This is the official implementation for our NeurIPS 2024 paper "Can Graph Learning Improve Planning in LLM-based Agents?" [中文] For running LLM's direct inference or GraphSearch, our codes are ...

Slide 1: Understanding LLM Benchmarks using Python. Large Language Models (LLMs) have revolutionized natural language processing. To evaluate their performance, we use benchmarks. This presentation ...

Learning to Live With Your UCaaS LLM, Part 1 Learning to Live With Your UCaaS LLM, Part 1. This three-part series provides an inside look at the generative AI inside Microsoft Teams, Zoom, Cisco Webex ...

This paper addresses the critical need for accurate and reliable point cloud quality assessment (PCQA) in various applications, such as autonomous driving, robotics, virtual reality, and 3D ...

Temporal Knowledge Graphs (TKGs) are structured representations of real-world data incorporating temporal dimensions. Traditional methods for Temporal Knowledge Graph Reasoning (TKGR) rely on deep ...

By combining LLM-generated knowledge graphs and graph machine learning, GraphRAG enables us to answer important classes of questions that we cannot attempt with baseline RAG alone. We have seen ...

Knowledge graphs enable customization by aligning the LLM’s outputs with the user’s historical data and preferences. This tailoring can make interactions with LLMs feel more personal and relevant.