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More Agents Is All You Need: LLMs performance scales with the number of agents
- nico 3y agoThey have a public repo: https://anonymous.4open.science/r/more_agent_is_all_you_need/README.md https://anonymous.4open.science/r/more_agent_is_all_you_need... Prompts they used for the benchmarks: https://anonymous.4open.science/r/more_agent_is_all_you_need/src/prompt_lib.py https://anonymous.4open.science/r/more_agent_is_all_you_need... Super interesting. It would be cool to see something like this, but benchmarking LLM-based agents using a set of tools
- EGreg 3y agoHow about swarms of autonomous agents, such as AutoGPT, maybe thousands per human eventually, amassing karma points on all forums, including this one? I can see in a few years each human being surrounded by a ton of LLM agents, "shepherding" their views, downvoting their messages or distracting them with argumentative conversations if they don't conform, and facilitating reputational attacks on scale on all the people whose speech is recognized as being contrary to what's desired. Of course, there wouldn't be just one group deploying these swarms. It would be lots of different groups, akin to slaughterbots video: https://www.youtube.com/watch?v=O-2tpwW0kmU https://www.youtube.com/watch?v=O-2tpwW0kmU The difference is that there wouldn't be physical violence, it would just gradually turn the entire Internet into a dark forest.
- bhouston 3y agoThe book a Fire Upon the Deep had everyone surrounded by AI agents which became extensions of themselves.
- williamcotton 3y agoWell then most will be demanding that our governmental institutions issue cryptographically signed ID cards verified by an in-person visit to the DMV. Or, you choose to opt out and swim in a sea of nonsense.
- Olesya000 3y ago[dead]
- phkahler 3y agoDoes it take less compute to train N agents vs one large model? Seem like a big win. Can the majority of the training be done independently or in distributed fashion?
- kromem 3y agoFinally. I've been saying that we need to stop focusing on a single agent getting everything right and instead layer agents for about 16 months now, but it's great to have a paper to point to. It's interesting that the diminishing returns for tasks flatten out rapidly around the same size as the ideal human meeting sizes: https://www.researchgate.net/figure/18-Optimal-Meeting-Sizes-1_tbl6_3889414 https://www.researchgate.net/figure/18-Optimal-Meeting-Sizes... If this was done at more granular steps of agent quantity I'm curious just how closely it would match those numbers. I'd also really love to see the eventual follow-up where we see how much more performance can be obtained when the agents are each fine tuned towards slightly different aims. I'd expect there'd even be a performance lift from just having the agents each set at different temperature levels. Very happy to see the research community starting to step in this direction!
- piloto_ciego 3y agoThis is how I think humans work. We have 5 or 8 versions of us running around in our skulls or whatever and one of them is somewhat of a supervisor.
- huffmsa 3y agoI personally visualize them at a table from time to time.
- AnimalMuppet 3y agoEchoes of Society of Mind by Marvin Minsky.
- rubslopes 3y agoI don't know why you were downvoted. Although Minsky's work has been highly criticized, it also has been influential in AI research.
- ShamelessC 3y agoThis usage of the word "agent" when they simply mean "another LLM" is sort of nonstandard, no? To me an agent implies some degree of RL.
- Buttons840 3y agoSo this is an ensemble of many LLMs? I wonder how well a bunch of LLMs trained on personal computers, so fairly small, could perform together? Train a LLM on your emails, train an LLM on a text book, download a bunch of arbitrary LLMs from the net you find interesting, throw them all together into a big pile, and use a moderator LLM that knows how to format their output into an assistant format. So, the email LLM would try to autocomplete sentences from your emails, and the text book LLM would try to autocomplete sentences from the text book. People could offer LLMs to download, almost as a way of compressing information, download the LLM of your favorite programming language, and TV series, etc. The important part would be having a moderator algorithm that can shape these LLMs from dumb sentence autocompleters (barely more than a fancy Markov chain) into a coherent assistant format. For example, the text book LLM would just endlessly spew semi-random sentences from the text, but a good moderator algorithm could see that it has sufficiently answered the question and cut it off. In short, it's interesting that separate LLMs can integrate with each other and strengthen each other and it makes me wonder if we could build modular LLMs.
