For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.

Cleaning missing values: The human-generated questions may be unanswerable. Thus, we remove the Q&A items that lack available answers. Additionally, documents lacking any valid Q&A pairs are also removed.

Therefore, many existing SLP vectorization schemes are driven by hand-crafted heuristics (Liu et al., 2012;

Modern benchmarks such as HELM MMLU account for multiple metrics like accuracy, robustness and efficiency. When trying to turn these metrics into a single ranking, natural aggregation procedures can become incoherent or unstable to changes in the model set. We formalize this aggregation as a social choice problem where each metric induces a preference ranking over models on each dataset, and a benchmark operator aggregates these votes across metrics. While prior work has focused on Arrow's impossibility result, we argue that the impossibility often originates from pathological examples and identify sufficient conditions under which these disappear, and meaningful multi-criteria benchmarking becomes possible. In particular, we deal with three restrictions on the combinations of rankings and prove that on single-peaked, group-separable and distance-restricted preferences, the benchmark operator allows for the construction of well-behaved rankings of the involved models. Empirically, we investigate several modern benchmark suites like HELM MMLU and verify which structural conditions are fulfilled on which benchmark problems.

As semiconductor devices become miniaturized and their structures become more complex, there is a growing need for large-scale atomic-level simulations as a less costly alternative to the trial-and-error approach during development.Although machine learning force fields (MLFFs) can meet the accuracy and scale requirements for such simulations, there are no open-access benchmarks for semiconductor materials.Hence, this study presents a comprehensive benchmark suite that consists of two semiconductor material datasets and ten MLFF models with six evaluation metrics.

The use of Retrieval-Augmented Generation (RAG) has improved Large Language Models (LLMs) in collaborating with external data, yet significant challenges exist in real-world scenarios. In areas such as academic literature and finance question answering, data are often found in raw text and tables in HTML or PDF formats, which can be lengthy and highly unstructured. In this paper, we introduce a benchmark suite, namely Unstructured Document Analysis (UDA), that involves 2,965 real-world documents and 29,590 expert-annotated Q&A pairs. We revisit popular LLMand RAG-based solutions for document analysis and evaluate the design choices and answer qualities across multiple document domains and diverse query types. Our evaluation yields interesting findings and highlights the importance of data parsing and retrieval. We hope our benchmark can shed light and better serve real-world document analysis applications.

Echocardiography is one of the most commonly used diagnostic imaging modalities in cardiology. Application of deep learning models to echocardiograms can enable automated identification of cardiac structures, estimation of cardiac function, and prediction of clinical outcomes. However, a major hindrance to realizing the full potential of deep learning is the lack of large-scale, fully curated and annotated data sets required for supervised training. High-quality pre-trained representations that can transfer useful visual features of echocardiograms to downstream tasks can help adapt deep learning models to new setups using fewer examples. In this paper, we design a suite of benchmarks that can be used to pre-train and evaluate echocardiographic representations with respect to various clinically-relevant tasks using publicly accessible data sets. In addition, we develop a unified evaluation protocol---which we call the echocardiographic task adaptation benchmark (ETAB)---that measures how well a visual representation of echocardiograms generalizes to common downstream tasks of interest. We use our benchmarking framework to evaluate state-of-the-art vision modeling pipelines. We envision that our standardized, publicly accessible benchmarks would encourage future research and expedite progress in applying deep learning to high-impact problems in cardiovascular medicine.

Mutual Information (MI) is a fundamental metric for quantifying dependency between two random variables.

For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.