Assessing Bias in Metric Models for LLM Open-Ended Generation Bias Benchmarks

This paper explores how large language models can be improved by learning from human feedback more effectively. Instead of relying only on reinforcement learning or static datasets, the authors introduce a new framework that makes the feedback process more scalable and adaptive. Their method allows models to better align with human values, preferences, and instructions, while also reducing common issues like bias and instability. Through experiments, they show that this approach leads to stronger performance across different tasks, better reasoning ability, and more reliable responses. The work highlights the importance of combining human feedback with efficient training methods to build models that are not just powerful, but also safer and more useful in real-world settings.