4 ms·

As Bluestein pointed you already to this, your question is being addressed by research from the begin on. Its called multi-agent AI, or some sort of.. if you m

As Bluestein pointed you already to this, your question is being addressed by research from the begin on. Its called multi-agent AI, or some sort of.. if you mean like asking certain LLMs the same question and pick the best ones, this, too, have been posted here some days/weeks back. But still, if you're not in the same knowledge domain, even if you get output of 50 different LLMs - how do you know, which one is the preopper/precise answer? No way of finding it out.

amichail 2y ago
What about synthesizing an answer that takes elements from some of the chatbot AIs?
- theGeatZhopa 2y ago
  still no chance. Just think of five blind persons and one one-eyed talking about a movie they saw yesterday. While it may make up perfect sense the guys talking about, its still not possible to find what the one-eyed said. So you take each one's answer and take parts out it synthesize/aligning them all to a perfect sensed prompt. Fed again back into the LLMs, you'll maaaayyyy find whats the proper answer, by finding contradictions in the returns. Just assume, 5 blind & 1 1-eyed' tell you about the movie. You'll get 6 different answers. Now you take them, mix them and feed them back into LLM. What will happen? - may be the 1-eyed will correct you / correct the false parts. So you might know "ok, that one is obviously been in the movie. Because he correct me and the others don't" - may be all of the blinds will correct you and the one-eyed (lets call him cotton' eye joe) dont do it. How you find out which one is correct? What is actually needed is a stress-maker, a "cortex"-LLM and a few of other ML-types for the domain expertise (like math, physics, ...). The cortex is the main LLM that will be prompted. Cortex utilizes domain experts for answering. Stress-maker always keep the cortex busy in the times, when its not used by a prompter, by synthesizing "questions" that has been seen but answered incorrectly before. That will simulate our brain, where we have an stress-maker that keep our brain always in thinking. And the other experts like tasting, sensing, hearing, seeing.. etc. But I don't know this is a good approach to solve the problem right now. Whats needed now is an LLM that can utilize the other LLMs. A multi-translator that understands the syntax and the data of other models. If that is put into existence, than its also possible to build a multi-modal-agent that can fuzz its way into different knowledge-bases and file-formats and what ever. This would be incredible.

amichail 2y ago

What about synthesizing an answer that takes elements from some of the chatbot AIs?

theGeatZhopa 2y ago
still no chance. Just think of five blind persons and one one-eyed talking about a movie they saw yesterday. While it may make up perfect sense the guys talking about, its still not possible to find what the one-eyed said. So you take each one's answer and take parts out it synthesize/aligning them all to a perfect sensed prompt. Fed again back into the LLMs, you'll maaaayyyy find whats the proper answer, by finding contradictions in the returns. Just assume, 5 blind & 1 1-eyed' tell you about the movie. You'll get 6 different answers. Now you take them, mix them and feed them back into LLM. What will happen? - may be the 1-eyed will correct you / correct the false parts. So you might know "ok, that one is obviously been in the movie. Because he correct me and the others don't" - may be all of the blinds will correct you and the one-eyed (lets call him cotton' eye joe) dont do it. How you find out which one is correct? What is actually needed is a stress-maker, a "cortex"-LLM and a few of other ML-types for the domain expertise (like math, physics, ...). The cortex is the main LLM that will be prompted. Cortex utilizes domain experts for answering. Stress-maker always keep the cortex busy in the times, when its not used by a prompter, by synthesizing "questions" that has been seen but answered incorrectly before. That will simulate our brain, where we have an stress-maker that keep our brain always in thinking. And the other experts like tasting, sensing, hearing, seeing.. etc. But I don't know this is a good approach to solve the problem right now. Whats needed now is an LLM that can utilize the other LLMs. A multi-translator that understands the syntax and the data of other models. If that is put into existence, than its also possible to build a multi-modal-agent that can fuzz its way into different knowledge-bases and file-formats and what ever. This would be incredible.