I’ve been exploring ways to reduce hallucinations in large language models and improve their reasoning capabilities. Inspired by recent discussions on LLMs, I developed a concept called ATLAS (Adaptive Thinking and Learning through Alternative Solutions), which is based on the ACDQ framework:
- Act: Direct the model to behave like an expert in any field.
- Context: Provide detailed, rich information to improve understanding.
- Deep-thinking: Encourage the model to reason deeply before responding.
- Questions: Prompt the AI to ask clarifying questions to enhance collaboration and accuracy.
By combining this with multi-path training strategies that expose the model to diverse problem-solving approaches, ATLAS aims to improve robustness, reduce contextual uncertainty, and significantly reduce hallucinations through enhanced contextual understanding and self-verification.
This approach could advance transformer-based models by integrating comprehensive context, deep reasoning, and diverse solution strategies.
That is an interesting way of promoting an LLM. Definitely very thorough!
In my series I try to explain more the training process. From what I understand, your prompting is applied directly to existing models (such as GPT4), not directly training a model right?
Thank you for your thoughtful question! You're correct that the ACDQ framework is primarily applied at the prompting level to existing models like GPT-4. However, the ATLAS architecture takes this concept further by integrating these principles directly into the training process of new neural network models.
The idea behind ATLAS is to combine multi-path training strategies with the ACDQ formula to create models that inherently reduce hallucinations. By exposing the model to diverse problem-solving approaches during training and embedding mechanisms for deep reasoning, contextual understanding, and self-verification, ATLAS aims to develop a more robust architecture. This approach goes beyond prompting by ensuring that the model learns adaptive thinking and reasoning capabilities at its core.
In essence, ATLAS is designed to enhance the training pipeline itself, creating models that are less prone to errors and better equipped for reliable human-AI collaboration. Would love to hear your thoughts on this deeper integration Sir!
I’ve been exploring ways to reduce hallucinations in large language models and improve their reasoning capabilities. Inspired by recent discussions on LLMs, I developed a concept called ATLAS (Adaptive Thinking and Learning through Alternative Solutions), which is based on the ACDQ framework:
- Act: Direct the model to behave like an expert in any field.
- Context: Provide detailed, rich information to improve understanding.
- Deep-thinking: Encourage the model to reason deeply before responding.
- Questions: Prompt the AI to ask clarifying questions to enhance collaboration and accuracy.
By combining this with multi-path training strategies that expose the model to diverse problem-solving approaches, ATLAS aims to improve robustness, reduce contextual uncertainty, and significantly reduce hallucinations through enhanced contextual understanding and self-verification.
This approach could advance transformer-based models by integrating comprehensive context, deep reasoning, and diverse solution strategies.
Would love to hear your thoughts or feedback!
That is an interesting way of promoting an LLM. Definitely very thorough!
In my series I try to explain more the training process. From what I understand, your prompting is applied directly to existing models (such as GPT4), not directly training a model right?
Yes sir!
Thank you for your thoughtful question! You're correct that the ACDQ framework is primarily applied at the prompting level to existing models like GPT-4. However, the ATLAS architecture takes this concept further by integrating these principles directly into the training process of new neural network models.
The idea behind ATLAS is to combine multi-path training strategies with the ACDQ formula to create models that inherently reduce hallucinations. By exposing the model to diverse problem-solving approaches during training and embedding mechanisms for deep reasoning, contextual understanding, and self-verification, ATLAS aims to develop a more robust architecture. This approach goes beyond prompting by ensuring that the model learns adaptive thinking and reasoning capabilities at its core.
In essence, ATLAS is designed to enhance the training pipeline itself, creating models that are less prone to errors and better equipped for reliable human-AI collaboration. Would love to hear your thoughts on this deeper integration Sir!