5 ms·
There are so many applications for LLMs where having a perfect score is much more important than speed, because getting it wrong is so expensive, damaging, or t
by retrac98 3y ago
There are so many applications for LLMs where having a perfect score is much more important than speed, because getting it wrong is so expensive, damaging, or time consuming to resolve for an organisation.
- malux85 3y agoYeah I agree - just an hour ago I was dealing with an LLM that was missing a "not" thus inverting the meaning of a rather important simulation parameter!
- bberrry 3y agoIf you have speed you can generate multiple answers and have another model pick the best one.
- Drakim 3y agoIf I ask an LLM a very complex and specific question 500 times, if it just doesn't know the facts you'll still get the wrong answer 500 times. That's understandable. The real problem is when the AI lies/hallucinates another answer with confidence instead of saying "I don't know".
- deleted 3y ago[deleted]
- helsinkiandrew 3y ago> If I ask an LLM a very complex and specific question 500 times, if it just doesn't know the facts you'll still get the wrong answer 500 times. Think the commenter meant use another model/LLM which could give a different answer, then let them vote on the result. Like "old fashioned AI" did with ensemble learning.
- simion314 3y agoThe problem is asking for facts, LLM are not a database so they know stuff but it is compressed so expect wrong facts, wrong names, dates, wrong anything. We will need an LLM as a front end then it will generate a query to fetch the facts from the internet or a database , then maybe format the facts for your consumption.
- ch_sm 3y agoThat‘s exactly it. It‘s ok for LLMs to not know everything, because they _should_ have a means to look up information. What are some projects where this obvious approach is implemented/tried?
- Jensson 3y agoBut then you need an LLM that can separate between grammar and facts. Current LLMs doesn't know the difference, that is the main source to these issues, these models treat facts like grammar and that worked well enough to excite people but probably wont get us to a good state.
- deleted 3y ago[deleted]
- samus 3y agoThis is called Retrieval Augmented Generation (RAG). The LLM driver recognizes a query, it gets send to a vector database or to an external system (could be another LLM...) and the answer is placed in the context. It's a common strategy to work around their limited context length, but it tends to be brittle. Look for survey papers.
- m348e912 3y agoThe weird problem is with LLM hallucinations is that it usually will acknowledge its mistake and correct itself if you call it out. My question is why can't LLMs included a sub-routine to check itself before answering. Simply asking itself something like "this answer may not be correct, are you sure you're right?"
- asimovfan 3y agoIts because theres no entity that is actually acknowledging anything. Its generating an answer to your prompt. You can gaslight it into anything being wrong or correct.
- ZitchDog 3y agoThe problem is that if you call it out, it will frequently change its answer, even if it was correct. LLMs currently lack chutzpa.
- Jensson 3y agoThat is a common bullshitting strategy, talk a lot of bullshit, and then backtrack and acknowledge you were wrong when people push back. That way they will think you know way more than you do. Many people will see thought that, but most will just think you are a humble expert who can acknowledge when you are wrong instead of you always acknowledging you are wrong even when you aren't. People have a really hard time catching such bullshitting from humans, which is why free form interviews doesn't work.
- Shrezzing 3y ago
- spaniard89277 3y agoI've tried to apply it to parsing HTML as this article into a pretty long pipeline. I'm using DeepInfra with Mistral 8x7B and I'm still unsure if I'm going to use for production. The problem I'm finding is that the time I wanted to save mantaining selectors and the like is time that I'm spending writing wrapper code and dealing with the mistakes it makes. Some are OK and can deal with them, others are pretty annoying because It's difficult to deal with them in a deterministic manner. I've also tried with GPT-4 but it's way more expensive, and despite what this guy got, it also makes mistakes. I don't really care about inference speed, but I do care about price and correctness.
- ogogmad 3y agoMight be a silly question, but if you want determinism in this, why don't you get the LLM to write the deterministic code, and use that instead? Interesting experiment, though! In fact, what about a hybrid of what you're doing now? Initially, you use an LLM to generate examples. And then from those examples, you use that same LLM to write deterministic code?
- Eisenstein 3y agoHave you tried swapping Mistral 8x7B with either command-r 34B, Qwen 1.5 70B, or miqu 70B? Those are all superior in my experience, though suited for slightly different tasks, so experimentation is needed.
- samus 3y agoParsing HTML and tagsoup is IMHO not the right application for LLMs since these are ultimately structured formats. LLM are for NLP tasks, like extracting meaning out of unstructured and ambiguous text. The computational cost of an LLM chewing through even moderately-sized document can be more efficiently spent on sophisticated parser technologies that have been around for decades, which can also to a degree deal with ambiguous and irregular grammars. LLMs should be able to help you write those.
- nathan_compton 3y agoIf you need a perfect score, don't use LLMs. This seems obvious to me, even given the state of the art LLMs. I am a heavy user of GPT4 and I wouldn't bet $1000 bucks on it being 100% reliable for any non-trivial task.
- retrac98 3y agoThey'll get better. Humans are far from perfect, and I have no doubt that LLMs will eventually outperform them for non-trivial tasks consistently.
- nathan_compton 3y agoMaybe so, but at this stage I wouldn't be betting a business model on it.
- Socnic 3y agoBusinesses do bet on imperfect and even criminal models all the time (way before LLMs existed)... they call it cost of doing business when they get it wrong or get caught.
- samus 3y agoThey already have superhuman image classification performance.
- pooper 3y agoI remember talking to a radiologist who said he was sure something like this was coming like ten years ago where instead of a radiologist looking at scans manually, a machine would go through a lot of images and flag some for manual review. We haven't even gotten there yet, have we?
- matheusd 3y agoMaybe it's a liability issue, not a competency issue.
- worldsayshi 3y agoIt makes much more sense to me to have the LLM infer the correct query for extracting data on the page. Much faster and reliable and it wouldn't really be a problem to have a human in the loop every now and then.
- onion2k 3y agoAll the places I see AI being applicable to my work don't require a perfect score, and a threshold is actually much more useful, especially where multiple factors come together to make evaluation to a single value hard.