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To be fair LLMs are predicting the next token. It's just that to get better and better predictions it needs to understand some level of reasoning and math. Howe
by rdedev 3y ago
To be fair LLMs are predicting the next token. It's just that to get better and better predictions it needs to understand some level of reasoning and math. However it feels to me that a lot of this reasoning is brute forced from the training data. Like chatgpt gets some things wrong when adding two very large numbers. If it really knew the algorithm for adding two numbers it shouldn't be making them in the first place. I guess same goes for issues like hallucinations. We can keep pushing the envelope using this technique but I'm sure we will hit a limit somewhere
- chaxor 3y agoOf course it predict the next token. Every single person on earth knows that so it's not worth repeating at all. As for the fact that it gets things wrong sometimes - sure, this doesn't say it actually learned every algorithm (in whichever model you may be thinking about). But the nice thing is that we now have this proof via category theory, and it allows us to both frame and understand what has occurred, and to consider how to align the systems to learn algorithms better.
- rdedev 3y agoThe fact that it sometimes fails simple algorithms for large numbers but shows good performance in other complex algorithms with simple inputs seems to me that something on a fundamental level is still insufficient
- zamnos 3y agoInsufficient for what? Humans regularly fail simple algorithms for small numbers, nevermind large numbers and complex algorithms
- starlust2 3y agoYou're focusing too much on what the LLM can handle internally. No LLMs aren't good at math, but they understand mathematic concepts and can use a program or tool to perform calculations. Your argument is the equivalent of saying humans can't do math because they rely on calculators. In the end what matters is whether the problem is solved, not how it is solved. (assuming that the how has reasonable costs)
- ipaddr 3y agoHumans are calculators
- glitcher 3y ago> Of course it predict the next token. Every single person on earth knows that so it's not worth repeating at all What's a token?
- visarga 3y agoA token is either a common word or a common enough word fragment. Rare words are expressed as multiple tokens, while frequent words as a single token. They form a vocabulary of 50k up to 250k. It is possible to write any word or text in a combination of tokens. In the worst case 1 token can be 1 char, say, when encoding a random sequence. Tokens exist because transformers don't work on bytes or words. This is because it would be too slow (bytes), the vocabulary too large (words), and some words would appear too rarely or never. The token system allows a small set of symbols to encode any input. On average you can approximate 1 token = 1 word, or 1 token = 4 chars. So tokens are the data type of input and output, and the unit of measure for billing and context size for LLMs.
- agentultra 3y agoAnd LLMs will never be able to reason about mathematical objects and proofs. You cannot learn the truth of a statement by reading more tokens. A system that can will probably adopt a different acronym (and gosh that will be an exciting development... I look forward to the day when we can dispatch trivial proofs to be formalized by a machine learning algorithm so that we can focus on the interesting parts while still having the entire proof formalized).
- chaxor 3y agoYou should read some of the papers referred to in the above comments before making that assertion. It may take a while to realize the overall structure of the argument, how the category theory is used, and how this is directly applicable to LLMs, but if you are in ML it should be obvious. https://arxiv.org/abs/2203.15544 https://arxiv.org/abs/2203.15544
- agentultra 3y agoThere are methods of proof that I'm not sure dynamic programming is fit to solve but this is an interesting paper. However even if it can only solve particular induction proofs that would be a big help. Thanks for sharing.
- zootreeves 3y agoYou know the algorithm for arithmetic. Are you telling me you could sum any large numbers first attempt, without any working and in less than a second 100% of the time?
- uh_uh 3y agoBoth of these statements can be true: 1. ChatGPT knows the algorithm for adding two numbers of arbitrary magnitude. 2. It often fails to use the algorithm in point 1 and hallucinates the result. Knowing something doesn't mean it will get it right all the time. Rather, an LLM is almost guaranteed to mess up some of the time due to the probabilistic nature of its sampling. But this alone doesn't prove that it only brute-forced task X.
- visarga 3y ago> If it really knew the algorithm for adding two numbers it shouldn't be making them in the first place. You're using it wrong. If you asked a human to do the same operation in under 2 seconds without paper, would the human be more accurate? On the other hand if you ask for a step by step execution, the LLM can solve it.
- catchnear4321 3y agoam i bad at authoring inputs? no, it’s the LLMs that are wrong.
- throwuwu 3y agoCreate two random 10 digit numbers and sit down and add them up on paper. Write down every bit of inner monologue that you have while doing this or just speak it out loud and record it. ChatGPT needs to do the same process to solve the same problem. It hasn’t memorized the addition table up to 10 digits and neither have you.
- gremlinsinc 3y agothis is one thing makes me think those claiming "it isn't AI" are just caught up in cognizant dissonance. For llm's to function, we have to basically make it reason out, in steps the way we learned to do in school, literally make it think, or use inner monologue, etc.
- ahoya 3y agoThis is not at all how it works. There is no inner monologue or thought process or thinking happening. It is just really good at guessing the next word or number or output. It is essentially brute forcing.
- throwuwu 3y agoIt is funny. Lots of criticisms amount to “this AI sucks because it’s making mistakes and bullshitting like a person would instead of acting like a piece of software that always returns the right answer.” Well, duh. We’re trying to build a human like mind, not a calculator.