Text classification is the most boring LLM feature, with use-cases on every corner
Something that not many companies did before (other than cool ones): large-scale data labeling is becoming ubiquitous with major advancements in generative LLMs, and dirty cheap also.
“ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks" [link]
“AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators” [link]
As the models become as good or better than human writers, a hypothesis that they could also label better than crowd-workers also seems to become true. And that actually feels like a big thing.
If foundational models do still get trained with human feedback (reportedly, the major source of advancements of GPT-3 → GPT-3.5), it’s more unclear if that role still has relevance for simpler scenarios: labeling products, reviews, feedback, prices, etc.
And one could argue, that with the process being (not now, but tomorrow) as easy as connecting Excel files, almost every company will find 1-2-N cases where it could bring value now.
I’ll share a few examples that come to mind.
Commoditization examples:
Businesses that should see some change quickly:
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Bit of a future (more obvious one though):
Probably even healthcare will see the wide use of it someday.
Fuzzy personalization systems:
So yeah, have a look around if free input things are still used somewhere in your company - there could be some hidden gold.
Last, but not least - I do remember to separate solutions in search of a problem and platform shift.
The former is rather a bad pattern that rarely works, the latter enables you to solve important problems which didn’t seem feasible at all.
Those problems could be additional explainability (if you’d better describe products, catalog, maps, etc.), automation/speed (ticket classification), non-formal aggregation, lots of things really, lots to explore and build.