I find that GPT's answers are for the most part more reliable the searches, specifically today's searches. In the last 12 months, search results have become so spammy with AI generated pages (oh the irony), that it's hard to find reliable answers.
So like search, I look at GPT's answers with a grain of salt and validate them, but these days I use GPT all day every day and search rarely. To be fair, I use it a lot because I have a GPT CLI that works just the way I want it to, since I wrote it :-). https://github.com/drorm/gish
It seems like you've been using similar workflows to what I've been trying for coding with gpt?
https://github.com/paul-gauthier/easy-chat#created-by-chatgp...
-----
#import ~/work/gish/tasks/coding.txt
Change the following so that it looks for the open AI key in the following fashion:
1. env variable
2. os.home()/.openai
3. Throws an exception telling the user to put it in one of the above, and then exits
#diff ~/work/gish/src/LLM.ts
-----
Puts me in vimdiff comparing the old code with the generated code letting me review and cherry pick the changes.
I haven't seen anyone else describing this workflow. Feed it the existing code, ask it to modify/improve/fix the code and output a new version of all the input code, review diffs.
It has downsides, because you can easily run out of context window of chatgpt-3.5-turbo. But I am getting much better code out of it versus other approaches I've tried. And it's a very efficient and natural workflow -- we're used to getting and reviewing diffs/PRs from human collaborators.
It's actually $0.002/1k, FYI
Also, I wonder how they decide what code is worth training on. Because a lot of code is written in poor style/has technical debt, it might be the case that these LLMs in the long run lead to an increase in the technical debt in our society. Plus, eventually, and this might already be happening, the LLM are going to end up training on their own outputs, so that could lead to self immolation by the model. I am not certain RLHF completely resolves this issue.
This. The value proposition is very clearly tied to the quality of the training data, and if there's secret sauce for automatically determining information quality that's obviously huge. Google was built in part on such insights. I suspect they do have something. I'd be utterly astonished if quality sorting were an emergent property of LLMs (especially given it's iffy in humans).
The problem, of course, is that if they do have a way of privileging data for training, that information is going to be the center of the usual arms race for attention and thinking. It can't be truly public or it's dead.
Rebecca Jarvis interviews Sam Altman for ABC News Rebecca Jarvis, https://www.youtube.com/watch?v=540vzMlf-54
(I don't think this contradicts what you said.)
Quoting what he says [0][1]:
> You know, a funny thing about the way we're training these models is I suspect too much of the like processing power for lack of a better word is going into using the models as a database instead of using the model as a reasoning engine. The thing that's really amazing about the system is that it, for some definition of reasoning, and we could of course quibble about it and there's plenty for which definitions this wouldn't be accurate. But for some definition it can do some kind of reasoning. And, you know, maybe like the scholars and the experts and like the armchair quarterbacks on Twitter would say, no, it can't. You're misusing the word, you know, whatever, whatever. But I think most people who have used the system would say, okay, it's doing something in this direction. And I think that's remarkable. And the thing that's most exciting and somehow out of ingesting human knowledge, it's coming up with this reasoning capability. However, we're gonna talk about that. Now, in some senses, I think that will be additive to human wisdom.
[0] https://steno.ai/lex-fridman-podcast-10/367-sam-altman-opena...
Google, in comparison, returned absolutely irrelevant SEO spam.
Sometimes search means “I can sort of describe what I’m looking for, can you tell me what it’s called?”. LLMs excel here. I told GPT4 I’m doing computer animation and want to do smooth blending, it told me that’s called “interpolation”, I asked for some common terms in the literature about this to help me look and it told me about LERP, SLERP, quaternions, splines, Beziers, keyframes, inverse kinematics, and motion capture. All useful jumping-off points. (A subset of this type of search is “I know what this is called, can you tell me more about it?”. This is probably the place where LLMs sell snake oil the most; they always provide a convincing explanation of the thing, but there’s no guarantee on veracity.)
Other times, search means “I have a specific phrase and I want to find occurrences of it”. LLMs aren’t just bad at this, they are constitutionally incapable of it. The way you build an LLM necessarily involves taking all specific phrases and occurrences thereof, and blending them up into a word slurry that is then condensed and abstracted into floating point weights. It no longer has the specifics to give you. It’s a shame that search engines have let this task (“ctrl-f the web”) fall by the wayside. It’s probably a large part of why people think Google search sucks now, it certainly is for me. (There’s this one essay about the Harappan civilization that I used to be able to find by searching for “strange builders mist of time”, I definitively remember that exact phrase working for me many years ago, and now it does not work and I cannot find that essay anymore.)
I agree: I do use it as a search engine myself for a bunch of things, but those tend to be things where I've developed a strong intuition that it's likely to give me a reasonable result.
People who haven't developed that intuition yet tend to run into problems - and will often then loudly proclaim that LLMs are evidently useless and shouldn't be trusted for anything.
One trick I use is to assume it has a "Wikipedia-level" knowledge of pretty much any topic. Often that's what I need! I want to ask some quick questions of someone eloquent who's read the Wikipedia article about something, to save me from having to read the whole thing myself.
If I need more expertise than you can get from reading Wikipedia I know that ChatGPT alone is very unlikely to cut it.
Sure things in Wikipedia or official documents could be accurate, but the internet is still full of misinformation