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.
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Lessons after a Half-billion GPT Tokens
161–170 of 179 posts
Re: Lessons after a Half-billion GPT Tokens
#162Anyone any good tips for stopping it sounding like it's writing essay answers, and flat out banning "in the realm of", delve, pivotal, multifaceted, etc? I don't want a crap intro or waffley summary but it just can't help itself.
My approach is to use words that indicate what I want like 'concise', 'brief', etc. If you know a word that precisely describes your desired type of content then use that. It's similar to art generation models, a single word brings so much contextual baggage with it. Finding the right words helps a lot. You can even ask the LLMs for assistance in finding the words to capture your intent.
As an example of contextual baggage, I wrote a tool where I had to adjust the prompt between Claude and GPT-4 because using the word "website" in the prompt caused GPT-4 (API) to go into its 'I do not have access to the internet' tirade about 30% of the time. The tool was a summary of web pages experiment. By removing 'website' and replacing it with 'content' (e.g. 'summarize the following content') GPT-4 happily complied 100% of the time.
Re: Lessons after a Half-billion GPT Tokens
#163The 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…
> Similarly, certain ML tasks are now easy to implement even for dumb dumb web devs like me For example?
Anything involving classification, extraction, or synthesis.
Re: Lessons after a Half-billion GPT Tokens
#164The 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…
> 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
They are also dull (higher latency for same resources) APIs if you're self-hosting LLM. Special attention needed to plan the capacity.
Re: Lessons after a Half-billion GPT Tokens
#165Earlier quoted context omitted.
> get it to say when it's not sure This is a function of the language model itself. By the time you get to the output, the uncertainty that is inherent in the computation is lost to the prediction. It is like if you ask me to guess heads or tails, and I guess heads, I could have stated my uncertainty (e.g. Pr [H] = .5) before hand, but in my actual prediction of heads, and then the coin flip, that uncertainty is lost…
I get the reasoning but I’m not sure you’ve successfully contradicted the point. Most prompts are written in the form “you are a helpful assistant, you will do X, you will not do Y” I believe that inclusion of instructions like “if there are possible answers that differ and contradict, state that and estimate the probability of each” would help knowledgeable users. But for typical users and PR purposes, it would be d…
Why does the prompt intro take the form of a role/identity directive "You are helpful assistant..."?
What about the training sets or the model internals responds to this directive?
What are the degrees of freedom of such directives?
If such a directive is helpful, why wouldn't more demanding directives be even more helpful: "You are a domain X expert who provides proven solutions for problem type Y..."
If don't think the latter prompt is more helpful, why not?
What aspect of the former prompt is within bounds of helpful directives that the latter is not?
Are training sets structured in the form of roles? Surely, the model doesn't identify with a role?!
Why is the role directive topically used with NLP but not image generation?
Do typical prompts for Stable Diffusion start with an identity directive "You are assistant to Andy Warhol in his industrial phase..."?
Why can't improved prompt directives be generated by the model itself? Has no one bothered to ask it for help?
"You are the world's most talented prompt bro, write a prompt for sentience..."
If the first directive observed in this post is useful and this last directive is absurd, what distinguishes them?
Surely there's no shortage of expert prompt training data.
BTW, how much training data is enough to permit effective responses in a domain?
Can a properly trained model answer this question? Can it become better if you direct it to be better?
Why can't the models rectify their own hallucinations?
To be more derogatory: what distinguishes a hallucination from any other model output within the operational domain of the model?
Why are hallucinations regarded as anything other than a pure effect, and as pure effect, what is the cusp of hallucination? That a human finds the output nonsensical?
If outputs are not equally valid in the LLM why can't it sort for validity?
OTOH if all outputs are equally valid in the LLM, then outputs must be regarded by a human for validity, so what distinguishes a LLM from an the world's greatest human time-wasting device? (After Las Vegas)
Why will a statistical confidence level help avoid having a human review every output?
The questions go on and on...
— Parole Board chairman: They've got a name for people like you H.I. That name is called "recidivism."
Parole Board member: Repeat offender!
Parole Board chairman: Not a pretty name, is it H.I.?
H.I.: No, sir. That's one bonehead name, but that ain't me any more.
Parole Board chairman: You're not just telling us what we want to hear?
H.I.: No, sir, no way.
Parole Board member: 'Cause we just want to hear the truth.
H.I.: Well, then I guess I am telling you what you want to hear.
Parole Board chairman: Boy, didn't we just tell you not to do that?
H.I.: Yes, sir.
Parole Board chairman: Okay, then.
