Update README.md

README.md

@@ -11,35 +11,36 @@ The Knowledge Engineering Group (**[KEG](https://twitter.com/thukeg)**) & Data M
 
 We build **LLMs** and related training & inference techniques: 
 
-* **[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)
-* **[CogAgent](https://github.com/THUDM/CogVLM)**: A Visual Language Model for GUI Agents
-* **[AgentTuning](https://github.com/THUDM/AgentTuning)**: Enabling Generalized Agent Abilities for LLMs
-* **[APAR](https://arxiv.org/abs/2401.06761)**: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
+* **[ChatGLM](https://github.com/THUDM/ChatGLM3)**, 
+  **[CodeGeeX](https://github.com/THUDM/CodeGeeX2)**,
+  **[CogVLM (VisualGLM)](https://github.com/THUDM/CogVLM)**,
+  **[WebGLM](https://github.com/THUDM/WebGLM)**,
+  **[GLM-130B](https://github.com/THUDM/GLM-130B)**,
+  **[CogView](https://github.com/THUDM/CogView)**,
+  **[CogVideo](https://github.com/THUDM/CogVideo)**. 
+
+  **[CogAgent](https://github.com/THUDM/CogVLM)**: A Visual Language Model for GUI Agents
+  **[AgentTuning](https://github.com/THUDM/AgentTuning)**: Enabling Generalized Agent Abilities for LLMs
+  **[APAR](https://arxiv.org/abs/2401.06761)**: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
 
 We also work on **LLM evaluations**: 
-* **[AgentBench](https://github.com/THUDM/AgentBench)**: A Benchmark to Evaluate LLMs as Agents (ICLR 2024)
-* **[AlignBench](https://github.com/THUDM/AlignBench)**: A Benchmark to Evaluate Chinese Alignment of LLMs
-* **[LongBench](https://github.com/THUDM/LongBench)**: A Bilingual, Multitask Benchmark for Long Context Understanding
+* **[AgentBench](https://github.com/THUDM/AgentBench)**,
+  **[AlignBench](https://github.com/THUDM/AlignBench)**,
+  **[LongBench](https://github.com/THUDM/LongBench)**.
  
 
 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)
+* **[CogDL](https://github.com/THUDM/CogDL)**,
+  **[GraphMAE](https://github.com/THUDM/GraphMAE)**,
+  **[GPT-GNN](https://github.com/acbull/GPT-GNN)**,
+  **[GCC](https://github.com/THUDM/CogDL)**,
+  **[SelfKG](https://github.com/THUDM/SelfKG)**. 
 
 We also work on **graph embedding theory, algorithms, and systems**: 
-* **[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)
+* **[SketchNE](https://github.com/THU-numbda/SketchNE)**,
+  **[ProNE](https://github.com/THUDM/ProNE)**,
+  **[NetSMF](https://github.com/xptree/NetSMF)**,
+  **[NetMF](https://github.com/xptree/NetMF)**. 
 
 We started with **social networks and graphs**, 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)
+* **[AMiner](https://www.aminer.cn/)**.