1.1k citations · 2.4k across the 37 of their papers we have counts for
11 papers · 1 filter
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents
Yifan Xu, Xiao Liu, Xueqiao Sun +7
Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been recently a frequently-mentioned interaction metho…
WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Zehan Qi, Xiao Liu, Iat Long Iong +11
Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive propr…
AutoGLM: Autonomous Foundation Agents for GUIs
Xiao Liu, Bo Qin, Dongzhu Liang +27
We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs). W…
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
Xiao Liu, Tianjie Zhang, Yu Gu +27
Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agent…
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Team GLM, :, Aohan Zeng +56
We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes…
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts
Shudan Zhang, Hanlin Zhao, Xiao Liu +6
Large language models (LLMs) have manifested strong ability to generate codes for productive activities. However, current benchmarks for code synthesis, such as HumanEval, MBPP, an…
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Yifan Xu, Xiao Liu, Xinghan Liu +9
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving. While many s…
ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback
Zhenyu Hou, Yilin Niu, Zhengxiao Du +8
ChatGLM is a free-to-use AI service powered by the ChatGLM family of large language models (LLMs). In this paper, we present the ChatGLM-RLHF pipeline -- a reinforcement learning f…
AutoWebGLM: A Large Language Model-based Web Navigating Agent
Hanyu Lai, Xiao Liu, Iat Long Iong +8
Large language models (LLMs) have fueled many intelligent web agents, but most existing ones perform far from satisfying in real-world web navigation tasks due to three factors: (1…
Extensive Self-Contrast Enables Feedback-Free Language Model Alignment
Xiao Liu, Xixuan Song, Yuxiao Dong +1
Reinforcement learning from human feedback (RLHF) has been a central technique for recent large language model (LLM) alignment. However, its heavy dependence on costly human or LLM…
Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments
Yu Gu, Yiheng Shu, Hao Yu +6
The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist agents capab…