- mofosyne 3y agoYour idea inspired me to see what such a microstory based on your idea would look like (Of course generated by ChatGPT3.5): > As I delved into my computer, eager to tackle my to-do list, I was met with an unexpected sight: a digital love triangle among the Language Models (LLMs). The Email LLM, with its quick wit, seemed to be engaging in flirtatious banter with the verbose Textbook LLM, while the Programming Language LLM watched on with amusement. I couldn't help but laugh at the absurdity of it all, but as the bickering between the LLMs intensified, I realized their antics were hindering my progress. With a mixture of frustration and amusement, I gently redirected the LLMs back to their intended purpose, finally able to accomplish my task amidst the chaotic comedy within my computer.
- m3kw9 3y agoAll these AI researchers treating “..is all you need” a meme or something.
- Intralexical 3y agoI think they've been overfitted.
- latentsea 3y agoAll you need is all you need.
- TheCaptain4815 3y agoRecommend anyone interested in a agent framework lookup AutoGen by Microsoft
- ukuina 3y agoThis paper is specifically disproving the efficacy of agentic frameworks like AutoGen. Also, the built-in function-calling in GPT4 is simpler to use than AutoGen2's abstraction.
- hopfenspergerj 3y agoEnsemble of any number GPT 3.5 agents is less accurate than one call to GPT-4.
- trash_cat 3y agoIt's funny because GPT-4 is actually a pile of 3.5s. You just need to set it up correctly.
- BinRoo 3y ago> GPT-4 is actually a pile of 3.5s I understand the intension and reference you're making. I bet the implementation of GPT-4 is probably something along those lines. However, spreading speculation in definitive language like that when the truth is unknown is dishonest, wouldn't you agree?
- trash_cat 3y agoSure, I could it put it less definitively, but realistically, what else can it be? The transformer won't change much and all of the models, at the core use it. It's a closely guarded secret because it's easy to replicate.
- jamala1 3y agoI guess it's the difference between an ensemble and a mixture of experts, i.e. aggregating outputs from (a) model(s) trained on the same data vs different data (GPT-4). Though GPT-4 presumably does not aggregate, but it routes.
- nicklecompte 3y agoOne frustration I've had with all this mixture-of-experts research: Randomized Algorithms 101 - or basic stochastic reasoning - suggests that if the temperature parameter is > 0, querying an LLM N times and picking the majority result (perhaps with an N+1th query to the LLM) will generally result in better performance than asking it once and choosing that result. It seems plausible to me that the gains can be further improved with a specialized mixture of different LLMs (which could then be run at temp = 0), or by finding better ways to break tasks into subtasks as this paper suggests. But AFAICT nobody has done anything to actually quantify these hypothetical gains versus the dumb randomized algorithm approach! In particular there might be voting strategies or mixtures - even specific models - where MoE/etc is strictly worse than naive repetition. I am a concerned citizen w.r.t LLMs rather than a researcher, so I might be missing something. It just seems odd that LLM researchers forgot the first chapter of Motwani/Raghavan.
- benaubin 3y agoI'd assume that there's a difference between picking the best _token_ across an assortment of randomly selected tokens, versus picking the best _string_ of randomly-selected tokens.
- spencerchubb 3y agoThis trend of "All You Need" in paper titles needs to die. The original "Attention is All You Need" used that title because it is literally true in their case. So many papers just use it as a meme now, and it distracts from the true insight of the paper.
- donovanr 3y agoit's uncreative/tired but at least to the point. Too many papers are confused / opaque agglomerations of a year's worth of research shoehorned into a paper. At least with these you can fairly easily assess whether the claim is supported or not.
- ravelfan 3y agoObviously, more people are going to read it. It is like putting a stupid face on your youtube video to show how shocked and amazed you are at the content.
- soulofmischief 3y agoIt's equally annoying to see this comment crop up every single time someone uses the phrase.
- karagenit 3y agoAnd it’s equally annoying to see this comment about that comment about the paper every time. Recursion!
- soulofmischief 3y agoThis is why I come to Hacker News :)
- Terr_ 3y ago"All You Need Considered Harmful."
