I'm really interested in understanding how large language models (LLMs) work. If they primarily function as word predictors, how are they able to tackle coding tasks and solve mathematical equations? I'm curious about the mechanics behind this process and what happens under the hood, especially when they seem to perform quite well at these tasks.
4 Answers
If you're looking for deeper insights into how these models work, check out research papers like 'Attention Is All You Need'. They lay out the foundations behind the technology.
They mostly rely on having been trained on tons of examples rather than really solving problems. For instance, if you ask them to complete a simple math phrase like "7 + 14 =", they can predict the answer fairly easily, but they don't genuinely do the math like we do.
Honestly, asking about LLM performance here might not get you the best answers. Consider looking at more specialized sources to get a real understanding. There’s some fascinating research from Anthropic that dives into the limitations and capabilities of LLMs.
It's true that LLMs can struggle with code and math sometimes. However, they actually do pretty well overall. I once needed to create a VBA script for Excel, and when I described what I wanted to Copilot, it handled it perfectly! So, there is more to it than just luck.
Exactly! While they are not perfect and don't truly 'understand' math or code, their training on massive datasets helps them guess correctly most of the time.
Right, they're advanced text generators, not calculators. They mimic our reasoning based on patterns from their training.