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GPT-4 LLM simulates people well enough to replicate social science experiments
- deleted 2y ago[deleted]
- thoughtpeddler 2y agoAccompanying working paper that demonstrates 85% accuracy of GPT-4 in replicating 70 social science experiment results: https://docsend.com/view/qeeccuggec56k9hd https://docsend.com/view/qeeccuggec56k9hd
- Jensson 2y agoDo you even get 85% replication rate with humans in social science? Doesn't seem right. But at least it can give them hints of where to look, but going that way is very dangerous as it gives LLM operators power to shape social science.
- TeaBrain 2y agoThe study isn't trying to do replication, but seems to have tested the rate that GPT-4 predicts human responses to survey studies. After reading the study, the writers really were not clear on how they were feeding the studies they were attempting to predict the responses to into the LLM. The data they used for training also was not clear, as they only dedicated a few lines referring to this. For 18 pages, there was barely any detail on the methods employed. I also don't believe the use of the word "replication" makes any sense here.
- nullc 2y agoBut do the experiments replicate better in LLMs than in actual humans? :D We should expect LLMs to be pretty good at repeating back to us the stories we tell about ourselves.
- pedalpete 2y agoI wonder if this could be used for testing marketing or UX actions?
- vekntksijdhric 2y agoSame energy as https://mastodon.radio/@wa7iut/112923475679116690 https://mastodon.radio/@wa7iut/112923475679116690
- dantyti 2y agowhy not just link directly? https://existentialcomics.com/comic/557 https://existentialcomics.com/comic/557
- BoredPositron 2y agoEveryone and their mom in advertising sold "GPT Persona" tools which are basically just an api call to brands for target group simulation. Think "Chat with your target group" kinda stuff. Hint: They like it because it's biased for what they want... like real marketing studies.
- markovs_gun 2y agoYeah anyone who has used ChatGPT for more than 30 minutes of asking it to write poetry about King Charles rapping with Tupac and other goofy stuff has realized that it is essentially trained to assume that whatever you're saying to it is true and to not say anything negative to you. It can't write stories without happy endings, and it can't recognize when you ask it a question that contains a false premise. In marketing, I assume if you ask a fake target demographic if it will like your new product that is pogs but with blockchain technology, it will pretty much always say yes
- Zambyte 2y ago[flagged]
- cj 2y agoI’ve noticed this in article summaries. It does seem to have some weird biases. I’ve been able to get around that by directly asking it for pros/cons or “what are the downsides” or “identify any inconsistencies” or “where are the main risks”… etc There’s also a complexity threshold where it performs much better if you break a simple question down into multiple parts. You can basically to prompt-based transformations of your own input to break down information and analyze it in different ways prior to using all of that information to finally answer a higher level question. I wish ChatGPT could do this behind the scenes. Prompt itself “what questions should I ask myself that would help me answer this question?” And go through all those steps without exposing it to the user. Or maybe it can or already does, but it still seems like I get significantly better results when I do it manually and walk ChatGPT through the thought process myself.
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- TeaBrain 2y agoI don't think "replicate" is the appropriate word here.
- dartos 2y agoWere those experiments in the training set? If so, how close was the examination vs the record the model was trained on. Some interesting insights there, I think.
- masterofpupp3ts 2y agoThe answers to your questions are in the paper linked in the first line of the app
- cpeterso 2y ago> Accuracy remained high for unpublished studies that could not appear in the model's training data (r = 0.90).
- croes 2y agoIs that the solution to social science's replication problem?
- nomel 2y agoWith the temperature parameter effectively set to 0, it may finally be possible!
- xp84 2y agoCan someone translate for us non-social-scientists in the audience what this means? "3. Treatment. Write a message or vignette exactly as it would appear in a survey experiment." Probably would be sufficient to just give a couple examples of what might constitute one of these. Sorry, I know this is probably basic to someone who is in that field.
- X0nic 2y agoSame for me. I had no idea what was being asked.
- LogicalRisk 2y agoA treatment might look like "In the US, XXX are much more likely to be unemployed than are YYY. The unemployment rate is defined as the percentage of jobless people who have actively sought work in the previous four weeks. According to the U.S. Bureau of Labor Statistics, the average unemployment rate for XXX in 2016 was five times higher than the unemployment rate for YYY" "How much of this difference do you think is due to discrimination?" In this case you'd fill in XXX and YYY with different values and show those treatments to your participants based on your treatment assignment scheme.
