I thought this would be about which is more useful in specific scenarios.
I'm always surprised that when it comes to "how useful are LLMs" the answers are often vibe-based like "I asked it this and it got it right". Before LLMs, information retrieval and machine learning were at least somewhat rigorous scientific fields where people would have good datasets of questions and see how well a specific model performed for a specific task.
Now LLMs are definitely more general and can somewhat solve a wider variety of tasks, but I'm surprised we don't have more benchmarks for LLMs vs other methods (there are plenty of LLM vs LLM benchmarks).
Maybe it's just because I'm further removed from academia, and people are doing this and I don't see?
I'm always surprised that when it comes to "how useful are LLMs" the answers are often vibe-based like "I asked it this and it got it right". Before LLMs, information retrieval and machine learning were at least somewhat rigorous scientific fields where people would have good datasets of questions and see how well a specific model performed for a specific task.
Now LLMs are definitely more general and can somewhat solve a wider variety of tasks, but I'm surprised we don't have more benchmarks for LLMs vs other methods (there are plenty of LLM vs LLM benchmarks).
Maybe it's just because I'm further removed from academia, and people are doing this and I don't see?