Enhancing Large Language Models: Integrating Human Preferences and Conditional Reinforcement Learning

This research enhances Large Language Models (LLMs) by leveraging human preferences and conditional reinforcement learning to improve training and response generation. Current LLMs often fall short in authenticity, security, and user engagement. By capturing human preferences and using dedicated datasets, we aim to reduce discrepancies between LLM outputs and human-like responses. Encouraging LLMs to articulate their decision-making process, combined with structured decision explanations and topic modeling, enhances understanding of user preferences. This approach aims to develop more accurate, contextually aware, and engaging models, advancing LLMs to better meet human needs while addressing AI security and real-world challenges.

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