Effective AI risk assessment relies on the quality of evaluations. Currently, there are large quality differences, such as in construct validity and annotation, between existing benchmarks. In this work, we propose a quality scorecard for benchmarks designed to make this diversity easier to navigate. The scorecard employs two main components: dimensions, which provide granular scores of an evaluation under that dimension, and classifications, which correspond to concrete use-cases ranging from research to post-deployment. By establishing a common language and objective methods, this framework aims to aid in transparency and raise the baseline quality of benchmarks used across the ecosystem.

Despite advancements, fine-tuning Large Language Models (LLMs) remains costly due to the extensive parameter count and substantial data requirements for model generalization. Accessibility to computing resources remains a barrier for the open-source community. To address this challenge, we propose the In2Core algorithm, which selects a coreset by analyzing the correlation between training and evaluation samples with a trained model. Notably, we assess the model’s internal gradients to estimate this relationship, aiming to rank the contribution of each training point. To enhance efficiency, we propose an optimization to compute influence functions with a reduced number of layers while achieving similar accuracy. By applying our algorithm to instruction fine-tuning data of LLMs, we can achieve similar performance with just 50% of the training data. Meantime, using influence functions to analyze model coverage to certain testing samples could provide a reliable and interpretable signal on the training set’s coverage of those test points.