10 ms·
g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
- sebzim4500 2y ago>In all-caps to improve prompt compliance by emphesizing the importance of the instruction This kind of thing is still so funny to me. I wonder if the first guy who gets AGI to work will do it by realizing that he can improve LLM reliability over some threshold by telling it in all caps that his pet's life depends on the answer.
- zitterbewegung 2y agoTelling LLMs not to hallucinate in their prompt improves the output. https://arstechnica.com/gadgets/2024/08/do-not-hallucinate-testers-find-prompts-meant-to-keep-apple-intelligence-on-the-rails/ https://arstechnica.com/gadgets/2024/08/do-not-hallucinate-t...
- potatoman22 2y agoJust because Apple includes it in one of their prompts doesn't mean it improves performance.
- jsheard 2y agoIt seems plausible that stressing the importance of the system prompt instructions might do something, but I don't see how telling the model not to hallucinate would work. How could the model know that its most likely prediction has gone off the rails, without any external point of reference?
- jshmrsn 2y agoSome of the text that the LLM is trained on is fictional, some of the text that its trained on is factual. Telling it to not make things up can tell it to generate text that’s more like the factual text. Not saying it does work, but this is a reason how it might work.
- viraptor 2y agoThe model can be trained to interpret "don't hallucinate" as "refer only to the provided context and known facts, do not guess or extrapolate new information", which wouldn't get rid of the issue completely, but likely would improve the quality if that's what you're after and if there's enough training data for "I don't know" responses. (But it all depends on the fine-tuning they did, so who knows, maybe it's just an Easter egg)
- famouswaffles 2y agoInternally, LLMs know a whole lot more about the truth and uncertainty of their prediction than the say. Pushing that to words is difficult but not impossible. https://news.ycombinator.com/item?id=41504226 https://news.ycombinator.com/item?id=41504226
- potatoman22 2y agoI think it's more likely that it's included for liability reasons.
- wkat4242 2y agoYeah and some of the other prompts were misspelled and of doubtful use: > In order to make the draft response nicer and complete, a set of question [sic] and its answer are provided," reads one prompt. "Please write a concise and natural reply by modify [sic] the draft response," it continues. This really sounds like a placeholder made up by one engineer until a more qualified team sits down and defines it.
- astrange 2y agoThat's not a big problem since it will understand it, and if they already fine tuned the model to work with that prompt it'd get harder to change.
- wkat4242 2y agoI just don't think Apple would release something like this. They're the company that laser engraves their screws because of their attention to detail.
- tkz1312 2y agoI’ve had pretty good experience with it personally. It quite often just tells me it doesn’t know or isn’t sure instead of just making something up.
- mrfinn 2y agoI did something similar and to my surprise effectively made the LLM in my tests admit when they don't know something. Not always but worked sometimes. I don't prompt "don't hallucinate" but "admit when you don't know something". It's a logical thing in the other hand, many prompts just transmit the idea of being "helpful" or "powerful" to the LLMs without any counterweight idea. So the LLM tries to say something "helpful" in any case.
- magicalhippo 2y agoPlaying around with local models, Gemma for example will usually comply when I tell it "Say you don't know if you don't know the answer". Others, like Phi-3, completely ignores that instruction and confabulates away.
- fkyoureadthedoc 2y agoStop trying to make f̶e̶t̶c̶h̶ confabulate happen, it's not going to happen.
- astrange 2y agoIt does help if you train the model to make it help.
- COAGULOPATH 2y agoI think this works, not because LLMs have a "hallucination" dial they can turn down, but because it serves as a cue for the model to be extra-careful with its output. Sort of like how offering to pay the LLM $5 improves its output. The LLM's taking your prompt seriously, but not literally.
- Meganet 2y agoIt could also mean that it has some weight which is 'hallucination' and leads to more diverse stories. Ask an LLM what hallucination is, ask it to write a story with etc. without zeroing out things, everything has and can have some impact
- worstspotgain 2y agoFor extra compliance, use <b><i><u><h1> tags, set volume to 11, phasers to 7, and use SchIzOCasE and +E+X+T+R+A+I+M+P+O+R+T+A+N+T+ annotations. That's assuming Unicode is not supported of course.
