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An example of how to apply the noun-phrase route collision avoidance to a real-world problem is given at the end of the video. Kindly see: youtu.be/ZBWoUVZuGao?t=…. Also, since writing the above post, I've released an API to fully automate the process, and I also finished the video which explains what types of chatbots can be built using…
An example of how to apply the noun-phrase route collision avoidance to a real-world problem is given at the end of the video. Kindly see: https://youtu.be/ZBWoUVZuGao?si=ublEF50jxfookXYn&t=902. Also, since writing the above post, I've released an API to fully automate the process, and I also finished the video which explains what types of chatbots can be built using the API: https://youtu.be/K4Wg6QzPfyI. The API is released at https://www.ragfix.ai. Not only are free tokens available, but it is priced just high enough to cover costs to make the automated solution available to everyone.
Provided the API is used properly, it returns 100% accurate responses every time. To document this, I've posted the API response to all the Evident Conflict and Subtle Conflict hallucinations in the RAGTruth corpus for GPT-4 and GPT-3.5-Turbo. The same model that responds with a hallucination returns a 100% accurate response to the same query and same passages. Simply remove the noun-phrase route collisions and the models work perfectly. For documentation see: https://hallucination-analyzer.ragfix.ai/.
By removing noun-phrase route collisions, we don't need bigger models to achieve 100% accuracy. In fact, I am now testing GPT-4o. So far every response has come back 100% accurate — including all the hallucinations in the RAGTruth corpus.
Kindly let me know if you need any help.