With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness --the semantic coherence of generated images to the prompts they were conditioned on. A variety of T2I faithfulness metrics have been proposed, leveraging advances in cross-modal embeddings and vision-language models (VLMs).

Machine learning has enabled great progress in the automatic interpretation of images, where vision language models (VLMs) translate features of images into text (Radford et al., 2021; Liu et al., 2024). In the medical domain, patient images are interpreted by radiologists, Evaluating radiology reports is a challenging which is referred to as radiology report generation problem as factual correctness is extremely important (RRG). Automated and high-quality RRG has due to the need for accurate medical the potential to greatly reduce the repetitive work of communication about medical images. Existing radiologists, alleviate burdens arising from shortage automatic evaluation metrics either suffer of radiologists, generally improve clinical communication from failing to consider factual correctness (Kahn Jr et al., 2009), and increase the accuracy (e.g., BLEU and ROUGE) or are limited of radiology reports (Rajpurkar and Lungren, 2023). in their interpretability (e.g., F1CheXpert Commonly used evaluation metrics in RRG literature and F1RadGraph). In this paper, we introduce (Lin, 2004; Zhang et al., 2019; Smit et al., 2020; GREEN (Generative Radiology Report Evaluation Delbrouck et al., 2022) seek to evaluate a generated and Error Notation), a radiology report radiology report against a reference report written by generation metric that leverages the natural language a radiologist by leveraging simple n-grams overlap, understanding of language models to general language similarity, pathology identification identify and explain clinically significant errors within specific imaging modalities and disease classes, in candidate reports, both quantitatively and commercially-available large language models.

Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of \emph{The Federalist Papers}. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of "proof texts," The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statistical natural language parser were explored: all subtrees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.

AWS Lambda is a serverless compute service that helps you run code without provisioning or managing hardware. You can run AWS Lambda function to execute a code in response to triggers such as changes in data or system state. For example, you can use Amazon S3 to trigger AWS Lambda to process data immediately after an upload. By combining AWS Lambda with other AWS services, developers can build powerful web applications that automatically scale up and down and run in a highly available configuration. Due to its transitory nature and handiness, Lambda has become a popular and integral part of many solutions or architectures.