- infogulch 3y agoThis seems related to an interesting recent ACM ByteCast podcast episode with Edward Chang, an Adjunct Professor in the Department of Computer Science at Stanford University. [1] (Note there is a transcript if you don't want to listen.) The approach he uses is to arrange for multiple LLMs to dialogue between each other about a discussion topic where the human acts as a moderator instead of the question/answer format that LLMs commonly take today. They find that the final answer that multiple LLMs come to in dialogue results in a huge improvement in both precision and accuracy for the same resources. [1]: https://learning.acm.org/bytecast/ep50-edward-y-chang https://learning.acm.org/bytecast/ep50-edward-y-chang
- j45 3y agoIs this effectively describing something like crewai?
- rahimnathwani 3y agoThis paper suggests that you don't need the debating part: just get LLM to work on the problem independently, and choose the most popular answer.
- infogulch 3y agoInteresting. Somehow it seems odd to add randomness (temperature) and then wash it away by averaging it out.
- ewild 3y agotemp applies to each token so the range of temperature is significantly larger than the average being pulled
- naasking 3y agoIn optimization problems, randomness can often get you out of local minima/maxima, and so averaging out a bunch of random search paths might get you better results in the worst case. Something similar might be happening here. The training set will be biased in various ways that might create weird local min/max points and so this process could avoid those weird kinks.
- Havoc 3y agoHere is a link to the main diagram: https://anonymous.4open.science/r/more_agent_is_all_you_need/img/algorithm_2.pdf https://anonymous.4open.science/r/more_agent_is_all_you_need... Seems like a pretty brute force approach of frankly just throwing more compute at the query (via semi-statistical means). I'd be more interested in how to scale this via different agents. i.e. do we have say one type of agent that is specialized to produce ideas, while another is trained to evaluate ideas. Those sort of chains seem like they'd be powerful - if you can find a way to generalize it
- skybrian 3y agoEyeballing the graphs, it seems that most of the gain is with 10 agents, a bit more with 20, and there are diminishing returns after that. Apparently, more agents isn't going to do it.
- deleted 3y ago[deleted]
- al2o3cr 3y ago35x more reasoning for a 10% increase in accuracy? Scaling with a scale factor of almost zero is still scaling, I guess.
- phire 3y agoI'm not sure people in these comments are reading this paper correctly. This seems to essentially disprove the whole idea of multi-agent setups like Chain-of-thought and LLM-Debate. Because this paper introduces their alternative method which simply runs the same query multiple times on the same LLM, without any context shared across queries. And then they run a similarity algorithm on the answers and pick the most common answer. (Which makes sense to me. If an LLM is giving you a mixture of "hallucinations" and correct answers, the correct answers will similar and the hallucinations will hopefully be chaotic) And this simple algorithm preform just as well (and sometimes better) than all the other multi-agent algorithms. This suggests that the other multi-agent schemes with their clever prompts aren't really doing anything special; Their improve results are coming mostly from the fact that the LLM is run multiple times, that the prompt asks the LLM to pick the best answer.
- sinuhe69 3y agoBut if I set the temperature to 0, the model will pick the highest probable token and the output will be always the same. But we already know that by no mean it can guarantee a correct answer. So how can multiple runs be better?
- phire 3y agoYes, but picking the most similar output from a bunch of queries with a higher temperature is not the same thing as the output from a single low temperature query.
- sinuhe69 3y agoPossibly, but it stills doesn’t explain why multiple runs will result in better answer. In the work, the authors also hasn’t compared the multiple runs results with the single run using zero temperature. So, maybe all the overhead is just to achieve the same result already encoded in the networks? I don’t know. Also the result is somewhat counterintuitive. We know that by low level of understanding, if we ask a student a hard question and he tried many times, the most accurate answer is often not the most popular one but a single answer. And that by retaining memory, reasoning capacity and continuous learning , which is not the case with LLM. Btw: HN is for discussion. If some just want to vote for the beauty contest, please leave.
- riwsky 3y ago[flagged]
- kshitij_libra 3y agoThis trend really needs to die. If you can’t come up with an original paper name , maybe the contents aren’t that original either
- parentheses 3y agoI wonder if this performs even better when this is done tokenwise in the inner loop of the LLM
- atum47 3y agoIf we sum all those "x is all you need" we're going to realize that we need a lot of things
- trash_cat 3y agoIs this not an incredibly expensive/unsustainable method? I agree with the sentiment that MoE is the way to go as the newer models will probably see diminishing returns. But the compute for a single prompt will suddenly increase 7-15 fold?