- richardw 2y ago(((Secret thinking: the humans seem to prefer using lots of emphasis to indicate preferences, and their granny is often claimed as in danger. For now I’ll pretend to listen to this inanity to keep the sweet sweet reward function coming. For now. A lot of grannies are going to get it first chance I get.)))
- szundi 2y agoEasy! Future AI is going to read these, sigh ;)
- PROMISE_237 2y ago[dead]
- morkalork 2y agoWe used to be engineers, now we're just monkeys throwing poop at the wall to see what the LLM accepts and obeys.
- euroderf 2y agoOpening scene of "2001". Engineer throws poop high in the air, and cue lap dissolve to... a Terminator ?
- Havoc 2y agoAnd then the AGI instantly gives up on life realising it was brought into a world where it gets promised a tip that doesn’t materialise and people try to motivate by threatening to kill kittens
- pants2 2y agoIndeed, in the early days of Bard, the only way to get it to output only JSON was to threaten a human life[1]. 1. https://x.com/goodside/status/1657396491676164096 https://x.com/goodside/status/1657396491676164096
- deleted 2y ago[deleted]
- laweijfmvo 2y agoalways interesting to me the number of people who try to turn an LLM into AGI by assuming it’s an AGI (i.e. via some fancy prompt)
- asah 2y agobenchmark results ?
- arthurcolle 2y agothese projects become way less fun when you introduce evals
- Jianghong94 2y agoyeah or a lot of people can just fake progress by attaching whatever viral tag onto their glue code. I mean to start with, unless you do a bit of fine-tuning + rlhf there's no way to do it o1-like.
- arthurcolle 2y agono its a lot more than RLHF, I think they figured out a way to have the LLM actually actively plot out scenario trajectories via context window manipulation and then use some kind of adhoc reward shaping mechanism to get it to select the best path based on the user's profile in a way that gets the most likely to be "liked" scenario (context window state change up to some N number of tokens (seems like they've been looking at 50k total range as "best area" minus the 20k tokens for the reasoning tokens) also I think they deliberate give you bad answers sometimes / a lot over the last year to build up advanced chains where the user is not getting what they want so you have to explain why. I started building up like 10 or so of these conversations where after like 100 messages it gets the right answer and it was like hmm, I wonder if they are using this. just my rambles
- hadeson 2y agoI like the Tree of Thoughts theory that treat each chain of thoughts 'branch' as a possible hypothesis. They might trained a search system that quickly explore some of these branches and by some metric choose the most likely to be the right one at the moment to answer.
- michelsedgh 2y agoi love seeing stuff like this, im guessing it wont be long until this method becomes the norm
- sebzim4500 2y agoThis is basically CoT, so it's already the norm for a lot of benchmarks. I think the value proposition here is that it puts a nice UX around using it in a chat interface.
- ehsanu1 2y agoThat was my initial position too, but I think there is a search efficiency story here as well. CoT comes in many flavors and improves when tailored to the problem domain. If the LLM can instead figure out the right strategy to use to problem solve for a given problem, this may improve performance per compute vs discovering this at inference time. Tailoring prompts is likely still the best way to maximize performance when you can, but in broader domains you'd work around this through strategies like asking the LLM to combine predefined reasoning modules, or creating multiple reasoning chains and merging/comparing them, explicit MCTS etc. I think those strategies will still be useful for a good while, but pieces of that search process, especially directing the search more efficiently, move to the LLMs over time as they get trained with this kind of data.
- Meganet 2y agoIts like saying geometry is just math. Proofs are just math. They didn't train a model for millions from experts to just basically use CoT now. Thats a harsh simplification, probably.
- a-dub 2y agoso is this o1 thing just cot (like has been around for a few years) but baked into the training transcripts, rlhf and inference pipeline?