- tylerrobinson 2y agoA YC company called Roundtable tried to do this.[1] The comments were not terribly supportive. They’ve since pivoted to a product that does survey data cleaning. [1] https://news.ycombinator.com/item?id=36865625 https://news.ycombinator.com/item?id=36865625
- AnthonBerg 2y agoA social science experiment in and of itself. A fine thread of tragedy in the rich tapestry of enterprise.
- janalsncm 2y agoProducts like this make me pretty cynical about VCs’ ability to evaluate novel technical products. Any ML engineer who spent 5 minutes understanding would have rejected the pitch.
- rytill 2y agoI’m an ML engineer who’s spent more than 5 minutes thinking about this idea and would not have automatically rejected the pitch.
- janalsncm 2y agoThere are so many basic questions raised in the Launch HN thread that didn’t have good answers. It indicates to me that YC didn’t raise those questions, which is a red flag.
- dongobread 2y agoI'm very skeptical on this, the paper they linked is not convincing. It says that GPT-4 is correct at predicting the experiment outcome direction 69% of the time versus 66% of the time for human forecasters. But this is a silly benchmark because people are not trusting human forecasters in the first place, that's the whole purpose for why the experiment is run. Knowing that GPT-4 is slightly better at predicting experiments than some human guessing doesn't make it a useful substitute for the actual experiment.
- SrslyJosh 2y ago[flagged]
- fl0id 2y agoThis so much. There was another similar one recently which was also BS.
- yas_hmaheshwari 2y agoNicely put! Well argued! I was not able to put my finger on what I felt wrong about the article -- till I read this
- authorfly 2y agoI totally agree. So many people are missing the point here. Also important is that in Psychology/Sociology, it's the counter-intuitive results that get published. But these results disproportionately fail to replicate! Nobody cares if you confirm something obvious, unless it's on something divisive (e.g. sexual behavior, politics), or there is an agenda (dieting, etc). So people can predict those ones more easily than predicting a randomly generated premise. The ones that made their way into the prediction set were the ones researchers expected to be counter-intuitive (and likely P-hacked a significant proportion of them to find that result). People know this (there are more positive confirming papers than negative/fail-to-replicate). This means the counter-intuitive, negatively forecast results, are the ones that get published i.e. the dataset saying that 66% of human forecasters is disproportionately constructed of studies that found counter-intuitive results compared to the overall neutral pre-published set of studies, because scientists and grant winners are incentivised to publish counter-intuitive work. I would even suggest the selected studies are more tantalizing that average in most of these studies, they are key findings, rather than the miniature of comments on methods or re-analysis. By the way the 66% result has not held up super well in other research, for example, only 58% could predict if papers would replicate later on: https://www.bps.org.uk/research-digest/want-know-whether-psychology-study-will-replicate-just-ask-bunch-people https://www.bps.org.uk/research-digest/want-know-whether-psy... - Results with random people show that they are better than chance for psychology, but on average by less than 66% and with massive variance. This figure doesn't differ from psychology professors which should tell you the stat represents more the context of the field and it's research apparatus itself rather than capability to predict research. What if we revisit this GPT-4 paper in 20 years, see which have replicated, ask people to predict that - will GPT-4 still be higher if it's data is frozen today? If it is up to date? Will people hit 66%, 58%, or 50%? My point is, predicting the results now is not that useful because historically, up to "most" of the results have been wrong anyhow. Predicting which results will be true and remain true would be more useful. The article tries to dismiss the issue of the replication crisis by avoiding it, and by using pre-registered studies, but such tools are only bandages. Studies still get cancelled, or never proposed after internal experimentation, we don't have a "replication reputation meter" to measure those (which affect and increase false positive results), and we likely never will, with this model of science for psychology/sociology statistics. If the authors read my comment and disagree, they should use predictions for underway replications with GPT-4 and humans, wait a few years for the results, and then conduct analysis. Also, more to the point, as a Psychology grant funded once told me - the way to get a grant in Psychology is to: 1) Acquire a result with a counter-intuitive result first. Quick'n'dirty research method like students filling in forms, small sample size, not