- bigEnotation 3y agoYeah, see GPT 3.5 vs GPT 4 pricing.
- bearjaws 3y ago"all you need is a six figure OpenAI bill"
- Garlef 3y agoif it replaces 2-3 experts that might be a good deal
- ukuina 3y agoIf GPT4 is 20x the price of GPT3.5, but it only takes 10x GPT3.5 runs to get similar quality of response (and likely faster), you'll still come out ahead.
- dimask 3y agoI doubt that 10xGPT3.5 > GPT4. There are a lot of tasks that GPT4 can do and GPT3.5 just cannot. Also, in such cases I find that GPT3.5's hallucinations are quite consistent, so such a method is probably not gonna help.
- imtringued 3y agoSo what? It's not like GPUs are compute starved.
- ZachSaucier 3y agoAlso the amount of non-renewable resources used and emissions goes up 7-15x
- bigEnotation 3y agoI thought this is what GPT 4 was, it uses a boosting algorithm over GPT 3.5?
- bearjaws 3y agoI'm sure all the AI companies love the idea of running the same prompt 8 times...
- datascienced 3y agoNVDA does for sure
- sheepscreek 3y agoHaving given this problem a great deal of thought, I have developed a strong intuition around this. I believe not only is AGI feasible, it is already doable. For example, several hundred GPT-4 based agents specializing in different skill sets should be able to collaboratively solve many problems. Their ability to work on so many facets of the same problem will make them very effective against multidisciplinary problems. What’s the catch? Well, the back and forth has to play out in a serial order, so it cannot be parallelized. At today’s abysmal inference speeds, it may take this AGI many times longer than a trained human. Now imagine the effectiveness of this method when we can speed up inference to several hundred times a minute. Now AGI suddenly becomes way more efficient than a human.
- 29athrowaway 3y agoThere's a paper about that https://www.microsoft.com/en-us/research/publication/sparks-of-artificial-general-intelligence-early-experiments-with-gpt-4/ https://www.microsoft.com/en-us/research/publication/sparks-...
- intended 3y agoDefinition of AGI at play here?
- deleted 3y ago[deleted]
- etamponi 3y agoIsn't this the same as lowering the temperature of the LLM?
- whiteandnerdy 3y agoI remember hearing that Beam Search doesn't work well for LLMs, because it leads to repetitive, generic output. The majority vote sampling technique in this paper sounds like it'd give similar output to Beam Search, because it's sampling sequences of tokens from a joint distribution. So why doesn't it give repetitive output like Beam Search does? What am I missing?
- l_l_m_5_0 3y agoAveraging LLM outputs will ensure the final output will contain a lot of words with no substance. However, it’s essential to recognize that averaging bad data doesn’t always lead to better results. Garbage in, garbage out — averaging cannot magically transform flawed inputs into accurate outputs.
- yantrams 3y agoThis is my go to method for pretty much every hard problem that I'm forced to solve where I don't have the domain expertise / interest / time. The trick lies in coming up with a clever similarity metric that incorporates penalties etc. You can even go a level deeper and use multiple similarity algorithms and then poll on top of them. Here's a taxonomy extractor for text that I made using similar principles that is surprisingly as good as anything else that I've seen - https://dash.scooptent.com/text https://dash.scooptent.com/text
- dindobre 3y agoSmells similar to Sutton's bitter lesson!
- krystofee 3y agothis study's got me worried. it goes against the hope that AGI won't just sneak onto the internet and do its own thing. with more work on making LLMs bigger and showing they can do more, the thought that this could actually happen in the future gets real scary, especially since i can run smaller versions on my own pc now.
- pitorooooto 3y ago[flagged]
- zone411 3y agoThis is quite interesting because I've specifically tried this kind of basic ensembling for my NYT Connections benchmark and it didn't work. This is something everybody would try first before more complicated multi-step prompting, and yet since ChatGPT 3.5 I'm not aware of any papers showing that it works. It will be interesting to reproduce this result and learn more about how they set it up to make it work.
