6 ms·
>> Because this paper introduces their alternative method which simply runs the same query multiple times on the same LLM, without any context shared across que
by zer00eyz 3y ago
>> 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.
https://en.wikipedia.org/wiki/Lorenz_system https://en.wikipedia.org/wiki/Lorenz_system
Years ago weather simulations started tweaking input params and running their models over and over. Discarding outliers, taking averages. It works pretty well.
Because LLM's mostly have random seeds (aka temperature) feeding them the same input and averaging the output is going to get you a better guess.
Lorenz also gives some clues (if not an outright explanation) as to why the "hallucination" problem is likely unsolvable.
If you buy into this line of thinking then it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. Simulating isnt emulating... an LLM is as likely to become intelligent as a forecast is to control the weather.
- ekianjo 3y ago> LLM's are more or less a dead end when it comes to AGI. I don't think many people believe that LLMs are a way to AGI (whatever that actually means). But LLMs can still have many valid uses even if their prospects are limited in scope.
- zer00eyz 3y ago>> I don't think many people believe that LLMs are a way to AGI Please tell Sam Altman ASAP Thanks!
- dartos 3y agoYou think he doesn’t know? Everything he says is marketing for OpenAI. Same as any other CEO with their company.
- buu700 3y agoI recently read an interesting thread that laid out the case for LLMs being a path to AGI: https://old.reddit.com/r/singularity/comments/13ox85j/how_does_an_llm_even_work/jl6unp4 https://old.reddit.com/r/singularity/comments/13ox85j/how_do... The argument boils down to the idea that language isn't simply strings of words or bits of factual information, but an actual encoding of logic. By training statistical models on vast amounts of logic, we've given them a generalizable ability to perform logic. A sufficiently advanced LLM could thus potentially fulfill some definition of AGI. To be clear, this doesn't in any way imply that LLMs could ever fit the definition of artificial consciousness, which would be a completely different form of strong AI. They're effectively just mathematical functions (albeit extremely complicated ones), which simply take inputs and return outputs without any intervening subjective experience. Even if they can perform a complicated task, retrieve and effectively summarize complicated information, or say all the right things as a conversational partner, they have no concept of the meaning of their output. Maybe that limitation in itself puts a ceiling on their potential. Maybe the best possible LLM can only ever be 99.99% effective, and that 0.01% of the time it will go completely off the rails and disregard its instructions or hallucinate something ridiculous. Maybe the only way to overcome that is by keeping a human or a true artificial consciousness in the loop, in which case LLMs would still be extremely useful, but a flawed AGI if "AGI" at all. Or maybe a sufficiently advanced LLM and/or a sufficiently advanced error correction architecture will actually be enough to mitigate those issues. I don't have a strong opinion on where LLMs are ultimately headed, but I'm looking forward to seeing how it all unfolds. It's amazing how capabilities that were strictly in the realm of sci-fi so quickly became mundane.
- samus 3y agoLLMs are definitely here to stay. Even if they don't turn out to be the road to AGI, they can be used by all sorts of sub-AGI agents as a "language centre". An encoder can be used to extract meaning from input, and an autoregressive decoder conditioned on the agent's internal state can be used to keep a conversation going. What's not clear at all is whether the traditional transformer architecture will endure.
- DiogenesKynikos 3y ago> They're effectively just mathematical functions (albeit extremely complicated ones), which simply take inputs and return outputs without any intervening subjective experience. So are human brains, which are subject to the laws of physics, and which work just as mechanistically as any computer. Unless you hold a dualist view that the brain accesses a spiritual realm outside of the physical world, then the fact that a computer operates mechanistically does not mean that it lacks consciousness.
- buu700 3y agoThe process of a human responding to a prompt isn't the same process an LLM follows. It involves subjectively experiencing being asked the question, having feelings about the question, possibly visualizing something related to the question, possibly reflecting on memories, wondering about how possible answers might be received and affect their future reputation, expressing their answer with a range of different emotions, and so on. There may be aspects of the brain that behave like statistical models, but the broader system seems more complex than that. I don't see that as in any way inherently spiritual. I expect that it could be artificially reproduced one way or another, but would be extremely complicated.
- DiogenesKynikos 3y ago> The process of a human responding to a prompt isn't the same process an LLM follows. It's not the same process, but it is a deterministic function, which was one of your objections to LLMs. Humans operate according to physical laws, after all.
- aerhardt 3y agoThere are plenty of people - technical and non-technical - who seem to be acting like AGI is right around the corner thanks to LLMs, and who are, more broadly, vastly overstating the current capabilities of LLMs. I’m observing this in real life as much as on the internet. There are two very distinct groups of people that stand out to me: (1) High level execs with vested interests around AI and (2) Managers who haven’t even bothered to create an OpenAI account and are asking their subordinates to use ChatGPT for them, in what is an unforeseen usage of LLMs: by human proxy.
- nuancebydefault 3y agoI think you are missing a step. A lot of people believe AI will advance so much that it will be indistinguishable from the best possible human reasoning. The evolution of LLMs just give us a clue of the speed of improvement of AI. That does not mean that LLMs, which are one form of AI, will become AGI. It is just one path that AI is following. It will probably become a subset of something more advanced.
