7 ms·
Is GPT-4 a good data analyst? (2023)
- einpoklum 3y agoI was somewhat put off by the abstract: > LLMs... have demonstrated their powerful capabilities in ... context understanding, code generation, language generation, data storytelling, etc., LLMs have not demonstrated understanding (in fact, one could argue that they are fundamentally incapable of understanding); they have only AFAICT demonstrated the ability to generate boilerplate-ish code; "language generation" is too general a task to claim that LLMs have succeeded in; and as for data storytelling I don't know, but they can spin yarns. The problems is that those yarns are often divorced from reality; see: https://www.ibm.com/topics/ai-hallucinations https://www.ibm.com/topics/ai-hallucinations -------- Leafing through the paper, and specifically tables 6 and 7, I don't believe their conclusion, that "GPT-4 can perform comparable [sic] to a data analyst", is well-founded.
- mewpmewp2 3y agoI don't even understand what understanding exactly means, perhaps anyone who understands it, can enlighten me? Do I, myself understand? Stand under what exactly? What is that supposed to mean?
- ocbyc 3y agoTransformers are just pattern matching. So if you write "give me a list of dog names" it knows that "Spot" should be in that result set. Even though it doesn't really know what a dog is, a list is, or what a spot is.
- bongodongobob 3y agoI don't think that's true. They clearly group related things together and seem to be able to create concepts that aren't specifically in the training data. For example, it will figure out the different features of a face, eyes, nose, mouth even if you don't explicitly tell it what those are. Which is why they are so cool.
- mewpmewp2 3y agoThey are cool, but then you are also cool.
- zeusk 3y agoMost of that magic comes from embedding no? which is clustering things by their relation in some N-dimensional space
- bongodongobob 3y agoExactly. It figures that out on its own. That's what "understanding" looks like in this context, imo.
- rafaelero 3y ago> Transformers are just pattern matching. That's trivially true. The question is: are we any different?
- parpfish 3y agoI approach LLMs with the perspective that “maybe this demonstrates that we humans are all just stochastic parrots?”and we should have the null hypothesis that humans are just pattern matchers.
- mewpmewp2 3y agoThis is the way I perceive my thoughts. I don't know what I'm going to think of beforehand or in advance, these could all be stochastic "tokens" based on what I've observed in my life. So of course I feel a bit offended when people claim LLMs are just stochastic parrots, because it doesn't feel to me, that I'm specifically any better? My thoughts - they just happen, and sometimes not in my favor - I have had times of depression, I didn't have control over my thoughts. Neither do I have now, but at least I am in a better place. Because the "happiness" chemicals are regulated to be in a more favorable state to me for various different factors. I didn't know what I was going to comment in response to your comment, I was just streaming my conscious.
- richardw 3y agoI think so. You ask that question because you’re interrogating the position, not because 1000 humans have asked that question in similar situations. You and I know there’s a truth and we’d like to find it. The GPT is just happy (I.e. rewarded) to produce frequently used tokens.
- ALittleLight 3y agoCan you describe a test that would separate trivial pattern matching from true understanding?
- lottin 3y agoA simple conversation would do.
- mewpmewp2 3y agoCould you share a conversation link with GPT-4 with either about a "list" or a "dog", to determine whether it truly understands one of those things compared to a human?
- lottin 3y agoI don't have a GPT account. I would start with: "Do you like dogs?" Next question: "Why?"
- mewpmewp2 3y agoIt kind of answered "why" for me """I think dogs are wonderful! They're known for their loyalty, playfulness, and their ability to bring joy to people's lives. What about you? Do you have a favorite breed or dog story?""" What do I ask next?
- lottin 3y agoThis reply sounds so fake that in my opinion should be enough to rule out any hint of intelligence. However if you insist I'd continue with this: "I'm not a fan of dogs. I do know a few dogs though. Sometimes I invite my neighbour's dog for dinner. He's got good taste, for a dog. The last time he came around we talked about the situation in the Middle East. Do you know a good book about this topic that I could recommend to him?"
- bongodongobob 3y ago
- mewpmewp2 3y agoHow would I test whether I "know" or "understand" what a dog is?
- notahacker 3y agoOh, that's easy, we just give the dog a keyboard and see if you accurately identify it's a dog from your text based interactions ;-)
- mewpmewp2 3y agoAre you calling me a dog?