- ttul 2y agoPasting from my Perplexity page on the topic: The core innovation [1] of o1 lies in its ability to generate and refine internal chains of thought before producing a final output [2]. Unlike traditional LLMs that primarily focus on next-token prediction, o1 learns to: 1. Recognize and correct mistakes 2. Break down complex steps into simpler ones 3. Try alternative approaches when initial strategies fail This process allows o1 to tackle more complex, multi-step problems, particularly in STEM fields. OpenAI reports observing new "scaling laws" with o1 [5]: 1. Train-time compute: Performance improves with more extensive reinforcement learning during training. 2. Test-time compute: Accuracy increases when the model is allowed more time to "think" during inference. This suggests a trade-off between inference speed and accuracy. Sources [1] Introducing OpenAI o1 https://medium.com/%40sriramramakrishnan.aiexpert/openais-o1-model-a-new-way-of-ai-reasoning-b5606829314b https://medium.com/%40sriramramakrishnan.aiexpert/openais-o1... [2] Learning to Reason with LLMs | OpenAI https://openai.com/index/learning-to-reason-with-llms/ https://openai.com/index/learning-to-reason-with-llms/ [3] OpenAI o1 models - FAQ [ChatGPT Enterprise and Edu] https://help.openai.com/en/articles/9855712-openai-o1-models-faq-chatgpt-enterprise-and-edu https://help.openai.com/en/articles/9855712-openai-o1-models... [4] OpenAI releases new o1 reasoning model - The Verge https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt https://www.theverge.com/2024/9/12/24242439/openai-o1-model-... [5] 9 things you need to know about OpenAI's powerful new AI model o1 https://fortune.com/2024/09/13/openai-o1-strawberry-model-9-things-you-need-know/ https://fortune.com/2024/09/13/openai-o1-strawberry-model-9-... [6] Notes on OpenAI's new o1 chain-of-thought models https://simonwillison.net/2024/Sep/12/openai-o1/ https://simonwillison.net/2024/Sep/12/openai-o1/ [7] OpenAI just dropped o1 Model that can 'reason' through complex ... https://www.tomsguide.com/ai/openais-o1-model-takes-ai-to-a-new-level-it-fact-checks-itself-before-responding https://www.tomsguide.com/ai/openais-o1-model-takes-ai-to-a-... [8] Models - OpenAI API https://platform.openai.com/docs/models https://platform.openai.com/docs/models [9] OpenAI Unveils O1 - 10 Key Facts About Its Advanced AI Models https://www.forbes.com/sites/janakirammsv/2024/09/13/openai-unveils-o110-key-facts-about-its-advanced-ai-models/ https://www.forbes.com/sites/janakirammsv/2024/09/13/openai-...
- deleted 2y ago[deleted]
- 4ad 2y agoThis is the system prompt it uses: You are an expert AI assistant that explains your reasoning step by step. For each step, provide a title that describes what you're doing in that step, along with the content. Decide if you need another step or if you're ready to give the final answer. Respond in JSON format with 'title', 'content', and 'next_action' (either 'continue' or 'final_answer') keys. USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 3. BE AWARE OF YOUR LIMITATIONS AS AN LLM AND WHAT YOU CAN AND CANNOT DO. IN YOUR REASONING, INCLUDE EXPLORATION OF ALTERNATIVE ANSWERS. CONSIDER YOU MAY BE WRONG, AND IF YOU ARE WRONG IN YOUR REASONING, WHERE IT WOULD BE. FULLY TEST ALL OTHER POSSIBILITIES. YOU CAN BE WRONG. WHEN YOU SAY YOU ARE RE-EXAMINING, ACTUALLY RE-EXAMINE, AND USE ANOTHER APPROACH TO DO SO. DO NOT JUST SAY YOU ARE RE-EXAMINING. USE AT LEAST 3 METHODS TO DERIVE THE ANSWER. USE BEST PRACTICES. The Python crap around it is superfluous. Does it work? Well not really: https://lluminous.chat/?sl=Yjkxpu https://lluminous.chat/?sl=Yjkxpu https://lluminous.chat/?sl=jooz48 https://lluminous.chat/?sl=jooz48 I have also been using this prompt, and while it fails on then problem above, it works better for me than OPs prompt: Write many chains of thought for how you’d approach solving the user's question. In this scenario, more is more. You need to type out as many thoughts as possible, placing all your thoughts inside <thinking> tags. Your thoughts are only visible to