even published, whatever. Just make the story good for this one and get some preliminary numbers on some topic by casting a big web of many questions (a few will get P < 0.05 by chance eventually in most topics anyway at this sample size) 2) Find an angle whereby said result says something about culture or development (e.g. "The Marhsmallow experiment shows that poverty is already determined by your response to tradeoffs at a young age", or better still "The Marshmallow experiment is rubbish because it's actually entirely explained by SES as a third factor, and wealth disparity in the first place is ergo the cause". Importantly, change the research method to something more "proper" and instead apply P-hacking if possible when you actually carry out the research. The biggest P-hack is so simple and obvious nobody cares: you drop results that contradict or are insignificant, and just don't report them - carrying out alternate analysis, collecting slightly different data, switching from online to in person experiments, whatever you canto get a result. 3) Upon the premise of further tantalizing results, propose several studies which can fund you over 5 years, apply some of the buzz words of the day. Instead of "Thematic Analysis", It's "AI Summative Assessment" for the Word Frequency amounts, etc. If you know the grant judgers, avoid contradicting whatever they say, but be just outside of the dogma enough (usually, culturally) to represent movement/progress of "science". This is how 99% of research works. The grant holder directs the other researchers. When directing them to carry out an alternate version of the experiment or change what we are analyzing, you motivate them that it's for the good of the future, society, being at the cutting edge, and supporting the overarching theory (which ofcourse, already has "hundreds" of supporting evidence from other studies constructed in the same fashion). As to sociology/psychology experiments - Do social experiments represent language and culture more than people and groups? Randomly. Do they represent what would be counter-intuitive or support developing and entrenching models and agendas? Yes. 90% of social science studies have insufficient data to say anything at P < 0.01 level which should realistically be our goal if we even want to do statistics with the current dogma for this field (said kindly because some large datasets are genuine enough and used for several studies to make up the numbers in the 10%). I strongly see a revolution in psychology/sociology within the next 50 years to redefine a new basis.
- dzwa 2y ago[flagged]
- itkovian_ 2y agoPhsycohistory
- scudsworth 2y agogarbage in, eh?
- AdieuToLogic 2y agoSo did ELIZA[0] about sixty (60) years ago. 0 - https://en.wikipedia.org/wiki/ELIZA https://en.wikipedia.org/wiki/ELIZA
- deleted 2y ago[deleted]
- uptownfunk 2y agoIs it possible to train an LLM that is minimally biased and that could assume various personas for the purpose of the experiments? Then I imagine it’s just some prompt engineering no?
- nsonha 2y agoplease don't, need I remind you the joke that social science is not real science
- visarga 2y agoReminds me of: > Out of One, Many: Using Language Models to Simulate Human Samples > We propose and explore the possibility that language models can be studied as effective proxies for specific human sub populations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the "algorithmic bias" within one such tool -- the GPT 3 language model -- is instead both fine grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this property "algorithmic fidelity" and explore its extent in GPT-3. We create "silicon samples" by conditioning the model on thousands of socio demographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT 3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and socio cultural context that characterize human attitudes. We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines. https://arxiv.org/abs/2209.06899 https://arxiv.org/abs/2209.06899
- anileated 2y agoIf GPT emulations of social experiments are not correct, policy decisions based on them will make them so. “GPT said people would hate buses, so we halved their number and slashed transportation budget… Wow, do our people actually hate buses with passion!” “A year ago GPT said people would not be worried about climate change, so we stopped giving it coverage and removed related social adverts and initiatives. People really don’t give a flying duck about climate change it turns out, GPT was so right!” This is an oversimplification, of course; to say it with more nuance, anything socio- and psycho- is a minefield of self-fulfilling prophecies that ML seems to be nicely positioned to wreak havoc in. (But the small “this is not a replacement for human experiment” notice is going to be heeded by all, right?) As someone wrote once, all you need for machine dictatorship is an LLM and a critical number of human accomplices. No need for superintelligence or robots.
