We often ignore the importance of using good baseline systems and jump to the latest shiny thing.
I had a similar experience few years back when participating in a ML competitions [1,2] for detecting and typing phrases in a text. I submitted an approach based on Named Enttiy Recognition using Conditional Random Field (CRF) which has been quite robust and well known in the community and my solution beat most of tuned Deep learning solutions by quite a large margin [1].
I think a lot of folks underestimate the complexity of using some of these models (DL, LLM) and just throw them at the problem or don't compare it well against well established baselines.
I had a similar experience few years back when participating in a ML competitions [1,2] for detecting and typing phrases in a text. I submitted an approach based on Named Enttiy Recognition using Conditional Random Field (CRF) which has been quite robust and well known in the community and my solution beat most of tuned Deep learning solutions by quite a large margin [1].
I think a lot of folks underestimate the complexity of using some of these models (DL, LLM) and just throw them at the problem or don't compare it well against well established baselines.
[1] https://scholar.google.com/citations?view_op=view_citation&h... [2] https://scholar.google.com/citations?view_op=view_citation&h...