Even if you love to code, there probably are times when you'd rather ask a question like, "What topics generated the highest reader interest this year?" than write an SQL query with phrases like STRFTIME('%&Y', Date) = STRFTIME('%Y', 'now'). And, if your data set has dozens of columns, it's nice to be able to avoid looking up or remembering the exact name of each one.
If you ask a large language model (LLM) like ChatGPT or Google Gemini to solve your customers' pain points, it will give you an answer based on the easiest-to-verify information. That often includes published articles, consistent founder commentaries, structured product pages and other third-party references. If those answers do not include your brand, these learning models default to featuring your competitors.
The TnT-LLM framework improves taxonomy generation and text classification by combining a zero-shot approach for taxonomy creation with pseudo-labeling from LLM outputs.