LOCUS: A System and Method for Low-Cost Customization for Universal Specialization

A new tutorial on HealthcareNLP has been proposed, focusing on the advancements and challenges within the healthcare domain applications of natural language processing (NLP). It aims to address overlooked tasks such as synthetic data generation and explainable clinical NLP, while providing an overview of essential sub-areas in a patient- and resource-oriented framework.

A new framework named Weaver has been introduced to enhance the performance of language model verifiers by combining multiple weak verifiers into a stronger ensemble. This approach addresses the existing performance gap between general-purpose verifiers and oracle verifiers, which have perfect accuracy. Weaver utilizes weak supervision to estimate the accuracy of each verifier, allowing for a more reliable scoring of generated responses.

SimSUM has been introduced as a benchmark dataset comprising 10,000 simulated patient records that connect unstructured clinical notes with structured background variables, specifically in the context of respiratory diseases. The dataset aims to enhance clinical information extraction by incorporating tabular data generated from a Bayesian network, with clinical notes produced by a large language model, GPT-4o.

A new paradigm called One-shot video-Clip based Retrieval AuGmentation (OneClip-RAG) has been proposed to enhance the efficiency of Multimodal Large Language Models (MLLMs) in processing long videos, addressing the limitations of existing models that can only handle a limited number of frames due to memory constraints.

The paper introduces LAPA, a log-domain prediction-driven dynamic sparsity accelerator designed for Transformer models, addressing the computational bottlenecks that arise due to varying input sequences. This innovative approach combines an asymmetric leading one computing scheme and a mixed-precision multi-round shifting accumulation mechanism to enhance efficiency across multiple stages of processing.

GeoShield has been introduced as a novel adversarial framework aimed at protecting geolocation privacy from Vision-Language Models (VLMs) like GPT-4o, which can infer users' locations from publicly shared images. This framework includes three modules designed to enhance the robustness of geoprivacy protection in real-world scenarios.

The introduction of the Visual Reasoning Sequential Attack (VRSA) highlights vulnerabilities in Multimodal Large Language Models (MLLMs), which are increasingly used for their advanced cross-modal capabilities. This method decomposes harmful text into sequential sub-images, allowing MLLMs to externalize harmful intent more effectively.

A new approach to sentence simplification has been introduced, utilizing Large Language Models (LLMs) as judges to create policy-aligned training data, eliminating the need for expensive human annotations or parallel corpora. This method allows for tailored simplification systems that can adapt to various policies, enhancing readability while maintaining meaning.