Xuanjing Huang (黄萱菁)

Xuanjing Huang (黄萱菁)

Professor at Fudan University, 复旦大学教授

学术论文

本页面展示了我的一部分学术论文。如需查看更完整的论文列表，请访问我的Google Scholar 个人主页、Semantic Scholar 个人主页、计算机科学文献数据库或ACL 论文集。

大模型智能体 · AI 科学家 · 价值对齐与 RLHF · 基础模型、评测与应用 · 可解释性与认知 · 预训练模型与经典 NLP · 著作

大模型智能体

Scaling Verifiable Environments for Long-horizon Work Agents

Published in arXiv, 2026

WorkForge 从真实世界资源出发，自动构建可验证的长程工作智能体训练环境，并把任务、解法和验证器都锚定到工作空间中的可检查事实。该框架构建了覆盖 40 个专业领域的 1.67 万个环境，用于后训练后显著提升工作智能体在 GDPVal 等评测上的表现。

Recommended citation: Jiazheng Zhang, Long Ma, Yunxian Yang, Zhiheng Xi, Zhikai Lei, Yajie Yang, Chenyang Liao, Enyu Zhou, Yang Nan, Yuchen Tian, Senjie Jin, Yibo Wang, Wei He, Boyang Liu, Jixuan Huang, Xin Guo, Shutong Jiang, Zhezheng Hao, Xinbing Liang, Zhihao Zhang, Changzhi Zhou, Wiggin Zhou, Tao Gui, Qi Zhang, Xuanjing Huang, et al. Scaling Verifiable Environments for Long-horizon Work Agents. arXiv:2610.04906, 2026. https://arxiv.org/abs/2610.04906

SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm

Published in arXiv, 2026

SocioVerse2 是面向社会科学研究的开源社会模拟框架，支持在长程模拟中插入干预、分叉出反事实分支，并让研究者控制研究过程本身。论文通过七个案例展示其在经典智能体建模复现、政策过程建模和宏观指数预测等任务中的应用。

Recommended citation: Xinnong Zhang, Jiayu Lin, Jia Wang, Yixu Huang, Xinyi Mou, Yingqian Wu, Jingcong Liang, Shijun Lei, Jianing Shi, Guanying Li, Siyuan Wang, Hanjia Lyu, Zhenfei Yin, Yunlu Yin, Siming Chen, Yulan He, Jiebo Luo, Xuanjing Huang, Liyin Jin, Baohua Zhou, Hanqi Yan, Zhongyu Wei. SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm. arXiv:2609.24911, 2026. https://arxiv.org/abs/2609.24911

AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress

Published in The ACM Web Conference 2026 (WWW 2026), 2026

We propose AgentPRM, a re-defined process reward model for LLM agent tasks that captures both the interdependence between sequential decisions and their contribution to the final goal, enabling better progress tracking and exploration-exploitation balance.

Recommended citation: Zhiheng Xi, Chenyang Liao, Guanyu Li, Yajie Yang, Wenxiang Chen, Zhihao Zhang, Bing Wang, Senjie Jin, Yuhao Zhou, Jian Guan, Wei Wu, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang. AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress. In Proceedings of the ACM Web Conference 2026 (WWW 2026). https://arxiv.org/abs/2511.08325

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

Published in arXiv, 2026

Agentic Harness Engineering（AHE）让编程智能体自动演化自身 harness：它把可编辑组件、海量轨迹证据和每次修改的预期效果都显式化，使 harness 迭代可以被观察和验证。经过 10 轮自动演化，Terminal-Bench 2 通过率从 69.7% 提升到 77.0%，并可迁移到 SWE-bench Verified 和其他模型系列。

Recommended citation: Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Zhiheng Xi, Xuanjing Huang, Hang Yan, Zhenhua Han, Tao Gui, Yu-Gang Jiang. Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses. arXiv:2604.25850, 2026. https://arxiv.org/abs/2604.25850

AgentGym-RL: An Open-Source Framework to Train LLM Agents for Long-Horizon Decision Making via Multi-Turn RL

Published in International Conference on Learning Representations (ICLR 2026), Oral, 2026

We propose AgentGym-RL, an open-source, modular framework for training LLM agents from scratch with multi-turn reinforcement learning across diverse real-world environments, together with ScalingInter-RL, a staged training approach for stable long-horizon RL.

