Enhancing LLM Responses Via Dynamic Adaptive Reasoning
Iterative Thought Refiner enhances LLM responses via dynamic adaptive reasoning, leveraging human engagement to refine answers.
This is a Plain English Papers summary of a research paper called Iterative Thought Refiner: Enhancing LLM Responses via Dynamic Adaptive Reasoning. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview Iterative human engagement is an effective way to leverage the advanced language processing capabilities of large language models (LLMs). The Iteration of Thought (IoT) framework is proposed to enhance LLM responses by dynamically generating thought-provoking prompts based on the input query and the current LLM response. Unlike static or s...