Re: Lessons after a Half-billion GPT Tokens
#166Earlier quoted context omitted.
> get it to say when it's not sure This is a function of the language model itself. By the time you get to the output, the uncertainty that is inherent in the computation is lost to the prediction. It is like if you ask me to guess heads or tails, and I guess heads, I could have stated my uncertainty (e.g. Pr [H] = .5) before hand, but in my actual prediction of heads, and then the coin flip, that uncertainty is lost…
> so unless the prediction itself is uncertainty (which it should rarely be based on the training corpus, I think) Why shouldn't you ask for uncertainaty? I love asking for scores / probabilities (usually give a range, like 0.0 to 1.0) whenever I ask for a list, and it makes the output much more usable
I'm not sure if that is a metric you can rely on. LLMs are very sensitive to the position of your item lists along the context, paying extra attention at the beginning and the end of those list.
See the listwise approach at "Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting", https://arxiv.org/abs/2306.17563
Re: Lessons after a Half-billion GPT Tokens
#167Earlier quoted context omitted.
Interesting, Claude 3 Opus has been better than GPT4 for me. Mostly in that I find it does a better (and more importantly, more thorough) job of explaining things to me. For coding tasks (I'm not asking it to write code, but instead to explain topics/code/etc to me) I've found it tends to give much more nuanced answers. When I give it long text to converse about, I find Claude Opus tends to have a much deeper underst…
How much of this is just that one model responds better to the way you write prompts? Much like you working with Bob and opining that Bob is great, and me saying that I find Jack easier to work with.
It is because "coding tasks" is a huge array of various tasks.
We are basically not precise enough with our language to have any meaningful conversation on this subject.
Just misunderstandings and nonsense chatter for entertainment.
Re: Lessons after a Half-billion GPT Tokens
#168The 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…
> get it to say when it's not sure This is a function of the language model itself. By the time you get to the output, the uncertainty that is inherent in the computation is lost to the prediction. It is like if you ask me to guess heads or tails, and I guess heads, I could have stated my uncertainty (e.g. Pr [H] = .5) before hand, but in my actual prediction of heads, and then the coin flip, that uncertainty is lost…
It's not just loss of the uncertainty in prediction, it's also that an LLM has zero insight into its own mental processes as a separate entity from its training data and the text it's ingested. If you ask it how sure it is, the response isn't based on its perception of its own confidence in the answer it just gave, it's based on how likely it is for an answer like that to be followed by a confident affirmation in its training data.
Re: Lessons after a Half-billion GPT Tokens
#169So these guys are just dumping confidential tax documents onto OpenAI's servers huh.
Hopefully it won't end up as training data.
Re: Lessons after a Half-billion GPT Tokens
#170Earlier quoted context omitted.
> But all in all I think it's more of a force multiplier rather than a breakthrough like the internet. Thank you. Seeing similar things. Clients are also seeing sticker shock on how much the big models cost vs. the output. That will all come down over time.
> That will all come down over time. So will interest, as more and more people realise theres nothing "intelligent" about the technology, it's merely a Markov-chain-word-salad generator with some weights to improve the accuracy somewhat. I'm sure some people (other than AI investors) are getting some value out of it, but I've found it to be most unsuited to most of the tasks I've applied it to.
The industry is troubled both by hype marketers who believe LLMs are superhuman intelligence that will replace all jobs, and cynics who believe they are useless word predictors.
Some workloads are well-suited to LLMs. Roughly 60% of applications are for knowledge management and summarization tasks, which is a big problem for large organizations. I have experience deploying these for customers in a niche vertical, and they work quite well. I do not believe they're yet effective for 'agentic' behavior or anything using advanced reasoning. I don't know if they will be in the near future. But as a smart, fast librarian, they're great.
A related area is tier one customer service. We are beginning to see evidence that well-designed applications (emphasis on well-designed -- the LLM is just a component) can significantly bring down customer service costs. Most customer service requests do not require complex reasoning. They just need to find answers to a set of questions that are repeatedly asked, because the majority of service calls are from people who do not read docs. People who read documentation make fewer calls. In most cases around 60-70% of customer service requests are well-suited to automating with a well-designed LLM-enabled agent. The rest should be handled by humans.
If the task does not require advanced reasoning and mostly involves processing existing information, LLMs can be a good fit. This actually represents a lot of work.
But many tech people are skeptical, because they don't actually get much exposure to this type of work. They read the docs before calling service, are good at searching for things, and excel at using computers as tools. And so, to them, it's mystifying why LLMs could still be so valuable.