keithclift24
commented
3 months ago I tested this idea's effectiveness, manually, with a series of various style questions, and the final consolidated response was better than any of the individual model's response in all the tests. I suppose the model chosen to grade could have a bias toward its own model's response (if it was the model for one of responses). To try to combat that, I created a new thread each time for the evaluation, grading, consolidation process, and didn't state where the answers came from.
enricoros
Best of the Best: Integrating multi-model responses with Peer Review to generate a consolidated response, taking the best elements from each leading model's response to query, improving overall accuracy and quality than from any one individual model.
Why This suggestion is grounded in the goal of enhancing the user experience by providing more accurate, comprehensive, and nuanced responses possible. Users will be able to receive the best possible answers to their queries, synthesized from the strengths of multiple leading language models (LLMs) such as GPT-4 Turbo, Claude Opus, and Gemini Ultra. This approach not only ensures a high quality of responses but also introduces a layer of reliability and depth not achievable by any single LLM. The peer review mechanism by an independent LLM further refines this process, ensuring that users have access to information that is not only diverse but also critically evaluated and consolidated.
Description Integrate multiple leading LLMs (e.g., GPT-4 Turbo, Claude Opus, Gemini Ultra) to simultaneously process a users prompt, and employ an separate additional LLM chat (e.g., ChatGPT or whichever is the leading model at the time) to independently review and grade these responses of 3 different models based on accuracy, relevance, and comprehensiveness. The system then consolidates these insights to deliver a single, optimized response that leverages the collective intelligence and strengths of all participating models.
Requirements