Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University

The Knowledge Engineering Group (KEG) & Data Mining (THUDM) at Tsinghua University.

We build LLMs:

• ChatGLM: Open Bilingual Chat LLMs, among which the ChatGLM-6B series has attracted 10,000,000 downloads on HF.
• CodeGeeX: A Multilingual Code Generation Model (KDD 2023)
• CogVLM (VisualGLM): An Open Visual Language Model
• WebGLM: An Efficient Web-Enhanced Question Answering System (KDD 2023)
• GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
• CogView: An Open Text-to-Image Generation Model (NeurIPS 2021)
• CogVideo: An Open Text-to-Video Generation Model (ICLR 2023)
• AgentTuning: Enabling Generalized Agent Abilities for LLMs

We also work on LLM evaluations:

• AgentBench: A Benchmark to Evaluate LLMs as Agents
• LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

We also pre-train graph neural networks:

• CogDL: A Library for Graph Deep Learning (WWW 2023)
• GraphMAE: (Generative) Masked Graph Neural Network Pre-Training. (KDD 2022 & WWW 2023)
• GPT-GNN: Generative Graph Neural Network Pre-Training (KDD 2020, MSR, UCLA).
• GCC: Constrative Graph Neural Network Pre-Training (KDD 2020)
• SelfKG: Self-Supervised Learning for Knowledge Graphs (WWW 2022)

We also work on graph embedding theory, algorithms, and systems:

• SketchNE: Embedding Billion-Scale Networks Accurately in One Hour (TKDE 2023)
• ProNE: Embedding Networks of 100 Million Nodes with 10-400 Speedup (IJCAI 2019)
• NetSMF: Embedding Networks of 100 Million Nodes (WWW 2019)
• NetMF: Understanding DeepWalk, LINE, PTE, and node2vec as Matrix Factorization (WSDM 2018)

We started with social networks and graphs, and always love them:

• AMiner: An Academic Search and Mining System Since 2006 (KDD 2008, ACM SIGKDD Test of Time Award)