Recommended citation: Zhiheng Xi, Jixuan Huang, Chenyang Liao, Baodai Huang, Jiaqi Liu, Honglin Guo, Yajie Yang, Rui Zheng, Junjie Ye, Jiazheng Zhang, Wenxiang Chen, Wei He, Yiwen Ding, Guanyu Li, Zehui Chen, Zhengyin Du, Xuesong Yao, Yufei Xu, Jiecao Chen, Tao Gui, Zuxuan Wu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang. AgentGym-RL: An Open-Source Framework to Train LLM Agents for Long-Horizon Decision Making via Multi-Turn RL. ICLR 2026 (Oral). https://openreview.net/forum?id=ZgCCDwcGwn

From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents

Published in ACM Computing Surveys, 2026

这篇综述系统梳理了大语言模型驱动的社会模拟研究，按照个体模拟、场景模拟和社会模拟三个层次总结方法、任务与评估。它讨论了生成式智能体如何作为“硅基样本”支持社会科学实验，也指出了可信性、可控性和伦理等开放问题。

Recommended citation: Xinyi Mou, Xuanwen Ding, Qi He, Liang Wang, Jingcong Liang, Xinnong Zhang, Libo Sun, Jiayu Lin, Jie Zhou, Xuanjing Huang, Zhongyu Wei. From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents. ACM Computing Surveys, 58(11): 1-41, 2026. https://doi.org/10.1145/3800683

MagicAgent: Towards Generalized Agent Planning

Published in arXiv, 2026

MagicAgent 研究面向真实数字环境的通用智能体能力，强调在多步骤任务中理解环境、调用工具并根据反馈持续调整行动。论文围绕智能体任务构建、评测和能力提升展开，服务于更稳定的 GUI 与工具使用智能体。

Recommended citation: Xuhui Ren, Shaokang Dong, Chen Yang, Qing Gao, Yunbin Zhao, Yongsheng Liu, Xinwei Geng, Xiang Li, Demei Yan, Yanqing Li, Chenhao Huang, Dingwei Zhu, Junjie Ye, Boxuan Yue, Yingnan Fu, Mengzhe Lv, Zezeng Feng, Boshen Zhou, Bocheng Wang, Xuanjing Huang, Yu-Gang Jiang, Tao Gui, Qi Zhang, Yunke Zhang. MagicAgent: Towards Generalized Agent Planning. arXiv:2602.19000, 2026. https://arxiv.org/abs/2602.19000

Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction

Published in arXiv, 2025

Nex-N1 面向大语言模型的复杂推理能力，通过过程监督与强化学习相结合的方法提升模型在数学、代码和通用推理任务上的表现。论文围绕可扩展训练流程、奖励建模和推理能力提升展开，是团队在下一代推理模型方向上的代表性工作。

Recommended citation: Nex-AGI Team (Yuxuan Cai, Lu Chen, Qiaoling Chen, et al., including Xuanjing Huang). Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction. arXiv:2512.04987, 2025. https://arxiv.org/abs/2512.04987

MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning

Published in arXiv, 2025

MagicGUI 面向图形用户界面智能体，研究如何让模型理解屏幕内容、执行操作并完成真实软件环境中的任务。论文关注 GUI 任务中的感知、规划与交互，为构建更可靠的桌面和移动端智能体提供基准与方法。