- hsyehbeidhh 2y ago[dead]
- crngefest 2y agoAll you need for dictatorship in general is a critical number of human accomplices. I don’t see how an LLM in the mix would make it worse. IMO mass communication technologies (radio, TV, internet) are much more important in building a dictatorship.
- anileated 2y agoThe quote was mostly a flourish (and apparently too open to interpretation to be useful). In any case, it is about hypothetical “machine dictatorship” in particular, not human dictatorships you describe. Machine dictatorship traditionally invokes an image of “AGI” and violent robots forcing or eliminating humans with raw power and compute capabilities, and thus with no substantial need for accomplices (us vs. them). In contrast, it could be that the more realistic and probable danger from ML is in fact more insidious and prosaic. What you say about human dictatorship is trivially true, but the quote is not about that. > I don’t see how an LLM in the mix would make it worse How about a thought experiment. 1. Take some historical persona you consider well-intentioned (for example, Lincoln), throw an LLM in that situation, and see if it could make it better 2. Take a person you consider a badly intentioned dictator (maybe that is Hitler), throw an LLM in that situation, and see if it could make it worse Let me know what you find.
- lccerina 2y agoSource: trust us. This is some bullshit science.
- padjo 2y agoWell that’s one way to solve the replication crisis
- benterix 2y agoSo, we finally found the cure for the replication crisis in social sciences: just run them on LLMs.
- consp 2y agoAt least they will confirm the experiments they have been trained on.
- somedude895 2y agoMaybe that will help extend the veneer of science on social studies for a few more years before the echo chamber implodes.
- raxxorraxor 2y agoProblem is that many policy decisions are based on bad science in the social sciences, because it provides an excuse. The validity is completely secondary.
- slimjimgrin 2y ago[dead]
- jtc331 2y agoBut does it replicate _better_ than really running the experiment again? Joking…but not joking.
- NicoJuicy 2y agoThat's only for known situations. Eg. Try LLM's to find availability hours when you have the start and end time of each day. LLM's don't really understand that you need to use the day 1 endhour and then the starthour of the next day.
- boesboes 2y agoAnd yet it can't replicate a human support agent. Or even a basic search function for that matter ;)
- 1oooqooq 2y agothis tells more about how social science data is manipulated than the usefulness of llm
- gitfan86 2y agoThe good news is that they should be able to replicate real world events to validate of this is true or not. Tesla FSD is a good example of this in real life. You can measure how closely the car acts like a human based off of interventions and crashes that were due to unhuman behavior, as well in the first round of the robot taxi fleet which will have a safety driver, you can measure how many people complain that the driver was bad
- freeone3000 2y agoI think it is far, far more likely that it replicates social science experiments well enough to simulate people
- pftburger 2y agoThis is gonna end well…
- Piskvorrr 2y agoOoooor maybe, testing if the experiments are similar to what was in the corpus.
- klyrs 2y agoWhy stop at social science? I say we make a questionnaire, give it to the GPT over a broad range of sampling temperatures, and collect the resulting score:temperature data. From that dataset, we can take people's temperatures over the phone with a short panel of questions! (this is parody)
- jrflowers 2y agoI love that anyone can just write whatever they want and post it online. GPT-4 can stand in for humans. Charlie Brown is mentioned in the Upanishads. The bubonic plague was a spread via telegram. Easter falls on 9/11 once every other decade. You can just write shit and hit post and boom, by nature of it being online someone will entertain it as true, even if only briefly so. Wild stuff!
- undergod 2y agoNature's technology vs. Human Technology Emergent vs Reductionist LLMs are Emergent Therefore, LLMs break the mold of human technology and enter the realm of nature's technology We should talk about AI Beings not apps if we wish to stay with this analogy. We can always say that's BS and that the analogy does not reach that deep Or we can take it for what it is and admit LLMs are not similar in their behavior to any tool that humans have created to date, all the way from homo habilus till homo sapiens sapiens. Tools are predictable. Intelligences are not.
- daghamm 2y agoNot so sure about that, but I can absolutely see people getting addicted to them.
- Log_out_ 2y agoWhat happened to running virtual experiments aka running filters on NSA metadata from all conversation of all mankind?
- hollowpython 2y agoDid they test whether GPT4 replicated already existing social science experiments? If so, this might have happened because the experiment was in the training data