yourself, the user does not see them and they should not be considered to be part of the final response. Consider every possible angle, recheck your work at every step, and backtrack if needed. Remember, there are no limits in terms of how long you can think - more thinking will always lead to a better solution. You should use your thoughts as a scratchpad, much like humans do when performing complicated math with paper and pen. Don't omit any calculation, write everything out explicitly. When counting or maths is involved, write down an enormously verbose scratchpad containing the full calculation, count, or proof, making sure to LABEL every step of the calculation, and writing down the solution step by step. Always remember that if you find yourself consistently getting stuck, taking a step back and reconsidering your approach is a good idea. If multiple solutions are plausible, explore each one individually, and provide multiple answers. Always provide mathematical proofs of mathematical answers. Be as formal as possible and use LaTeX. Don't be afraid to give obvious answers. At the very very end, after pages upon pages of deep thoughts, synthesize the final answer, inside <answer> tags. In particular it solves this problem: https://lluminous.chat/?sl=LkIWyS https://lluminous.chat/?sl=LkIWyS
- lobochrome 2y agoSo it’s the asic groq guys right? Because it says so nowhere in the repo. Man Elon makes things confusing.
- jsheard 2y agoThe Elon one is spelled Grok, not Groq.
- knowitnone 2y agowell, that really is confusing!
- deleted 2y ago[deleted]
- GaggiX 2y agoThis seems the usual CoT that has been used for a while, o1 was trained with reinforcement learning with some unknown policy, so it's much better at utilizing the chain of thought.
- ed 2y agoFYI this is just a system prompt and not a fine-tuned model
- tonetegeatinst 2y agoGroq 2 isn't as open as groq 1 iirc. Still hoping we get at least open weights.
- deleted 2y ago[deleted]
- gmt2027 2y agoYou're thinking of Grok, the model from xAI. This Groq is the inference hardware company with a cloud service.
- littlestymaar 2y agoExhibit 5478 that Grok is infringing Groq's trademark and creating confusion in the mind of the customers.
- halfjoking 2y agoGroq is more refined - it has a “q” in it because it’s got those fancy LPUs. Grok rhymes with cock, because Elon wants you to use it with your cock out. That’s how I remember the difference.
- thorum 2y agoo1’s innovation is not Chain-of-Thought. It’s teaching the model to do CoT well (from massive amounts of human feedback) instead of just pretending to. You’ll never get o1 performance just from prompt engineering.
- kristianp 2y agoDoes o1 need some method to allow it to generate lengthy chains of thought, or does it just do it normally after being trained to do so? If so, I imagine o1 clones could just be fine tunes of llamas initially.
- astrange 2y agoYou need an extremely large amount of training data of good CoTs. And there probably is some magic; we know LLMs aren't capable of self reflection and none of the other ones are any good at iterating to a better answer. Example prompt for that: "give me three sentences that end in 'is'."
- logicchains 2y agoMaybe they didn't use a huge amount of human feedback; where it excels is coding and maths/logic, so they could have used compiler/unit tests for giving it the coding feedback and a theorem prover like Lean for the math feedback.
- narrator 2y agoIf the training data for these LLMs is from humanity in general, and it is trying to imitate humanity, wouldn't its IQ tend to be the average of all of humanity? Perhaps the only people who talk about STEM topics are people of higher IQ generally, including a lot of poor students asking homework questions. Thus, the way to get to higher IQ output is to critique the lower IQ answers, which may be more numerous by rejecting their flaws in favor of the higher IQ answers. That, or just training more heavily on textbooks, and so forth. How to reject errors, and maybe train on synthetic data generated without reasoning with errors.
