91–100 of 158 posts
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It's interesting that DeepMind still publishes this stuff. OpenAI doesn't publish anything of that sort anymore. DeepMind is more research/publication focused, but this is a disadvantage in a competitive landscape where OpenAI and Anthropic can just apply the results of your paper without giving anything back to the research community.
Anthropic publishes quite a lot too though.
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> but this is a disadvantage in a competitive landscape Or it's a unique advantage because this stuff doesn't happen without good researches who may want: 1) Their name in scientific papers 2) They might actually care about the openess of AI
So far it seems to be a disadvantage as DeepMind has fallen behind OpenAI, despite their size, and to some extent even behind Anthropic.
Playing catch up and trying to attract talent from the hot-new-thing OpenAI requires incentives beyond lots of money. I contend actually being open helps.
I'm sure that's one reason Facebook has an open source model, scientists can care about ethics and could be attracted to openness.
Let'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 lang…
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-o...
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For extra compliance, use 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.
(((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.)))
This 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 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…
Where 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?
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).
This 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 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…
Came here hoping to find this. You will not unlock "o1-like" reasoning by making a model think step by step. This is an old trick that people were using on GPT3 in 2020. If it were that simple, it wouldn't have taken OpenAI so long to release it. Additionally, some of the prompt seems counterproductive: >Be aware of your limitations as an llm and what you can and cannot do. The LLM doesn't have a good idea of its lim…
A LLM has a huge amount of data ingested. It can create character profiles, audience, personas etc.
Why wouldn't it have potentially even learned to 'understand' what 'being aware of your limitations' means?
Right now for me 'change of reasoning' feels a little bit of quering the existing meta space through the reasoning process to adjust weights. Basically priming the model.
I would also not just call it a 'trick'. This looks simple, weird or whatnot but i do believe that this is part of AI thinking process research.
Its a good question though what did they train? New Architecture? More parameters? Is this training a mix of experiments they did? Some auto optimization mechanism?
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Telling LLMs not to hallucinate in their prompt improves the output. https://arstechnica.com/gadgets/2024/08/do-not-hallucinate-t...
I 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.
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
o1’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.
Well, with Tree Of Thought (ToT) and fine-tuned models, I'm sure you can achieve the same performance with margin to improve as you identify the bottlenecks. I'm not convinced OpenAI is using one model. Look at the thinking process (UI), which takes time, and then suddenly, you have the output streamed out at high speed. But even so, people are after results, not really the underlying technology. There is no differen…
According to OpenAI, the model does it's thinking behind the scenes, then at the end summarizes that thinking for the user. We don't get to see the original chain-of-thought reasoning, just the AI's own summary of that reasoning. That explains the output timing.
o1’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.
If 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 reje…
A LLM can therefore have an higher IQ because it can combine all fields.
Also parameters and architecture might or might not be a limiting factor to us humans or a LLM. But LLM and parameter size, optimizations etc. are just at the beginning.
If we now have a good reasoning llm, we can build more test data automatically. Basically using the original content + creating new ones which can then lead to new knowledge = research.
Let'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 lang…
Your 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 woul…
> I think it's important to keep in mind that we're very early in the AI journey.
That's what I am saying. At the moment there is this one really dumb idea, that bigger is better.