arXiv:2310. 11714v5 Announce Type: replace Abstract: Ranking generative models based on the fidelity and diversity of their outputs is required to identify the best generator in a group of candidate generative AI models.
arXiv:2608. 08422v1 Announce Type: cross Abstract: Ranking data arise in scientific and machine learning applications, including recommendation systems, information retrieval, voting, marketing, and AI preference ranking from human feedback.
arXiv:2608.25200v2 Announce Type: replace-cross Abstract: We study learning a mixture of $k$ Plackett-Luce models from multi-way ranking responses from annotators that may represent heterogeneous und...
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn ho...
arXiv:2603.08064v3 Announce Type: replace Abstract: Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are e...
arXiv:2602. 11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the document being relevant to the query given some feature representation of the query-document pair.
CORE improves compositional reasoning in multimodal language models by distilling a cross‑attentive reranker’s fine‑grained judgments into the embedding model. It generates candidate lists across five compositional matching levels and trains with a Rank‑KL objective to replicate the reranker’s ranking. Experiments on COLA, SUGARCREPE++, and NEGBENCH show CORE‑RERANKER‑8B outperforms Jina‑Reranker by 10.7 points, while CORE‑EMBED‑8B achieves the best overall average among evaluated embeddings, with gains also transferring to the MCMR benchmark without harming COCO or Flickr30K retrieval.
arXiv:2604. 05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.
arXiv:2608. 01301v2 Announce Type: replace-cross Abstract: Infrared-visible image fusion (IVIF) has no ideal fused reference, so fusion algorithms are routinely ranked by scalar objective metrics that formalize different proxies for information transfer, structure, or source similarity.
The paper argues that measuring diversity in AI-generated content using a single scalar score is inherently ambiguous and often misleading. It reviews existing diversity metrics, demonstrates their limitations through axiomatic and empirical analyses, and introduces diversity profiles—curve-valued, condition-aware summaries that evaluate diversity across a range of thresholds, scales, exponents, or orders. These profiles reveal whether comparisons are robust across resolutions or depend on arbitrary parameter choices, offering a more transparent framework for generative AI evaluation.
arXiv:2607. 05046v1 Announce Type: new Abstract: Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model development.
arXiv:2501. 18897v4 Announce Type: replace-cross Abstract: Generative models have achieved remarkable success across a range of applications, yet their evaluation still lacks principled uncertainty quantification.
The paper introduces MoPLEx, an expectation‑maximization algorithm for learning mixtures of Plackett‑Luce models from multi‑way ranking data. It augments rankings with synthetic responses from a base language model and uses a gradient‑based estimation to reduce inference cost, enabling efficient fitting of large‑scale models. Experiments show the method achieves low probability estimation error and improves clustering and ranking accuracy by 43.7% and 15.2% over baselines.