Recommended citation: Liujian Tang, Shaokang Dong, Yijia Huang, Minqi Xiang, Hongtao Ruan, Bin Wang, Shuo Li, Zhiheng Xi, Zhihui Cao, Hailiang Pang, Heng Kong, He Yang, Mingxu Chai, Zhilin Gao, Xingyu Liu, Yingnan Fu, Jiaming Liu, Xuanjing Huang, Yu-Gang Jiang, Tao Gui, Qi Zhang, Kang Wang, Yunke Zhang, Yuran Wang. MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning. arXiv:2508.03700, 2025. https://arxiv.org/abs/2508.03700

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments

Published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Large language models (LLMs) have emerged as a promising foundation to build generally-capable agents (LLM-based agents) that can handle multi-turn decision-making tasks across various environments. However, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents, and enables exploration and learning for their self-improvement.

Recommended citation: Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang. AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27914–27961, Vienna, Austria. Association for Computational Linguistics, 2025. https://aclanthology.org/2025.acl-long.1355/

The Rise and Potential of Large Language Model Based Agents: A Survey

Published in Science China Information Sciences, 2025

In this paper, we perform a comprehensive survey on LLM-based agents.

Recommended citation: Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wenjuan Qin, Yongyan Zheng, Xipeng Qiu, Xuanjing Huang, Qi Zhang, Tao Gui: The rise and potential of large language model based agents: a survey. Sci. China Inf. Sci. 68(2) (2025) https://link.springer.com/article/10.1007/s11432-024-4222-0

AI 科学家

Atria Dawn: The Dawn of Agentic Superintelligence

Published in arXiv, 2026

Atria Dawn 是面向科研与工程任务的基础智能体语言模型，通过可验证经验管线训练，并以模型自身研发过程为案例分析人机协作形态。模型基于 7440 亿参数 MoE 基座，支持 256K 上下文，在 16 项真实科研、工程与数字工作基准上与前沿智能体相当，其中 5 项取得当前最高成绩。

Recommended citation: Atria Team (Honglin Guo, Tao Gui, Kun Cai, et al., including Xuanjing Huang). Atria Dawn: The Dawn of Agentic Superintelligence. arXiv:2609.15818, 2026. https://arxiv.org/abs/2609.15818

AI Can Learn Scientific Taste

Published in arXiv, 2026

我们提出了基于社区反馈的强化学习（RLCF），这是一种利用大规模社区信号作为监督的训练范式，并将科学品味学习形式化为偏好建模和对齐问题。

Recommended citation: Jingqi Tong, Mingzhe Li, Hangcheng Li, Yongzhuo Yang, Yurong Mou, Weijie Ma, Zhiheng Xi, Hongji Chen, Xiaoran Liu, Qinyuan Cheng, Ming Zhang, Qiguang Chen, Weifeng Ge, Qipeng Guo, Tianlei Ying, Tianxiang Sun, Yining Zheng, Xinchi Chen, Jun Zhao, Ning Ding, Xuanjing Huang, Yugang Jiang, Xipeng Qiu: AI Can Learn Scientific Taste. arXiv:2603.14473, 2026. https://arxiv.org/pdf/2603.14473

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents

Published in International Conference on Machine Learning (ICML 2026), 2026

我们提出了SciAgentGym，一个可扩展的交互环境，包含跨4个自然学科的1780个领域特定工具，并配有强大的执行基础设施。

Recommended citation: Yujiong Shen, Yajie Yang, Zhiheng Xi, Binze Hu, Huayu Sha, Jiazheng Zhang, Qiyuan Peng, Junlin Shang, Jixuan Huang, Yutao Fan, Jingqi Tong, Shihan Dou, Ming Zhang, Lei Bai, Zhenfei Yin, Tao Gui, Xingjun Ma, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang: SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents. ICML 2026 https://arxiv.org/pdf/2602.12984

OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment

Published in arXiv, 2026

We introduce OpenNovelty, an LLM-powered agentic system for verifiable scholarly novelty assessment that addresses the critical need for automated, reliable evaluation of research novelty.