- DiogenesKynikos 3y agoLLMs already are intelligent. They're not the same as humans, but they are able to give intelligent answers to highly nontrivial questions.
- hackable_sand 3y agoI have yet to see an LLM that is cooperative. The magic of collaborating with someone is that we can both understand the problem and reason about it. The current degree of LLM intelligence is not compelling for a social creature like me.
- theendisney 3y agoIs it even allowed to ask questions?? Edit: my sience fiction joke in the 90s was AI though bots chatting in irc channels. They could seemlesly integrate human intelligence that way.
- ben_w 3y agoSurprised to read that. I use them as a cooperative partner by default. Also: quite a few people have had instances work with other instances, sometimes of the same model and sometimes of other models.
- hackable_sand 3y agoCooperation is more than an i/o loop. Layering and pooling models is nice though. Perhaps "conceptualization" is the indicator here.
- ben_w 3y agoPerhaps I'm up too late, but I can't think what else is there to cooperation besides two or more agents doing things in alignment with some goal? (Regardless of who or what sets that goal). Also I don't know what you mean by "conceptualization".
- RyEgswuCsn 3y agoExcept that a weather forecasting model can't experiment on weather, but a LLM system may be designed to be able to perform experiments and take feedbacks?
- Terretta 3y ago> it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. On the contrary, sit and listen in a college cafeteria, and it quickly becomes apparent most conversation participants are LLMs.* > Simulating isnt emulating... These are not synonyms, true. > an LLM is as likely to become intelligent as a forecast is to control the weather. I don't see uncertainty of intelligence as a property of an LLM as being equivalent to certainty of weather control as a effect of a forecast. Among other things, whether weather was controlled would tend to be agreed by all observers, while it's often unclear if intelligence is being observed in these threads. :-) --- * While my last line was a joke, humans in LLM mode was not. We can drive on autopilot, and get where we need to go while not being able to remember how we got there. We definitely converse on autopilot, indistinguishably from LLMs talking to each other, after an opening line every word of every sentence in the entire exchange perfectly predictable to a stranger. Are the speakers intelligent? What about the stranger who knows what they will say next? To say LLMs are not intelligent is easier if we agree humans spend a good deal of time being unintelligent.
- hooande 3y agoLLMs were specifically trained to emulate human interaction patterns. Of course we sound like them at times. It's the things we can do that they can't that are relevant. If I study Einstein and learn to do a really good impression, the statement "Einstein often sounds like karmacondon" will be true. That does not make me Einstein.
- OJFord 3y ago> If I study Einstein and learn to do a really good impression, the statement "Einstein often sounds like karmacondon" will be true. Wrong alt, hooande ;)
- zer00eyz 3y ago>>> I don't see uncertainty of intelligence as a property of an LLM as being equivalent to certainty of weather control as a effect of a forecast. GTA 5 is a simulation. Do you expect to be arrested out side your front door for the car you stole in game? Weather forecasting is a simulation, it tells you what the weather will look like in the next few days. It gets better as we get more sensors, collect more data and build more accurate models based on those two factors. It will never leap to weather. Language forecasting (because this is what an LLM is) is a simulation. It tells you what the next token (word) will be based on what came before it. It gets better as we collect more data and hone and refine these models. It will never make the leap to intelligence. >> To say LLMs are not intelligent is easier if we agree humans spend a good deal of time being unintelligent. To say that LLMs are intelligent means that language is a requirement for intelligence. Thats some fairly magical thinking... buy any sufficently advanced technology...
- ben_w 3y ago> If you buy into this line of thinking then it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. Simulating isnt emulating... an LLM is as likely to become intelligent as a forecast is to control the weather Up until this point, I agree. This puts humans on too high a pedestal: LLMs aren't magic, and we're not magic either. (There's other reasons for me to think Transformers aren't the answer, but not this kind of reasoning).
- sangnoir 3y ago> we're not magic either We pretty much are compared to present-day neural architectures. How many simulated neurons and synapses are in the largest architectures, and how do those numbers compare to humans?
- dartos 3y agoIt’s a non starter to assume that virtual “synapses and neurons” behave like ours do. We barely understand how ours works. Also, modern LLMs built on the transformers architecture no longer use the neuron-inspired perceptron style topology for most of their compute. I’ve heard that spiking NNs are supposed to mimic organic brains more closely, but I haven’t read into them much yet.
- samus 3y agoThe attention mechanism is in practice implemented using three linear layers. The matrix multiplication to average the output and to implement the masking is the only non-neuronal part of that computation, but it can be seen as an activation function. Usually, linear perceptrons and ReLUs or GeLUs are used. Due to the enormous compute requirements to evaluate models of interesting size, other types of neuronal networks and activation functions have received very little attention (pun intended) so far.
- dartos 3y agoUsing ReLU instead of sigmoid is a significant departure with regards to how closely it models actual neurons. Using non fully connected layers is as well. Our brains likely aren’t fully connected, but the connections that matter are made stronger through living life and learning. If you squint, it’s kind of like training a dense series of linear layers, but that’s not what we’re doing anymore (for the better) Comparing NNs to organic brains is an apples to oranges comparison, is what I’m saying.