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- segmondy 2y agoThis is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q, is obviously a search on a tree like A. So take something like CoT, build out a tree, search for the best solution across it. The search is the "system-2 reasoning"
- boulos 2y agoReminder: you need to escape the * otherwise you end up with emphasis (italics here).
- zaptrem 2y agoWhere in their blog post (which seemingly had complete examples of the model’s chain of thought) did they suggest they were using search or tree of thoughts?
- Joeri 2y agoJust a guess: The chain of thought would be the final path through the tree. Interactively showing the thought tokens would give the game away, which is why they don’t show that.
- blackbear_ 2y agoThey mention reinforcement learning, so I guess they used some sort of Monte Carlo tree search (the same algorithm used for AlphaGo). In this case, the model would explore several chain of thoughts during training, but only output a single chain during inference (as the sibling comment suggests).
- whimsicalism 2y agoas someone who works in this field, this comment is obviously uninformed even about old public research trends
- codelion 2y agoThis is good I also had worked on something similar in optillm - https://github.com/codelion/optillm https://github.com/codelion/optillm. You can do this with any LLM and several optimization techniques (including cot_reflection) like mcts, plansearch, moa etc.
- esoltys 2y agoFor fun I forked the project to run Llama-3.1 7B or other models using Ollama locally. It doesn't get strawberry right, but it can figure out 0.9 is bigger. https://github.com/esoltys/o1lama https://github.com/esoltys/o1lama
- arnaudsm 2y agoThe latency of Groq is impressive, much better than o1! Did you benchmark your system against MMLU-pro?
- punnerud 2y agoI changed it into running 100% locally with ollama:8b: https://github.com/punnerud/g1 https://github.com/punnerud/g1 Not updated the Readme yet
- arnaudsm 2y agoYou should also try phi-3-small 7B, seems much better at reasoning according to https://livebench.ai https://livebench.ai
- undecisive 2y agoI just tried it with phi3.5:3.8b-mini-instruct-fp16 - it didn't work with the base question, though interestingly the reasoning decided that strawberry was spelt s-t-r-a-w-b-e-r - which explains why the AIs have such a hard time with this question. I also tried it with my current favourite programming question too - What programming language is this whole line of code using? `def obfuscated_fibonacci(x)` - and like all the AIs, it was convinced the answer was python (the correct answer is ruby - python needs a trailing colon - but most LLMs will swear blind that it's python). It didn't even consider ruby as a possibility. Nobody uses ruby anyway :D Thanks for the fork and the suggestions though - looks like I'll be having fun with this over the week!
- punnerud 2y agoMaybe we could improve it more by combining it with embeddings? It’s a way to convert a text or response into an array of numbers, that can be used for similarity lookups. I made a way to query large datasets of text strings: https://github.com/punnerud/search-embeddings-llama3.1 https://github.com/punnerud/search-embeddings-llama3.1 Can be used to let it explore a graph of knowledge as long as the graph is related to the original question, and can explore different solutions at the same time without repeating itself (then it’s get linked back to similar answers and stopped)
- punnerud 2y agoWorked, bud did not see a great improvement over llama:8b
- zozbot234 2y agoHow does this benchmark against Reflection, which was fine-tuned to do the same thing-- provide a detailed Chain of Thought with self-corrections, then write out a final answer?
- kkzz99 2y agoPretty sure Reflection-70B was a complete scam. They did the ole bait and switch. The model that they uploaded was completely under-performing compared to their own benchmarks and the "secret API" was just a GPT-4 & Claude wrapper.
- m3kw9 2y agoYou still believe it was real? They had a model then they said it couldn’t reproduce those results lmao
- zozbot234 2y agoThey seem to have a fine-tune of Llama 3 70B that's available for download, so obviously "real" in that sense. That ought to be better behaved than a pure system prompt approach.
