PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning

A new framework called Generation-Augmented Generation (GAG) has been proposed to enhance the injection of private, domain-specific knowledge into large language models (LLMs), addressing challenges in fields like biomedicine, materials, and finance. This approach aims to overcome the limitations of fine-tuning and retrieval-augmented generation by treating private expertise as an additional expert modality.

A new theoretical framework utilizing Sheaf Diffusion has been proposed to enhance fairness in machine learning algorithms, particularly in critical sectors such as justice, healthcare, and finance. This method aims to project input data into a bias-free space, thereby addressing both individual and group fairness metrics.