I can’t tell you what I’m working on but I can give you a real world example of where traditional models don’t work well.
Sentiment analysis is like the “Hello World” when you’re using Machine Learning.
But I had a use case similar to a platform like Uber eats where someone can be critical of the service provider or be critical of the platform itself. I needed to be able to distinguish sentiment about the platform based on reviews and sentiment about someone on the platform.
No matter what you do, people are going to conflate the reviews.
As far as costs, I mentioned in another comment that I work with online call centers sometimes. There anytime a person has to answer a call, it costs the company from $2-$5.
One call deflection that saves the company $5 can pay for a lot of inference. It’s literally 100x cheaper at least to use an LLM.
Sentiment analysis is like the “Hello World” when you’re using Machine Learning.
But I had a use case similar to a platform like Uber eats where someone can be critical of the service provider or be critical of the platform itself. I needed to be able to distinguish sentiment about the platform based on reviews and sentiment about someone on the platform.
No matter what you do, people are going to conflate the reviews.
As far as costs, I mentioned in another comment that I work with online call centers sometimes. There anytime a person has to answer a call, it costs the company from $2-$5.
One call deflection that saves the company $5 can pay for a lot of inference. It’s literally 100x cheaper at least to use an LLM.