Part 3/7:
The innovative process involved creating a multilingual censorship classifier, which acts similarly to spam identification algorithms, to determine which user prompts would likely trigger censorship. Perplexity analysts gathered a diverse set of 40,000 multilingual prompts related to these topics, facilitating the classification process.
After gathering this extensive prompt dataset—excluding personal identifiable information—Perplexity strived to generate accurate, factually correct answers. A unique aspect of R11 1776 is its inclusion of a Chain of Thought reasoning model, allowing the AI to provide contextually accurate answers while avoiding typical censorship deterrents.