About
Research & Development
AutoLit: An AI Approach to Faster Literature Reviews
TLDR: The paper introduces AutoLit, an AI-driven pipeline that automates literature reviews by using transformer-based embeddings and cosine similarity. It generates keywords from a...
Research & Development
When LLMs Invent: The Challenge of Knowledge Overriding Evidence in Process Modeling
TLDR: A new study reveals that Large Language Models (LLMs) frequently suffer from 'knowledge-driven hallucination,' where their pre-trained general knowledge overrides explicit, conflicting evidence...
Research & Development
Steering AI Agents Towards Expert-Level Behavior
TLDR: A new diagnostic framework, ADM-ES, is introduced to evaluate and improve multi-agent LLM systems. It uses expert-annotated "golden" data, generates expanded "silver" datasets...
Research & Development
Mapping the Landscape of Intelligent Automation: A New Taxonomy for Machine Learning in RPA
TLDR: A new research paper introduces a taxonomy for understanding the integration of Machine Learning (ML) into Robotic Process Automation (RPA), creating "Intelligent RPA."...
Research & Development
CLIMB: A New Framework for Data-Driven Occupation Taxonomies
TLDR: CLIMB is a novel framework that automates the creation of high-quality, data-driven occupation taxonomies directly from raw job postings. It uses global semantic...
Research & Development
EHR-MCP: Bridging Large Language Models with Electronic Health Records
TLDR: A study evaluated EHR-MCP, a framework integrating large language models (LLMs) with hospital electronic health records (EHR) via the Model Context Protocol (MCP)....