Search Results for author:
Found 14 papers, 9 papers with code
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
FastMoE: A Fast Mixture-of-Expert Training System
However, training trillion-scale MoE requires algorithm and system co-design for a well-tuned high performance distributed training system.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (OCNLI_50K)
Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration
We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e. g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques.
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM).
AgentBench: Evaluating LLMs as Agents
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding.
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities.
CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation
Since the natural language processing (NLP) community started to make large language models (LLMs), such as GPT-4, act as a critic to evaluate the quality of generated texts, most of them only train a critique generation model of a specific scale on specific datasets.
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework.
APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
The massive adoption of large language models (LLMs) demands efficient deployment strategies.
Understanding Emergent Abilities of Language Models from the Loss Perspective
Recent studies have put into question the belief that emergent abilities in language models are exclusive to large models.
ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback
The work presents our practices of aligning LLMs with human preferences, offering insights into the challenges and solutions in RLHF implementations.
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving.
