large language models

Impact of Intermediate-Task Language Count on Zero-Shot Cross-Lingual Transfer Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on...

mT5 Efficiency in Low-Resource Languages via Intermediate-Task Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on...

Impact of Intermediate-Task Language Count on Zero-Shot Cross-Lingual Transfer Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on...

mT5 Efficiency in Low-Resource Languages via Intermediate-Task Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on...

Moral Concern Is a Response Regime: A Preregistered Cross-Model Test of Persona Framing in Large Language Models

Four large language models from four providers judged the same 20 ambiguous scenarios under a neutral frame and six loaded personas, across 11,200 main trials. The prediction came from Motivated Violation Construction (MVC), a theory of human moral cognition on which a loaded actor identity can move ambiguous behaviour...

ChatEvac: End-to-End automation assessment for building evacuation safety based on large language and diffusion models

Graph neural network enhanced large language model for commonsense question answering

Integrating Large Language Models (LLMs) with Oracle 26AI for Advanced Enterprise Analytics and Knowledge Management

Results indicate that LLM-augmented analytics on a converged Oracle 26AI platform can reduce average analytical query resolution time by approximately 60 percent relative to traditional BI report cycles, achieve semantic retrieval precision above 90 percent for enterprise knowledge corpora, and reduce generative output...

Large language model-enhanced multi-agent framework for seamless design and manufacturing integration

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery

A hybrid framework that decouples detection from damage assessment is proposed, combining the precision of CV models with the reasoning power of LVLMs, and the best combination under this framework accurately counts intact, partially damaged and completely destroyed buildings.

Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph

Debate-on-Graph (DoG) is proposed, a new framework that enables LLMs and UKGs to collaborate adaptively for reliable reasoning and introduces a Multi-Agent Debate mechanism, which yields reliable answers through adaptive adversarial debates, aiming to fully exploit the knowledge in UKGs while preserving the reliability...

A Hybrid LLM-Based Framework for Automated Security Annotation Generation in Business Process Models

A hybrid framework that takes a BPMN process model and a security requirements document as input and automatically generates security annotations adhering to the SecBPMN2 specification is presented, providing a scalable foundation for security-by-design BPM.