5 ms·
LLMs can see and hear without any training
- underdeserver 1y agoPaper: https://arxiv.org/pdf/2501.18096 https://arxiv.org/pdf/2501.18096
- suddenlybananas 1y agoI don't understand how the title relates to the content of this article at all. They're even using CLIP which definitely has been trained.
- dragonwriter 1y agoYou don't have to train the LLM soecifically for the tasks and even the auxiliary tools aren't trained on the tasks they are used as scorers for (because they aren't doing the task,just evaluating how well the LlM is), so there is no task-specific training.
- suddenlybananas 1y agoTask-specific training sure, but the title implies that vision itself is not trained.
- jagged-chisel 1y agoComputers can receive input without any programming. Not sure what’s interesting here.
- fortran77 1y agoReally? How?
- skydhash 1y agoThe base layer is just electronic circuitry. As long there is electricity it will do stuff (like a radio producing noise). GPU, CPU, is mostly software embedded in hardware.
- barotalomey 1y agoPrimarily, processing input.
- HaZeust 1y agoLogic gates aren't coding? Could have fooled me!
- amelius 1y agoThere's more to seeing and hearing than just receiving inputs. Anyway, this looks like a case of human trying to understand article without reading it.
- lud_lite 1y ago[flagged]
- deleted 1y ago[deleted]
- dragonwriter 1y agoThis isn't receiving input, its generating output competitive with models with task-specific training. I’m guessing the iterative approach burns a lot of tokens though, though that may not matter too much with 8B Llama as the LLM.
- scribu 1y agoThis seems to be a system to generate better prompts to be fed into a base multimodal model. Interesting, but title is definitely clickbait.
- wangii 1y agoExactly! There is definitely something wrong with FAIR.
- deleted 1y ago[deleted]
- throwaway4aday 1y agoThey only did that for image generation. The more interesting part is that an LLM can approach or find the correct caption for an image, video or audio during test time with no training using only the score as a guide. It's essentially working blind almost like the game Marco Polo where the scorer is saying "warmer" or "colder" while the LLM is finding its way towards the goal. This is an example of emergent capabilities since there are no examples of this in the training data.
- matt123456789 1y agoActually, it's the name of the paper. And while the team also developed and released a system to elicit the behavior by doing what you described, it's entirely possible that the researchers thought the title to be the most important finding in their work.
- EncomLab 1y agoMy photoresistor nightlight can "see" that it is dark and it "knows" to turn on the light - not only does it not have training, it does not have any code! And if you think that is amazing, my bi-metallic strip thermostat "feels" the temperature and then modifies the environment because it "knows" if it's hot to turn on the A/C, and if it's cold to turn on the heat - no training or code! All of this AI stuff is just unbelievably incredible - what a brave new world (of word games)!
- JoBrad 1y agoThe nightlight and thermostat's response to stimulus is nowhere near analyzing a picture of a clock tower and responding with "Image of a city's tallest, historic landmark with a sepia filter." To me, recognizing the umbrella in the spoon is one of the most impressive items they list.
- EncomLab 1y agoIt's not the technology that is bad - it's the extreme anthropomorphizing language that's used to describe it.
- horacemorace 1y agoIt might be bad if its behavior wasn’t so anthropomorphic.
- bamboozled 1y agoThese devices are still "recognizing" something, which is quite interesting in itself.
- viraptor 1y agoThat looks like a classic Actor/Critic setup, yet it's not mentioned even once in the paper. Am I missing some large difference here?
- oneseven 1y agoYes, apparently they've developed new names: Generator and Scorer. This feels a bit like "Tai's Model" https://news.ycombinator.com/item?id=17863514 https://news.ycombinator.com/item?id=17863514
- lukeinator42 1y agoHaha "Tai's Model" is absolutely hilarious, that gave me a good chuckle. I checked and it currently is cited 568 times.
- dawnofdusk 1y agoIn actor/critic the actor and critic are normally learned, i.e., their weights are adjusted during the process. The paper is correct that their method is zero-shot, but it doesn't mention that their method is essentially equivalent to a few rounds of training but then discarding the training update. Anyone who works with deep architectures and momentum-based optimizers knows that the first few updates alone provide large improvements in loss. In this paper the breakthrough is that computing these first few updates at test time enables one to describe the algorithm as "without training" and therefore attract hype.