- inopinatus 3y agoEven this seems too grand a claim. I’d water it down thus: the LLM encodes that the token(s) for “Spot” are probabilistically plausible in the ensuing output.
- bongodongobob 3y ago...because it understands what a dog name is. Why wouldn't you see Gary or Florence in that list? How does it know those aren't dog names? You can't be suggesting it has memorized relationships between all concepts, the model would be enormous. So clearly, there is something else going on. It's able to encode concepts/ideas.
- inopinatus 3y agoThe model is enormous, and N-dimensional for very high N. But the model remains insufficiently enormous for understanding, and moreover, the model cannot observe itself and adjust. Ask an LLM to extrapolate, see any semblance of reason collapse.
- mewpmewp2 3y agoExtrapolate what?
- advael 3y agoSolipsism is truly the best fully-general counterargument
- mewpmewp2 3y agoTo AI? Or that you are not a NPC?
- advael 3y agoTo anything, that's what "fully-general" means
- mewpmewp2 3y agoSo you are a bot?
- advael 3y agoI mean from your perspective I'm just a name making more words on your screen, right? Don't worry too much about it, buddy, you're doin' great :)
- mewpmewp2 3y agoHaha, you are funny! What's the weather tomorrow? Please also remind me tomorrow to put my gym clothes to washer and dry them after.
- advael 3y agoI can't spoil the weather tomorrow (it's a major plot point) but I can tell you that fortune has been tweaked to favor the bold by an additional 10%, just for tomorrow. Laundry service is complimentary, but our records show that you haven't registered your home. Would you like to register your home address at this time?
- richardw 3y agoThe fact that you ask that is in many ways the difference. You feel there’s a limitation in your knowledge of the term “understand” and its use in this context and would like clarification before you’re more certain, either way. At some point either enough information arrives to convince you, or you decide it’s not true. Whatever that process and internal states are, is something GPT can’t do. It’ll 100% confidently produce something and be fully rewarded that it chose tokens that humans would most likely choose given the preceding tokens. There’s no “aha”.
- mewpmewp2 3y agoBut it frustratingly, frequently tells me it doesn't have enough data or other XYZ reasons to why it can't answer my weird questions.
- modriano 3y agoTo understand means a few things, but they all essentially boil down to having a correct (or at least correct enough to be useful for your usecase(s)) model for something in your head. Have you told someone something like "no, don't do it that way because <insert non-obvious downstream problem>, instead, do <insert alternative strategy that achieves better outcomes>"? That's an artifact of understanding and of the model you've developed for that thing. It's well described as a mixture of knowledge and wisdom, and is essentially the property of knowing the effect that pulling a lever will cause, coupled with good judgement about how, when, and why to pull the lever.
- mewpmewp2 3y agoBut GPT-4 has told me that as well? Usually in a bit more polite way. That my approach is a "novel" and an "interesting" approach, hinting that it's really probably not the best option here.
- unclebucknasty 3y agoAgreed, right down to their conclusion resonating as way overstated. Actually, meaningless would be more accurate. The thing about LLMs is exactly that they don't understand by design. It often feels very distinctly like it's just engaging in sophisticated wordplay. A parlor trick. When ChatGPT 4 first came out I spent a couple of hours putting together a chess game using ChatGPT as the engine. It was shockingly bad, as in even attempting to make invalid moves. I get it: it's not tuned for that purpose, and its chess training corpus could probably be expanded to improve it as well. But, it actually served as a near-perfect demonstration of its lack of understanding, as well as the confidence with which it asserts things that are simply wrong. On a recent integration project with a good bit of nuanced functionality, it led me astray multiple times. I've gotten to a point where I can feel when its answers are not quite right, particularly if I know just a little about the topic. And, when challenged, it does that strange thing of responding with something along the lines of, "My apologies you're completely right that I was completely wrong". Over time, there becomes a sense that there is no there there. Even it's writing capabilities, lauded by so many, are of a style that is superficial and perfunctory or rote. That makes sense when you know what it is, but that's the thing: we get articles like these, lauding its wisdom.
- bongodongobob 3y agoIdk. One of my first tests for GPT4 was writing a website "for snakes." It was a flask app, and it did all the obvious things you'd expect. There was a title that said "Snake.com - A website for snakes" and a bunch of silly marketing stuff. What impressed me is when I asked to make it more snake-like (what does that even mean right?). It changed the colors to shades of green, used italic fonts, added some hisssssing sssstuff to wordssss, and added a diamond pattern through the background. It was a dumb and not very fancy site, but I'm not sure you can say it doesn't understand anything at all when you ask it to make a website more snakelike and actually made a pretty good attempt at doing it.
