Yuhui Zhang

TL;DR: This work introduces Stanza, an open-source Python natural language processing toolkit supporting 66 human languages that features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition.

TL;DR: The Holistic Evaluation of Language Models (HELM) as mentioned in this paper ) is a popular benchmark for language models, with 30 models evaluated on 16 core scenarios and 7 metrics, exposing important trade-offs.

TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.

TL;DR: The study introduces biomedical and clinical NLP packages built for the Stanza library, which offer performance that is similar to the state of the art, and is also optimized for ease of use.

TL;DR: Modality gap, an intriguing geometric phenomenon of the representation space of multi-modal models, is presented and it is demonstrated that varying the modality gap distance has a significant impact in improving the model’s downstream zero-shot classi-cation performance and fairness.