- fc417fc802 1y ago> discarding the training update But they aren't updating the model weights. They're iteratively updating the prompt. It's automating the process that humans use with generative models. Agreed that it's conceptually equivalent though.
- lngnmn2 1y ago[dead]
- deleted 1y ago[deleted]
- JoBrad 1y agoExactly how little training is "without any"? I'm assuming that companies haven't been spending billions trying to train LLMs to better understand things when they can do it without any training.
- blogabegonija 1y ago[dead]
- 3rdworldeng 1y agoFind me Jose Monkey will do that too :-)
- sega_sai 1y agoThe paper certainly contradicts my expectation from the title. I.e. it does not present an LLM that can generate images without any access to images before.
- vessenes 1y agoI’ve read the paper and the skeptical comments here, to wit: it’s just an actor/critic pipeline by another name. I’ll bite and say this is actually interesting — and the paper title is misleading. What they’ve done here is hooked up a text-only LLM to multimodal critics, given it (mostly) an image diffusion generation task, and asked it to improve its prompting of the multimodal generation by getting a set of scores back. This definitely works, based on their outputs. Which is to say, LLMs can, zero shot, with outside tool feedback, iteratively improve their prompting using only that tooling feedback. Why is this interesting? Well, this did not work in the GPT-3 era; it seems to do so now. I see this as an interesting line to be added in the ‘model capabilities’ box as our models get larger and more sophisticated — the LLMs can perform some sort of internally guided search against a black box generator and use a black box scorer to improve at inference time. That’s pretty cool. It’s also generalizable, and I think is worth keeping in mind on the stack of possible approaches for, say agentic coding, that you can use a critic to not just ‘improve’ generated output, but most likely do some guided search through output space.
- nightski 1y agoAre they using the same diffusion models as the GPT-3 area? Meaning is it the LLM that has improved or is it the diffusion model? I know it's probably a foolish take but I am really skeptical of the "larger models will solve all our problems" line of thinking.
- vessenes 1y agoThey don’t compare in the paper. I will say I experimented extensively with GPT-3 era LLMs on improving ouput by trying to guide early diffusion models with critical responses. It was a) not successful, and b) pretty clear to me that GPT-3 didn’t “get” what it was supposed to be doing, or didn’t have enough context to keep all this in mind, or couldn’t process it properly, or some such thing. This paper has ablations, although I didn’t read that section, so you could see where they say the effectiveness comes from. I bet you thought that it’s emergent from a bunch of different places. FWIW, I don’t think LLMS will solve all our problems, so I too am skeptical of that claim. I’m not skeptical of the slightly weaker “larger models have emergent capabilities and we are probably not done finding them as we scale up”.
- nico 1y agoTo people curious or skeptical if this could be called “seeing” or “hearing”, I recommend listening to the Batman podcast episode on NPR (https://www.npr.org/2015/01/23/379134306/batman-pt-1 https://www.npr.org/2015/01/23/379134306/batman-pt-1) Through the story and experience of a blind man, they end up getting into the question of what does it mean to see The podcast is pretty straightforward, but it does end up showing that defining “seeing” is a philosophical question, rather than a simple obvious answer
- TheCoreh 1y agoIs the LLM essentially playing "Wordle" with an external system that rates the quality of its output, gradually climbing the score ladder until it produces good results?
- robocop_legacy 1y agoI think there is potentially a powerful method here. Specifically, the optimal context for a given task can be saved and a meta-learner can be trained to map the task to the context. This would allow fine tuning a model for some specific task without retaining the LLM. For example, generating an SEM image with of some material with a specified porosity and grain size.
- v-rt 1y ago"without training" describes transfer learning
- deleted 1y ago[deleted]
- v01rt 1y ago"without training" describes transfer learning with an actor / critic approach
- alex1138 1y agoI just remember Zuck's comments about AI and how the idea of it dooming our species is a bit silly, etc This is the wrong approach to take. At minimum you have to say things like "well yes we're always on the lookout for this kind of thing". With him? Not a care in the world
- gitroom 1y agopretty cool seeing models get a bit smarter each time - always makes me wonder how much of this is luck vs real skill tbh
- qgin 1y agoEmergent capabilities have been one of the wildest developments in software. For most traditional programmers you learn quickly and with great pain that the computer only does what you explicitly program it to do, no more, no less, and unintended behavior is a bug (and if you’re lucky, an accidental feature). But the idea that entire abilities just emerge from scale… I still have a hard time accepting it.