- unclebucknasty 3y agoYeah, that's kind of a different conception of understanding though. The lines do get a little blurry at a certain point, and a lot of what it does "feels" like understanding, especially given how it "communicates". But I think it comes down to whether it can reason about things and whether it can draw new conclusions or create new information as a result. Your snake site is probably a good example. ChatGPT has a bunch of words that it knows are associated with snakes. It's pretty straightforward pattern matching. It doesn't really "understand" what those words mean, except that they have relationships to other words. But, if you were to ask it to reason and draw new conclusions about these things beyond its training corpus, it would be unable to reliably do so. Similarly, it had no idea about the quality (and sometimes legality) of the chess moves it generated.
- kva 3y agoGiven the right prompt, I'm sure it is....but when do users ever enter the right prompt? :(
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- viscanti 3y agoOpenAI should make something so that people can enter their prompt and maybe even drop in a knowledge base and then share with anyone else who wants that functionality.
- snoman 3y agoThat’s ptetty close to what GPTs are, with the exception of knowledge bases. There’s more to it, but the tooling to create a GPT is basically a hand-holding mechanism to create a prompt.
- gregorymichael 3y agoGPTs have the knowledge base too. (Mixed results though)
- wolpoli 3y agoWould the final product be similar to Github copilot, but for prompt?
- williamcotton 3y agoDidn’t you get the memo? If you’re holding the hammer by the head and wondering why it isn’t driving the nail in that it is clearly the fault of the manufacturer. There’s even a handy aphorism to remind you that the user is never to blame: “You’re holding it wrong.” Jokes aside, I wonder what the general writing abilities and communication skills are for people that cannot for the life of them get usable results from an LLM.
- richardw 3y ago
- greenavocado 3y agoEven the latest commercial LLMs are happy to confidently bullshit about what they think is in published research even if they provide citations. Often the citations themselves are slightly corrupted. I actually verify each LLM claim so I know this is happening a lot. Occasionally they are complete fabrications. It really varies by research topic. Its really bad in esoteric research areas. They even acknowledge the paper was actually about something else if you call them out on it. What a disaster. LLMs are still useful for information retrieval and exploration as long as you understand you are having a conversation with a habitual liar / expert beginner and adjust your prompts and expectations accordingly.
- bongodongobob 3y agoUnintuitively, I think you'll probably end up with better answers if you don't ask for citations. The vast majority of its training isn't white papers so you're artificially constraining its "imagination" to the cited sources space. I find the more constraints you add, the worse your answers are.
- notnullorvoid 3y ago> They even acknowledge the paper was actually about something else if you call them out on it. For clarity is not really acknowledging it made a mistake. "Calling out" an LLM's mistake just leads to the next most likely text to be something that sounds like an acknowledgement of a mistake, but the same is likely to happen if the LLM generated a correct response and you respond claiming that it's incorrect.
- greenavocado 3y agoThe LLM generally correctly identifies the topics in the paper after calling out the original mistake.
- jiggawatts 3y ago> What a disaster. Using tool inappropriately leads to suboptimal outcomes -- news at 11. A good mental model is that an LLM is a blurry JPEG of the Internet. You sound like a scientist, right? You reference "published research", after all. What would your opinion be of a researcher measuring the exact values of the pixels of a JPEG image instead of the RAW sensor data?
- andy99 3y agoMay 2023 using GPT-4-0314.
- mritchie712 3y agoreminds me of this tweet [0] Them: Can you just quickly pull this data for me? Me: Sure, let me just: SELECT * FROM some_ideal_clean_and_pristine.table_that_you_think_exists GPT-4 is good on a single CSV, but breaks down quickly applied to a real database / data warehouse. I know they're using multiple tables in the paper, but it appears to be a pristine schema that's very easy to reason about. In the real world, when you're trying to join postgres to hubspot and stripe data, an LLM isn't able to write the SQL from scratch get the right answer. We're working on an approach using a semantic layer at https://www.definite.app/ https://www.definite.app/ if you're interested in this sort of thing. 0 - https://twitter.com/sethrosen/status/1252291581320757249?lang=en https://twitter.com/sethrosen/status/1252291581320757249?lan...
