The research focuses on defining the attributes that constitute good rationales and examines how human preferences can be explained by these attributes. By utilizing automatic metrics, LLM judgments, and human annotations, the study aims to identify key elements that enhance the evaluation process. This approach addresses the limitations of traditional binary comparisons, providing more nuanced insights into model performance.
The findings show that employing attribute-specific ELO scores allows for a more detailed comparison of LLM-generated rationales. This method not only enhances the interpretability of human interactions with LLMs but also has implications for guiding future research towards better evaluation practices. Improved evaluations could foster more reliable performance metrics and help refine the development of LLMs, thereby enhancing their application in complex reasoning tasks.
👉 Pročitaj original: arXiv AI Papers
