Few-shot prompting is a pretty common technique used for LLMs. By providing a few examples of your data in the prompt, the model learns "on the fly" and produces better results -- but what happens if the examples you provide are error-prone?
I spent some time playing around with Open AI's davinci LLM and I discovered that real-world data is messy and full of issues, which led to poor quality few-shot prompts and unreliable LLM predictions.
This article explains my methodology and how I used data-centric AI to automatically clean the messy few-shot examples pool and increased model performance by 30%.
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[ 4.7 ms ] story [ 17.3 ms ] threadI spent some time playing around with Open AI's davinci LLM and I discovered that real-world data is messy and full of issues, which led to poor quality few-shot prompts and unreliable LLM predictions.
This article explains my methodology and how I used data-centric AI to automatically clean the messy few-shot examples pool and increased model performance by 30%.