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- neeleshs 3y agoIt goes beyond just joining postgres to hubspot and stripe even when humans are doing it. Typos in source systems, duplicative data, unwarranted prefixes, suffixes, stuff you don't care about, columns named c0,c1,c2 etc. A semantic layer is just really all about defining data models in the domain of interest. It's the hardest part in dealing with data strategies, very manual, very company and process and history specific. Once it's defined, the next set of tasks is to make sure that the data in the model is correct and coherent. And only then, querying this data, applying ML etc start becoming worthwhile. We at https://syncari.com https://syncari.com take the centralized data model centric approach. https://www.definite.app/ https://www.definite.app/ also looks very cool!
- dangoodmanUT 3y agoNot on useful datasets in real places
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- scottlepp 3y agoI think some of these dataset examples are useful: https://youtube.com/playlist?list=PLfRs4l8INQS44y62Z2aFzPzHH7rh8xE8d&si=AHXW_4WpLsjzEbxc https://youtube.com/playlist?list=PLfRs4l8INQS44y62Z2aFzPzHH...
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- cstanley 3y agoThis paper was published 154 days ago, probably a year since the authors did the experiment. Sooo much has happened since then! This showed already that GPT4 is pretty darn good analyst. All this real-world complexity can be tamed by stuffing the prompt with a ton of relevant context and an amazing prompt engine. We'll have bots that autonomously query the database hundreds of times building a 5 page "deep-dive" analytics report in minutes. At least that's what we're trying at patterns.app.
- elietoubi 3y agoIf anyone is interested i built for myself and open sourced parse.dev https://github.com/ParseDev/parsedev https://github.com/ParseDev/parsedev
- lutusp 3y ago> However, we are still at a stage of divergent opinions without any definitive conclusion. Okay, I know picking people's sentences apart has fallen out of fashion, but: "Divergent opinions" are ... opinions. A "definitive conclusion" is ... a conclusion. I see more examples, but I wanted to make a point: I miss the days when fewer words conveyed more meaning. From the classic https://en.wikipedia.org/wiki/The_Elements_of_Style https://en.wikipedia.org/wiki/The_Elements_of_Style: "Make every word count." About brevity of expression, I must add this (possibly apocryphal) story about Ernest Hemingway. In the 1920s Hemingway and his Paris friends had a contest: who could write the shortest readable short story? Hemingway won with this entry: For sale. Baby shoes. Never worn.
- apineda 3y agoFrom an alternative viewpoint, this could be seen as descriptive of a community. Opinions could be shared or not, hence "divergent" as an adjective to describe the community. Similar with conclusions people have drawn and "definitive" may refer to a larger consensus among the community. Fun to think about.
- 93po 3y agoThe author uses "divergent opinions" to provide balance, showing both skeptics and supporters of LLMs in data analysis. Similarly, the notion of a definitive conclusion highlights that while conclusions may exist, they might lack solid foundations. Also just for you I copied and pasted my original comment into ChatGPT to make it more brief ;) as an aside, asking chatGPT 3.5 to make similar stories to "For sale. Baby shoes. Never worn." had very bad results and completely failed to do a single meaningful example of "common item with uncommon description that tells a story"
- nicklecompte 3y agoI am glad studies like this exist but "we subjected human data analysts to 100 trite data analysis problems with unambiguous solutions, without any surrounding context or personal/professional incentive" is going to overestimate the relative efficacy of LLMs. Certainly one advantage they have over humans is that they don't get bored with SQL 101 questions.
- leptronix 3y agoI was pretty impressed with GPT-3.5 Turbo. Some examples below. We just started with some simple examples/questions. Then got a bit more complicated. But the result was pretty spot on the first time. Simple 1 table dataset: https://youtu.be/vM0NAIROTD4 https://youtu.be/vM0NAIROTD4 Analyzing Log Data: https://www.youtube.com/watch?v=5hbanOZjHm4 https://www.youtube.com/watch?v=5hbanOZjHm4 Joining 2 datasets: https://youtu.be/VUc0flsC4pg https://youtu.be/VUc0flsC4pg Simple Filtering example: https://youtu.be/wTiaq2J-IdY https://youtu.be/wTiaq2J-IdY Analyzing Ride Share data: https://youtu.be/HWZUHxT7Vx8 https://youtu.be/HWZUHxT7Vx8