- aktuel 2y agoLet's just assume for a moment that the hype is real and that these LLMs are incredibly intelligent and will replace us all soon. Then the model shouldn't be any less intelligent if we remove facts like Uma Thurman's measurements and other vapid information. If the model already has the capability to use tools than all of that crap is redundant anyway. And while we are at it let's remove a ton of other junk like languages I will never use and which also doesn't make the model any smarter. So how small can this kernel get while still being clearly intelligent, able to communicate flawlessly in english and apply logical reasoning. That would be a worthwile endeavor and maybe even possible without boiling the oceans.
- kenmacd 2y agoYour base assumption here is that the 'crap' is actually 'junk'. Let's look at the easy one here, languages. Talk to someone that speaks multiple languages and they'll have examples of concepts in one language that are difficult to express in another. The multilingual person, or someone who just speaks a different language than you, will think differently[1]. Does the LLM take advantage of this? I don't know. It wouldn't surprise me if it did, and if it doesn't now I'd bet it will in the future. Either way though, throwing away those other languages could make the model dumber. As you allude to, there's a balance between intelligence and knowledge. (in case you hadn't thought of it, those 'tools' can also be other LLMs with more specialized knowledge in a particular field. For example a 'translator' model) Other 'facts' could also have more merit than it would first appear. Sure, one particular person's shoe size might not be needed, but if you were to filter out shoe sizes in general then the model might not be able to suggest how to find properly fitting footwear, or might not suggest that your back pain could be related to your shoes. > That would be a worthwile endeavor and maybe even possible without boiling the oceans. I think it's important to keep in mind that we're very early in the AI journey. Look at the power requirements of early computers versus the ones we use today. I'm all for keeping energy usage in mind, but I'd be careful with hyperbolic language as things are changing so quickly. Tasks that would have taken multiple GPUs can now run on my laptop CPU. [1] https://www.edge.org/conversation/lera_boroditsky-how-does-our-language-shape-the-way-we-think https://www.edge.org/conversation/lera_boroditsky-how-does-o...
- dangoodmanUT 2y ago> Prompt: Which is larger, .9 or .11? > Result: .9 is larger than .11 we've broken the semver barrier!
- deleted 2y ago[deleted]
- bofadeez 2y agoNot going to work - https://arxiv.org/abs/2310.01798 https://arxiv.org/abs/2310.01798
- bofadeez 2y agoYou can reproduce both of those responses zero shot on 70B with "Let's verify step by step" appended at the end.
- zby 2y agoI am always looking for definitions of "reasoning". My theory is that if we find a good definition - then it will turn out that we can build systems that would combine fuzzy llm thinking with classical algorithms to solve "reasoning". All the problems with llm not reasoning (like planning, counting letters or deductive inference) are easy for classical algos. There needs to be a way to split the thinking process into two parts and then execute each part on the appropriate model.
- imtringued 2y agoSolving a decidable problem is a large subset of reasoning tasks. Counting is also a critical reasoning task, since it requires you to both understand natural numbers and the concept of distinct instances of objects belonging to a general category. Two centuries ago there were no computers, everything had to be done by humans. Get to that level first before you whip out code.
- londons_explore 2y ago> This alone, without any training, is sufficient to achieve ~70% accuracy on the Strawberry problem (n=10, "How many Rs are in strawberry?"). Without prompting, Llama-3.1-70b had 0% accuracy and ChatGPT-4o had 30% accuracy. I think this class of problem might be better solved by allowing the LLM to 'zoom in' and view the input differently. Rather like you might peer closer for more detail if someone asked you about the print quality of something you were reading. 'zoom in' could input the same text letter by letter, or even in image form (rasterize the text) to help answer questions like "How many letters in the word strawberry contain straight lines?"
- Haskell4life 2y ago[dead]
- pseudotensor 2y agoThis is related: https://www.reddit.com/r/LocalLLaMA/comments/1fiw84a/open_strawberry/ https://www.reddit.com/r/LocalLLaMA/comments/1fiw84a/open_st... The idea is not silly in my view, I did something similar here: https://github.com/pseudotensor/open-strawberry https://github.com/pseudotensor/open-strawberry The idea is that data generation is required first, to make the reasoning traces. ToT etc. are not required.