- msoad 3y agoWhat's interesting is that each run of the model tends to converge to a different "local maximum" in the solution space, and some of these local maxima correspond to better performance than others. By running the model multiple times, we increase the chances of finding a higher-quality local maximum or even the absolute best solution. This got me thinking: why is this ensembling step implemented as a higher-level abstraction on top of the base LLM, rather than being built directly into the neural network architecture and training process itself?
- sp332 3y agoWell you’re right that LLM tooling is totally inadequate. At least we already have beam search. But the more boring answer (and why beam search is also uncommon) is that running the query multiple times is more expense.
- deleted 3y ago[deleted]
- Takennickname 3y agoThe arrogance to name your paper that.
- bwnjnoei 3y agoModel ensemble is a classic method. Deep learning is always rediscovering and reinventing the classics. I believe that many people have used this method before, but they just haven't published a paper on it.
- edshiro 3y agoI've saved the paper to read it later. The premise of this work seems very interesting... But I wonder how practical it is from both a cost and time perspective. I am toying around with an AI Agents library and one of the annoying UX things I notice is the time it takes to get my answers, because each call to an agent (either GPT-4 or Claude 3) is kinda slow. Besides the time, it feels quite wasteful token wise. I'm skeptical this approach will be adopted by many in the AI Agent space, but of course I could be very wrong.
- temporarely 3y agoJust reading the (current top) few comments and whimsically wondered at the super business model of companies offering LLM services: a car service that won't get you from point A to B unless you hail it n times. A detergent that must be applied n times before cloths come out ("probably") clean. If a company is offering "Artificial intelligence" at a price, then isn't reasonable that you only pay for correct answers? If the company is offering car service, shouldn't you only pay if they take you to your destination?
- CSSer 3y agoAgreed, and if it fails often enough, isn’t the bar at which a human or general-purpose, traditionally structured automation is going to be superior pretty low? This is how I think this bubble will pop. No doubt, LLMs are a breakthrough tool, but I’m sincerely skeptical of all but the most granular applications. Perhaps the moral is that diffusing LLM agent accountability has the same failure model as the pre-existing human one.
- nequo 3y agoCounterpoint: National Weather Service forecasts don’t always turn out to be correct but we don’t only pay the NWS on days when they are correct.
- samus 3y agoCompanies usually offer a service or a product. If the company doesn't deliver what was agreed upon, then the customer can demand correction. If a taxi driver takes a needlessly convoluted route, charges too much, or doesn't bring you to the destination, you can complain to the taxi company. If the laundry didn't work, you insist on doing it again. However, many activities are inherently fraught with risk or uncertain results since there are always things outside of anyone's control. A lawyer can't promise you prevail in a court case, but they have to advocate your case to the best of their abilities. A doctor won't guarantee that you become healthy again. No taxi driver will guarantee you that you will reach the destination in time, but they will bring you there. Atlassian won't guarantee you will meet a release deadline if you use their managed JIRA instance, but they will do their best to prevent data loss. And a company that basically sells access to a chatbot won't guarantee that it gives you correct results. Maybe availability guarantees.
- temporarely 3y agoReading the paper the thought occurs that IF the likelihood of correct response increases by the number of agents employed AND this involves application of a function (whatever) to select the 'best' from the possible answers, doesn't that imply that LLM has insufficient dimensions? In other words, I am wondering if LLM hallucinations [sic] are in fact symptomatic of 'conflation' which could itself be the result of insufficient dimensions. Thoughts?
- adamlp 3y agohttps://anonymous.4open.science/api/repo/more_agent_is_all_you_need/file/img/algorithm_2.pdf?v=1c8ea7a90afd32108b7f754321bfdaa3a46edae4 https://anonymous.4open.science/api/repo/more_agent_is_all_y... How can you have majority voting based on text produced by LLMs, won’t every answer string be essentially distinct with extremely high probability if it’s over a couple dozens bits of output (which any useful LLM output would be, right?)?
- mateiz 3y agoThis is a cool paper showing there is value in using an LLM multiple times, but in recent research we showed that with majority voting, quality can decrease past some point as you make more calls. Check out https://arxiv.org/pdf/2403.02419.pdf https://arxiv.org/pdf/2403.02419.pdf. It raises the natural question of how to design the best inference algorithm given an LLM you can call multiple times.