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Lessons after a Half-billion GPT Tokens

kenkantzer.com

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Re: Lessons after a Half-billion GPT Tokens

#151

Post by throwup238 »

ShamelessC wrote: post #145
Kiro wrote: post #135

Earlier quoted context omitted.

There's nothing to switch to. You just enable it. No need to change the prompt or anything else. All it requires is that you mention "JSON" in your prompt, which you obviously already do.

I think that’s only true when using ChatGPT via the web/app, not when used via API as they likely are. Happy to be corrected however.

If you don’t know, why speculate on something that is easy to look up in documentation?

https://platform.openai.com/docs/guides/text-generation/json...

Re: Lessons after a Half-billion GPT Tokens

#152

Post by chasd00 »

squigz wrote: post #122
egonschiele wrote: post #103

I have a personal writing app that uses the OpenAI models and this post is bang on. One of my learnings related to "Lesson 1: When it comes to prompts, less is more": I was trying to build an intelligent search feature for my notes and asking ChatGPT to return structured JSON data. For example, I wanted to ask "give me all my notes that mention Haskell in the last 2 years that are marked as draft", and let Chat GPT f…

This seems like something that would be better suited by a database and good search filters rather than an LLM...

I setup a search engine to feed to a rag setup a while back. At the end of the day, I took out the LLM and just used the search engine. That was where the value turned out to be.

Re: Lessons after a Half-billion GPT Tokens

#153

Post by wavemode »

visarga wrote: post #140
fnordpiglet wrote: post #120

Earlier quoted context omitted.

There’s a difference between certainty of the next token given the context and the model evaluation so far and certainty about an abstract reasoning process being correct given it’s not reasoning at all. These probabilities and stuff coming out are more about token prediction than “knowing” or “certainty” and are often confusing to people in assuming they’re more powerful than they are.

> given it’s not reasoning at all When you train a model on data made by humans, then it learns to imitate but is ungrounded. After you train the model with interactivity, it can learn from the consequences of its outputs. This grounding by feedback constitutes a new learning signal that does not simply copy humans, and is a necessary ingredient for pattern matching to become reasoning. Everything we know as humans c…

Yeah but this doesn't change how the model functions, this is just turning reasoning into training data by example. It's not learning how to reason - it's just learning how to pretend to reason, about a gradually wider and wider variety of topics.

If any LLM appears to be reasoning, that is evidence not of the intelligence of the model, but rather the lack of creativity of the question.

Re: Lessons after a Half-billion GPT Tokens

#154

Post by airstrike »

thisgoesnowhere wrote: post #11

The team I work on processes 5B+ tokens a month (and growing) and I'm the EM overseeing that. Here are my take aways 1. There are way too many premature abstractions. Langchain, as one of may examples, might be useful in the future but at the end of the day prompts are just a API call and it's easier to write standard code that treats LLM calls as a flaky API call rather than as a special thing. 2. Hallucinations are…

> Summarizing is pretty rock solid in my testing

Yet, for some reason, ChatGPT is still pretty bad at generating titles for chats, and I didn't have better luck with the API even after trying to engineer the right prompt for quite a while...

For some odd reason, once in a while I get things in different languages. It's funny when it's in a language I can speak, but I recently got "Relm4 App Yenileştirme Titizliği" which ChatGPT tells me means "Relm4 App Renewal Thoroughness" when I actually was asking it to adapt a snippet of gtk-rs code to relm4, so not particularly helpful

Re: Lessons after a Half-billion GPT Tokens

#157

Post by saaaaaam »

amelius wrote: post #156

This reads a bit like: I have a circus monkey. If I do such and such it will not do anything. But when I do this and that, then it will ride the bicycle. Most of the time.

I don’t really understand your comment.

Personally I thought this was an interesting read - and more interesting because it didn’t contain any massive “WE DID THIS AND IT CHANGED PUR LIVES!!!” style revelations.

It is discursive, thoughtful and not overwritten. I find this kind of content valuable and somewhat rare.

Re: Lessons after a Half-billion GPT Tokens

#158

Post by jbeninger »

simonw wrote: post #75
satisfice wrote: post #49

I keep seeing this pattern in articles like this: 1. A recitation of terrible problems 2. A declaration of general satisfaction. Clearly and obviously, ChatGPT is an unreliable toy. The author seems pleased with it. As an engineer, I find that unacceptable.

Working with models like GPT-4 is frustrating from a traditional software engineering perspective because these systems are inherently unreliable and non-deterministic, which differs from most software tools that we use. That doesn't mean they can't be incredibly useful - but it does mean you have to approach them in a bit of a different way, and design software around them that takes their unreliability into account…

Unreliable? Non-deterministic? Hidden variables? Undocumented behaviour? C'mon fellow programmers who got their start in the Win-95 era! It's our time to shine!

Re: Lessons after a Half-billion GPT Tokens

#159

Post by saaaaaam »

spunker540 wrote: post #115
weatherlite wrote: post #91

Earlier quoted context omitted.

> Similarly, certain ML tasks are now easy to implement even for dumb dumb web devs like me For example?

Lots of applied NLP tasks used to require paying annotators to compile a golden dataset and then train an efficient model on the dataset. Now, if cost is little concern you can use zero shot prompting on an inefficient model. If cost is a concern, you can use GPT4 to create your golden dataset way faster and cheaper than human annotations, and then train your more efficient model. Some example NLP tasks could be clas…

I’m interested by your comment that you can “use GPT4 to create your golden dataset”.

Would you be willing to expand a little and give a brief example please? It would be really helpful for me to understand this a little better!

Re: Lessons after a Half-billion GPT Tokens

#160

Post by gmd63 »

CuriouslyC wrote: post #90
Xenoamorphous wrote: post #63

> We always extract json. We don’t need JSON mode I wonder why? It seems to work pretty well for me. > Lesson 4: GPT is really bad at producing the null hypothesis Tell me about it! Just yesterday I was testing a prompt around text modification rules that ended with “If none of the rules apply to the text, return the original text without any changes”. Do you know ChatGPT’s response to a text where none of the rules…

You know all the stories about the capricious djinn that grants cursed wishes based on the literal wording? That's what we have. Those of us who've been prompting models in image space for years now have gotten a handle on this but for people who got in because of LLMs, it can be a bit of a surprise. One fun anecdote, a while back I was making an image of three women drinking wine in a fancy garden for a tarot card,…

Programming is already the capricious djinn, only it's completely upfront as to how literally it interprets your commands. The guise of AI being able to infer your actual intent, which is impossible to do accurately, even for humans, is distracting tech folks from one of the main blessings of programming: forcing people to think before they speak and hone their intention.
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