Accuracy
OxonFair: A Flexible Toolkit for Algorithmic Fairness
This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles.
CALVIN: Improved Contextual Video Captioning via Instruction Tuning
The recent emergence of powerful Vision-Language models (VLMs) has significantly improved image captioning.
Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
Training (IIT) which we call Strict IIT (SIIT). SIIT models maintain Tracr's original circuit while being more realistic.
ConStat: Performance-Based Contamination Detection in Large Language Models Jasper Dekoninck 1, Mark Niklas Mรผller 1,2, Martin Vechev 1 Department of Computer Science 1
Public benchmarks play an essential role in the evaluation of large language models.
Intrinsic Self-Supervision for Data Quality Audits
Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance.
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Therefore, they fall short in facilitating methodological advances in semantic modeling within dynamic graphs and exploring the impact of text attributes on downstream tasks.
