Llms look for patterns in their training data. So like if you asked 2+2= it would look its training and finds high likelihood the text that follows 2+2= is 4. Its not calculating, its finding the most likely completion of the pattern based on what data it has.
So its not deconstructing the word strawberry into letters and running a count… it tries to finish the pattern and fails at simple logic tasks that arent baked into the training data.
But a new model chatgpt-o1 checks against itself in ways i dont fully understand and scores like 85% on international mathematic standardized test now so they are making great improvements there. (Compared to a score of like 14% from the model that cant count the r’s in strawberry)
Over simplification but partly it has to do with how LLMs split language into tokens and some of those tokens are multi-letter. To us when we look for R’s we split like S - T - R - A - W - B - E - R - R - Y where each character is a token, but LLMs split it something more like STR - AW - BERRY which makes predicting the correct answer difficult without a lot of training on the specific problem. If you asked it to count how many times STR shows up in “strawberrystrawberrystrawberry” it would have a better chance.
Its 3 right? Am i real? Why can’t ai guess that one?
Llms look for patterns in their training data. So like if you asked 2+2= it would look its training and finds high likelihood the text that follows 2+2= is 4. Its not calculating, its finding the most likely completion of the pattern based on what data it has.
So its not deconstructing the word strawberry into letters and running a count… it tries to finish the pattern and fails at simple logic tasks that arent baked into the training data.
But a new model chatgpt-o1 checks against itself in ways i dont fully understand and scores like 85% on international mathematic standardized test now so they are making great improvements there. (Compared to a score of like 14% from the model that cant count the r’s in strawberry)
Over simplification but partly it has to do with how LLMs split language into tokens and some of those tokens are multi-letter. To us when we look for R’s we split like S - T - R - A - W - B - E - R - R - Y where each character is a token, but LLMs split it something more like STR - AW - BERRY which makes predicting the correct answer difficult without a lot of training on the specific problem. If you asked it to count how many times STR shows up in “strawberrystrawberrystrawberry” it would have a better chance.