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README.md

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-Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University
+The Knowledge Engineering Group (**KEG**) & Data Mining (THUDM) at Tsinghua University. 
+
+We build LLMs: 
+
+* **[ChatGLM](https://github.com/THUDM/ChatGLM3)**: Open Bilingual Chat LLMs, among which the ChatGLM-6B series has attracted **10,000,000** downloads on HF. 
+* **[CodeGeeX](https://github.com/THUDM/CodeGeeX2)**: A Multilingual Code Generation Model (KDD 2023)
+* **[CogVLM (VisualGLM)](https://github.com/THUDM/CogVLM)**: An Open Visual Language Model
+* **[WebGLM](https://github.com/THUDM/WebGLM)**: An Efficient Web-Enhanced Question Answering System (KDD 2023)
+* **[GLM-130B](https://github.com/THUDM/GLM-130B)**: An Open Bilingual Pre-Trained Model (ICLR 2023)
+* **[CogView](https://github.com/THUDM/CogView)**: An Open Text-to-Image Generation Model (NeurIPS 2021)
+* **[CogVideo](https://github.com/THUDM/CogVideo)**: An Open Text-to-Video Generation Model (ICLR 2023)
+* **[AgentTuning](https://github.com/THUDM/AgentTuning)**: Enabling Generalized Agent Abilities for LLMs
+
+We also work on LLM evaluations: 
+* **[AgentBench](https://github.com/THUDM/AgentBench)**: A Benchmark to Evaluate LLMs as Agents
+* **[LongBench](https://github.com/THUDM/LongBench)**: A Bilingual, Multitask Benchmark for Long Context Understanding
+ 
+
+We also pre-train graph neural networks:
+* **[CogDL](https://github.com/THUDM/CogDL)**: A Library for Graph Deep Learning (WWW 2023)
+* **[GraphMAE](https://github.com/THUDM/GraphMAE)**: (Generative) Masked Graph Neural Network Pre-Training. (KDD 2022 & [WWW 2023](https://github.com/THUDM/GraphMAE2))
+* **[GPT-GNN](https://github.com/acbull/GPT-GNN)**: Generative Graph Neural Network Pre-Training (KDD 2020, MSR, UCLA).  
+* **[GCC](https://github.com/THUDM/CogDL)**: Constrative Graph Neural Network Pre-Training (KDD 2020)
+* **[SelfKG](https://github.com/THUDM/SelfKG)**: Self-Supervised Learning for Knowledge Graphs (WWW 2022)
+
+We also work on graph embedding theory and system: 
+* **[SketchNE](https://github.com/THU-numbda/SketchNE)**: Embedding Billion-Scale Networks Accurately in One Hour (TKDE 2023)
+* **[ProNE](https://github.com/THUDM/ProNE)**: Embedding Networks of 100 Million Nodes with 10-400 Speedup (IJCAI 2019)
+* **[NetSMF](https://github.com/xptree/NetSMF)**: Embedding Networks of 100 Million Nodes (WWW 2019)
+* **[NetMF](https://github.com/xptree/NetMF)**: Understanding DeepWalk, LINE, PTE, and node2vec as Matrix Factorization (WSDM 2018)
+
+We started with graphs and networks, and always love them: 
+* **[AMiner](https://www.aminer.cn/)**: An Academic Search and Mining System Since 2006 (KDD 2008, ACM SIGKDD Test of Time Award)