王业全

研究员，团队主管

北京智源人工智能研究院

个人简介

王业全是北京人工智能研究院的一位科研团队主管，研究兴趣主要包含大模型 (Large Model) 。他带领认知模型团队，旨在构建更低成本但能力更强的大模型，并开展相关的的大模型研究，并最终实现 AGI 的目标。

王业全博士在朱小燕教授和黄民烈教授的指导下，在清华大学获得计算机科学博士学位。其中，2017年到2018年，在南洋理工大学以访问博士的方式跟随孙爱欣教授学习。

2022年，获得2022年全球最具影响力人工智能学者提名奖(自然语言处理)。

2022年11月-2025年11月，主持“新一代人工智能”国家科技重大专项。

大模型研发的团队理念：系统能力和科研能力缺一不可。没有系统能力，就无法研发大模型。没有科研能力，只能亦步亦趋，在大模型领跑者选择闭源的情况下，无法进一步突破。

谷歌学术引用: 3,500+

ORCID: 0000-0001-7530-6125

兴趣爱好

大模型 (Large Model)
具身智能 (Emboddied AI)
自然语言处理 (Natural Language Processing)

教育经历

人工智能博士, 2014-2019

清华大学, 北京
人工智能联合培养博士, 2017-2018

南洋理工大学, 新加坡

项目

FLM 系列大模型

FLM 系列大模型是 Cofe-AI 团队牵头完成的一系列大模型，包含FLM-2 (Tele-FLM-1T, Tele-FLM-52B, FLM-2-52B-Instruct等)，FLM-101B 和 FreeLM。其核心技术主要为生长技术，损失预测技术和 FreeLM 框架等。

阿拉伯语语言大模型 (ALM 1.0)

我们构建并开源了阿拉伯语语言大模型 (ALM 1.0)。

人工智能基础模型支撑平台与评测技术

科技部“新一代人工智能国家科技重大专项”：本项目以大模型建设为抓手，以促进我国超大规模智能模型前沿技术发展、促进人工智能赋能经济社会发展为目标，构建国际领先的基础模型开源技术体系，建立以基础模型为核心的人工智能开放创新生态。

面向互联网复杂语境的文本隐式情感分析研究

国家自然科学基金 (NSFC) 青年基金项目(62106249)：隐式情感分析则通过对典型隐式情感，如反讽等进行分析识别。并对对比情感分析等细粒度的情感进行探索。

代表性论文

Masked Structural Growth for 2x Faster Language Model Pre-training

To lower the computional cost of training large model, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one.

Yiqun Yao, Zheng Zhang, Jing Li, Yequan Wang

52B to 1T: Lessons Learned via Tele-FLM Series

As scaling laws underscore the potential of increasing model sizes, the academic community has intensified its investigations into LLMs with capacities exceeding 50 billion parameters. This technical report builds on our prior work with Tele-FLM (also known as FLM-2), a publicly available 52-billion-parameter model.

Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, China Telecom, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang

Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales

We find that Maximal Update parametrization (uP) enables accurate fitting of scaling laws for hyperparameters close to common loss basins, without any search. Thus, different models can be directly compared on large scales with loss prediction even before the training starts. We propose a new paradigm as a first step towards reliable academic research for any model scale without heavy computation.

Yiqun Yao, Yequan Wang

Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales

Sentiment Analysis by Capsules

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis.

Yequan Wang, Aixin Sun, Jialong Han, Ying Liu, Xiaoyan Zhu

Attention-based LSTM for Aspect-level Sentiment Classification

To reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification.

Yequan Wang, Minlie Huang, Li Zhao, Xiaoyan Zhu

Attention-based LSTM for Aspect-level Sentiment Classification

论文

Yiqun Yao, Zheng Zhang, Jing Li, Yequan Wang (2028). Masked Structural Growth for 2x Faster Language Model Pre-training.

PDF 引用代码项目项目

Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, China Telecom, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang (2024). 52B to 1T: Lessons Learned via Tele-FLM Series.

PDF 引用项目 Tele-FLM-1T Tele-FLM

Yiqun Yao, Yequan Wang (2023). Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales.

PDF 引用项目

Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng (2022). CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts. In Findings of the EMNLP 2022.

PDF 引用代码数据集项目

Ting Lin, Aixin Sun, Yequan Wang (2022). EDU-Capsule: aspect-based sentiment analysis at clause level. KAIS.

PDF 引用代码 DOI

Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo (2022). CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction. In COLING 2022.

PDF 引用代码数据集项目

Xuying Meng, Yequan Wang, Runxin Ma, Haitong Luo, Xiang Li, Yujun Zhang (2022). Packet Representation Learning for Traffic Classification. In KDD 2022.

PDF 引用代码 DOI

Siqi Fan, Yequan Wang, Jing Li, Zheng Zhang, Shuo Shang, Peng Han (2022). Interactive Information Extraction by Semantic Information Graph. In IJCAI 2022.

PDF 引用代码数据集

Yiyi Liu, Yequan Wang, Aixin Sun, Xuying Meng, Jing Li, Jiafeng Guo (2022). A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict. In Findings of NAACL 2022.

PDF 引用代码数据集

Xi Lin, Yequan Wang, Xiaokui Xiao, Zengxiang Li, Sourav S. Bhowmick (2019). Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network. In CIKM 2019.

PDF 引用 DOI