Hallucinations are a big problem when working with LLMs.
We made a simple Colab template to help you verify your responses from LLMs.
The template uses Chain-of-Verification (CoVe), a prompt engineering technique developed from research at Meta AI. CoVe enhances the accuracy of LLMs by generating a baseline response to a user query and then validating the information through a series of verification questions. This iterative process helps to correct any errors in the initial response, leading to more accurate and trustworthy outputs.
The template is backed by AIConfig - a JSON serializable format for models, prompts, and model parameters. You can easily switch out the models used for this template and use the AIConfig directly in your application code.
Try it here: colab notebook
Please star our repo for AIConfig! It's our first open-source project and we're thrilled to share it with you. ⭐️
Top comments (3)
Thanks for sharing! this looks very interesting. Will give it a try.
interesting topic. I want to know more!
Where can I read up about this?
Thank for sharing!