Chain-of-Thought

Ontology-Based LLM Prompting for Construction Activity Recognition: 73.68% Accuracy With No Training Data.

Ontology-Based LLM Prompting for Construction Activity Recognition: 73.68% Accuracy With No Training Data

Leave a Comment / Machine Learning, Engineering AI, Natural Language Processing /

Ontology-Based LLM Prompting for Construction Activity Recognition: 73.68% Accuracy With No Training Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision Engineering AI About Construction AI · Advanced Engineering Informatics 69 (2026) 103869 · 20 min read What Is a Construction Site Actually Doing Right Now? TU Berlin Built a System That Reads Site […]

Ontology-Based LLM Prompting for Construction Activity Recognition: 73.68% Accuracy With No Training Data Read More »

A visual representation of the EasyDistill toolkit revolutionizing knowledge distillation in large language models.

7 Revolutionary Ways EasyDistill is Changing LLM Knowledge Distillation (And Why You Should Care!)

1 Comment / Machine Learning /

Introduction: The Future of LLM Optimization Starts Here Artificial Intelligence (AI) has transformed how we interact with technology, especially through Large Language Models (LLMs) . These powerful systems have redefined natural language processing (NLP), enabling machines to understand and generate human-like text. However, as impressive as these models are, they come with significant challenges—high computational

7 Revolutionary Ways EasyDistill is Changing LLM Knowledge Distillation (And Why You Should Care!) Read More »

Follow by Email
Facebook
X (Twitter)
Tiktok