DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM

TL;DR: A novel taxonomy for topic modelling summary judgment cases in UK law is developed and applied, revealing distinct patterns in the application of summary judgments across various legal domains.

Abstract: ABSTRACT Large language models (LLMs), like the GPT series, have recently emerged as transformative tools in the medical field due to their human‐like language generation and understanding. This systematic review examines the evolution, applications, and challenges of medical LLMs in digital health and clinical technology. A structured search was conducted across ScienceDirect, PubMed, Scopus, and manual sources from 2007 to 2025, following PRISMA 2020 guidelines. After applying inclusion and exclusion criteria, 185 studies were selected from an initial pool of 698 papers. Among these, 30 representative studies were analyzed in‐depth based on their relevance, methodological quality, and contribution to diverse LLM applications in health care. Most research centered on GPT‐based models, with over 81% demonstrating strong performance in language generation, diagnostic assistance, and clinical documentation, based on automated metrics and human feedback. Notably, some models achieved up to 90% satisfaction from healthcare professionals. The findings reveal LLM's potential to enhance patient interaction, decision support, and overall healthcare efficiency. This review contributes by synthesizing key advancements, assessing model performance, and outlining ethical challenges such as trust, privacy, and safe deployment. It offers novel insights for researchers and practitioners seeking to adopt or improve LLM integration in health care. Future directions include improving transparency, developing domain‐specific models, and establishing regulatory frameworks for responsible use.

Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.

TL;DR: Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.

TL;DR: Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity is presented.