Over just a few months, ChatGPT went from accurately answering a simple math problem 98% of the time to just 2%, study finds::ChatGPT went from answering a simple math correctly 98% of the time to just 2%, over the course of a few months.
Over just a few months, ChatGPT went from accurately answering a simple math problem 98% of the time to just 2%, study finds::ChatGPT went from answering a simple math correctly 98% of the time to just 2%, over the course of a few months.
Why is “98%” supposed to sound good? We made a computer that can’t do math good
It’s a language model, text prediction. It doesn’t do any counting or reasoning about the preceding text, just completes it with what seems like the most logical conclusion.
So if enough of the internet had said 1+1=12 it would repeat in kind.
Not quite.
Legal Othello board moves by themselves don’t say anything about the board size or rules.
And yet when Harvard/MIT researchers fed them into a toy GPT model, they found that the neural network best able to predict outputting legal moves had built an internal representation of the board state and rules.
Too many people commenting on this topic as armchair experts are confusing training with what results from the training.
Training on completing text doesn’t mean the end result can’t understand aspects that feed into the original generation of that text, and given a fair bit of research so far, the opposite is almost certainly the case to some degree.
This program was designed to emulate the biological neural net of your brain. Oftentimes we’re nowhere near that good at math just off the top of our heads (we need tools like paper and simplifying formulas). Don’t judge it too harshly for being bad at math, that wasn’t its purpose.
This lil robot was trained to know facts and communicate via natural language. As far as I’ve interacted with it, it has excelled at this intended task. I think it’s a good bot
LLMs act nothing like our brains and they aren’t trained on facts.
LLMs are essentially complicated mathematical equations that ask “what makes the most sense as the next word following this one?” Think autosuggest on your phone taken to the extreme limit.
They do not think in any sense and have no knowledge or facts internal to themselves. All they do is compose words together.
And this is also why they’re garbage at math (and frequently lie, and why they can’t “remember” anything). They are simply stringing words together based on their model, not actually thinking. If their model shows that the next word after “one plus two equals” is more likely to be four than three, they will simply answer four.
Err, yes they are. You don’t even need to read a paper on the subject, just go straight to the Wikipedia page and it’s right there in the first line. The ‘T’ in GPT is literally Transformer, you’re highly unlikely to find a Transformer model that doesn’t use an ANN at its core.
Please don’t turn this place into Reddit by spreading misinformation.
Edited, thanks!