large language models

A general convergence theorem for nearest-element recurrences

Let b(n) be a strictly increasing sequence of positive integers satisfyinglim_{n→∞} b(n+1)/b(n) = 1. Define a recurrence by taking a_n to be the element of b nearest to a_{n-1} + a_{n-2}, with ties broken by taking the smaller element. We prove that for any initial values a_1, a_2 in b, lim_{n→∞} a_n / a_{n-1} = φ := (...

Generative Artificial Intelligence in Cartographic Production: Evaluation of Large Language Models for Map Generation at Different Levels of Prompt Complexity

Artificial Intelligence in Geospatial Data Processing and Analysis -> Knowledge representation and geospatial data handling with Multimodal Language Models and Agentic Systems

Calibrated Confidence Expression for Radiology Report Generation

ConRad (Confidence Calibration for Radiology Reports), a reinforcement learning framework for fine-tuning medical LVLMs to produce calibrated verbalized confidence estimates alongside radiology reports, is introduced and substantially improves calibration and outperforms competing methods.

Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction

It is found that, at variance with networks not based on the attention mechanism, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties; this is true both for synthetic and natural protein sequences.

CQ4OE: A benchmark for assessing LLM-assisted ontology generation from competency questions

Ontology generation from Competency Questions (CQs) is a central yet labor-intensive phase of Ontology Engineering. While large language models (LLMs) offer promising automation capabilities, current evaluations remain fragmented. Task formulations are heterogeneous, gold standards often lack fine-grained CQ provenance...

Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review

Empirical studies of LLM-enabled AI chatbot applications and evaluations for health purposes across four stakeholder groups: the public or patients, health professionals, health researchers and students, and health institutions are mapped.

RadCoT: a Radiological Chain-of-Thought framework for enhanced error detection in radiology reports

Structured prompting improves detection of interpretation and section-consistency errors across seven models and consistently enhances LLM-based error detection, closing the gap between open-source and commercial models and offering a pathway for privacy-preserving, on-premises QA.

The Host Is Not Idle: Computational SSDs for Host-Free Checkpointing in Multimodal LLM Training

Large language models (LLMs) are increasingly extending beyond plain text to jointly reason over images and video. Among such multimodal models, video LLMs that take video as input require the host CPU to decode and preprocess each upcoming batch of video, making it busy throughout training. We show that this property...

Song Generation Using Artificial Intelligence and Large Language Models

Fairness-aware artificial intelligence tutoring for multilingual learners: evaluating adaptive feedback across L1 and proficiency groups

Introduction Recent advances in large language models (LLMs), such as GPT-4, have created new opportunities for intelligent computer-assisted language learning, particularly through personalized feedback for multilingual English learners. Building on research in automated writing evaluation (AWE), this study developed...

ProtoSurv: prototype-guided adaptation of computed tomography foundation model for lung-cancer prognosis prediction

Abstract Prognosis prediction is important for precision treatment in lung cancer. Chest computed tomography (CT) and deep learning are feasible approaches for learning imaging-based prognostic representations. However, prognostic labels are difficult to obtain because survival endpoints require long-term follow-up; th...

Artificial Intelligence-Related Risks in Interventional Pulmonology: An Exploratory Enumeration and Ranking Study Across Five General-Purpose Large Language Models

Background/Objectives: Artificial intelligence (AI) is entering interventional pulmonology (IP) faster than its potential risks have been systematically catalogued. We explored whether general-purpose large language models (LLMs), now widely consulted informally by patients and clinicians, could provide a rapid and str...