The official implementation of "RouteExplainer: An Explanation Framework for Vehicle Routing Problem" (PAKDD 2024, oral)
Automated LLM-based Prompt Engineering for Structured Data Processing
Powerful framework for building applications with Large Language Models (LLMs), enabling seamless integration with memory, agents, and external data sources.
RAG enhances LLMs by retrieving relevant external knowledge before generating responses, improving accuracy and reducing hallucinations.
ScholarLens analyzes research papers using RAG with AI models from OpenAI, Anthropic, and Google. It identifies research gaps, assesses novelty, extracts key concepts, visualizes citations, and enables natural language queries of academic content. Features include PDF processing, arXiv/Semantic Scholar integration, batch processing, and intelligent
Master’s Thesis at TU Vienna, assessing state-of-the-art LLMs for automating BPO tasks. Features a custom Action Research-Based Compliance Testing (ARCT) framework, exploring LLM capabilities, context impact, and limitations in optimizing complex workflows.
An interactive Jupyter Notebook demonstrating AI agent collaboration using CrewAI. This project explores how multiple AI agents can research, generate content, and automate workflows through task orchestration.
Add a description, image, and links to the llm-applications topic page so that developers can more easily learn about it.
To associate your repository with the llm-applications topic, visit your repo's landing page and select "manage topics."