large language models

An Agency-Specific Project Authoring Advisor: LLM-Based RAG System with Automatic Prompt Optimization Method

Abstract Despite the vast potential of large language models (LLMs) in specialized fields, their application in architecture, engineering, and construction (AEC) has been hindered by several challenges, including inaccurate information retrieval, limited access to domain information, inadequate customization of prompts...

Project Brahmaand: Sovereign, Non-Autoregressive Edge Intelligence via 24-Dimensional Topological Cartridges, Zero-Multiplication Subtractive Meshes, and Predictive B-Frames

State-of-the-art Large Language Models (LLMs) rely heavily on autoregressive transformer decoders requiring billions of dense floating-point Multiply-Accumulate (MAC) operations, massive memory bandwidth, and centralized cloud server infrastructure. In this paper, we introduce Project Brahmaand, an edge-native, non-aut...

Watermarking large language models in Europe: interpreting the AI act in light of technology

Abstract To foster trustworthy Artificial Intelligence (AI) within the European Union, the AI Act requires providers to mark and detect the outputs of their general-purpose models. The Article 50 and Recital 133 call for marking methods that are "sufficiently reliable, interoperable, effective and robust". Yet, the rap...

Algorithmic Shortlisting in Participatory Budgeting

This work designs and test privacy-preserving methods for algorithmic shortlisting and proposes a vote-based pipeline that enables state-of-the-art LLMs to perform on par with classical machine learning.

Linguistic Triggers of Gender and Racial Bias in Open-Weight LLMs Applied to Recruitment

Open-weight large language models are rapidly entering hiring pipelines, yet their discriminatory failure modes—and the regulatory exposure these create under the EU AI Act high-risk classification (Annex III) and U.S. EEOC adverse-impact analysis—remain poorly understood. We present the first systematic, multi-model a...

From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at Scale

Large language models (LLMs) are increasingly deployed in high-stakes domains such as housing screening. While alignment techniques mitigate explicit racial bias in generated text, they often leave covert attitudinal associations in internal probability distributions untouched. Adapting the matched-guise sociolinguisti...

A multi-site benchmarking framework for scalable extraction of geriatric care constructs from electronic health records

Findings highlight key trade-offs among symbolic, generative, and instruction-tuned approaches for CGA and 4Ms phenotyping, suggesting that hybrid heuristic-LLM methods offer interpretability and stability, whereas large language models provide greater adaptability for complex clinical narratives.

Generative AI And Large Language Models in Intelligent Systems

Cultural bias or universal traits? Exploring personality-like profiles of large language models

Do large language models (LLMs) exhibit coherent personality profiles, and are these profiles culturally neutral? This study used two validated personality inventories, the Big Five Inventory–2 and the Dark Triad Dirty Dozen, to assess five state-of-the-art LLMs (ChatGPT-4o, Gemini 2.0 Flash, DeepSeek-V3, Ernie 3.5 a...

Application of artificial intelligence in hepatology

Artificial intelligence (AI) is being applied across diagnostic and therapeutic workflows in hepatology. This narrative review summarizes recent advances in AI for liver disease. In medical imaging and digital pathology, computer vision enables automated quantitative analysis of ultrasound, CT, MRI, and histologic imag...

Adaptive multi-format synergistic table understanding framework for large language models

The proposed Adaptive Multi-Format Synergistic Table Understanding framework effectively alleviates the core bottlenecks in table understanding, and achieves synergistic improvements in both accurate evidence localization and precise arithmetic computation.

The Post-ChatGPT Surge: Does AI-Health-Education Research Growth Reflect Evidence or Enthusiasm

Thematic analysis revealed that in 2024, ChatGPT and other large language models started to dominate over machine learning, which previously accounted for the majority of publications, which suggest a possible gap between the quantitative expansion of the literature and the consolidation of the field.