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[D] With LLMs hallucinating nature, how do we create a credible production ready application? (alien.top)

submitted 1 year ago by software-n-erd@alien.top to c/machinelearning@academy.garden

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I want to use LLMs to automate analysing data and use it to provide insights to my users, but often times I notice insights being generated on factually incorrect data. I tried fine tuning my prompts, the structure in which I pass data to LLM, few shot learning but there still some chance of it to hallucinate. How can I create a production ready application where this insights are surfaced to end users and presenting incorrect insights is not accepted? I am out of ideas. Any guidance is appreciated 🙏🏻

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[–] UndocumentedMartian@alien.top 1 points 1 year ago (1 children)

By not using LLMs to do the modelling. Use specialized models for data analysis and use an LLM to orchestrate those models and communicate with the user. LLMs are not cheap to run, though, so you may want to do a cost/benefit analysis.

[–] software-n-erd@alien.top 1 points 1 year ago

Gotcha, I was honestly not aware of any data analysis models. Have you ever used any of them which you think I should look at?