on my MacBook Pro on a…
Lossily compress.
Except they kinda are? LLMs are just word models built from a corpus of the internet. There are examples of GPT3 regurgitating reddit comments in full given the right prompt. Certainly I find LLMs replace a lot of searches for me and google/microsoft is right to eat its own breakfast to get ahead of it.
In any case, LLMs feeded with carefully curated content instead of random pages or social networks posts (maybe upvoted because funny instead of accurate) may have better chances of giving out good results.
I don’t know about you, but I am using search engines a lot less, and asking LLM’s a lot more.
It doesn’t bode well for the search engine business overall.
How I interpret it: it is a more powerful version of stemming and synonym expansion of information retrieval classics when generating the queries it feeds into traditional information systems (such as the Bing search engine via API, or other index).
After retrieval, it's a selector and summarizer of repetition seen in the results to give you something of a blended outcome, pertinent to the prompt you gave it. Like any other tool, you get a feel for when it has is having problems, and some of those problems can be assessed by at least glancing at the sources it consulted. You get all sorts of weird stuff when your sources don't include relevant results or biased results
The first problem happens when the documents you are searching for do not exist, or something about your prompt -- it's usually obvious what it is -- is not sourcing documents you know to exist.
The second, bias, I've seen when researching something like the design conceits of Infiniband. While it has its genuine virtues, almost nobody talks about it...and many of those things that discuss it are Infiniband marketing materials that are both a bit too fluffy and sometimes stretch the truth, as marketing materials are wont to do. But you can spot this in the sources panel immediately.
I never found "disembodied" LLMs very useful.
“…the outcomes of Large Language Models are not designed to be true — they are merely designed to be statistically likely.” yep!
A search engine does not return a true answer. It returns a set of possibilities ranked by likelihood. An LLM is just an aggregation of search results.
There is a world of difference between these two results.
Ultimately the problem is that a single repository of all verifiably true human knowledge just doesn't exist and both search engines and LLMs are copes to deal with this fact.
Except they kinda are? LLMs are just word models built from a corpus of the internet. There are examples of GPT3 regurgitating reddit comments in full given the right prompt. Certainly I find LLMs replace a lot of searches for me and google/microsoft is right to eat its own breakfast to get ahead of it.
Agreed, I don't see why the abundance of pages that come up in searches these days with a paragraph of text and a long scroll of ads are doing any better than the generalizations LLMs are trained to make. I think it's also worth pointing out the more advanced LLMs are exceptionally accurate (despite being imperfect and not without bias) and highly available. That's not a peg below your average search result.
It's not that I trust randos on reddit, it's that even when they're wrong, I can link someone to the same bad advice and it doesn't change on me, for the most part.