Recommended citation: Yifan Liu, Yifan Wang, Zixuan Li, Zizheng Wang, Zihan Wang, Wenxuan Wang, Yifei Wang, Yifan Song, Yifan Liu, Xuanjing Huang, Zhilin Yang, Wei Chen, Tao Gui: OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment. arXiv preprint arXiv:2601.01576 (2026) https://arxiv.org/abs/2601.01576

价值对齐与 RLHF

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Published in arXiv, 2024

From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization.

Recommended citation: Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, Songyang Gao, Nuo Xu, Yuhao Zhou, Xiaoran Fan, Zhiheng Xi, Jun Zhao, Xiao Wang, Tao Ji, Hang Yan, Lixing Shen, Zhan Chen, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang: Secrets of RLHF in Large Language Models Part II: Reward Modeling. arXiv:2401.06080, 2024. http://xuanjing-huang.github.io/files/reward.pdf

Secrets of RLHF in Large Language Models Part I: PPO

Published in arXiv, 2023

We dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training.

Recommended citation: Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang: Secrets of RLHF in Large Language Models Part I: PPO. arXiv:2307.04964, 2023. http://xuanjing-huang.github.io/files/rlhf.pdf

基础模型、评测与应用

CL-bench: A Benchmark for Context Learning

Published in arXiv, 2026

CL-bench 是面向“上下文学习”能力的真实任务基准，考察模型能否从任务给定的复杂上下文中学习新知识并解决问题。基准包含 500 个专家构建的上下文、1,899 个任务和 31,607 条验证规则，显示当前前沿模型在这种能力上仍有明显不足。

Recommended citation: Shihan Dou, Ming Zhang, Zhangyue Yin, Chenhao Huang, Yujiong Shen, Junzhe Wang, Jiayi Chen, Yuchen Ni, Junjie Ye, Cheng Zhang, Huaibing Xie, Jianglu Hu, Shaolei Wang, Weichao Wang, Yanling Xiao, Yiting Liu, Zenan Xu, Zhen Guo, Pluto Zhou, Tao Gui, Zuxuan Wu, Xipeng Qiu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Di Wang, Shunyu Yao. CL-bench: A Benchmark for Context Learning. arXiv:2602.03587, 2026. https://arxiv.org/abs/2602.03587

What is wrong with your code generated by large language models? An extensive study

Published in Science China Information Sciences, 2026

The increasing development of large language models (LLMs) in code generation has drawn significant attention among researchers. To enhance LLM-based code generation ability, current efforts are predominantly directed towards collecting high-quality datasets and leveraging diverse training technologies. However, there is a notable lack of comprehensive studies examining the limitations and boundaries of existing methods.

Recommended citation: Shihan Dou, Haoxiang Jia, Shenxi Wu, Huiyuan Zheng, Muling Wu, Yunbo Tao, Ming Zhang, Mingxu Chai, Jessica Fan, Zhiheng Xi, Rui Zheng, Yueming Wu, Ming Wen, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang: What is wrong with your code generated by large language models? An extensive study. Sci. China Inf. Sci. 69, 112107 (2026) https://doi.org/10.1007/s11432-025-4632-8

Searching for Best Practices in Retrieval-Augmented Generation

Published in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times.

Recommended citation: Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang. Searching for Best Practices in Retrieval-Augmented Generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17716–17736. Association for Computational Linguistics, 2024. https://aclanthology.org/2024.emnlp-main.981/

可解释性与认知

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

Published in Findings of the Association for Computational Linguistics: ACL 2026; extended version in Computer Science Review, 2026

这篇综述关注可操作的机械可解释性，系统梳理如何从解释模型内部机制走向可干预、可验证和可改进模型行为的解释方法。文章总结了概念发现、回路分析、因果干预和可解释性驱动模型控制等方向，为大模型可解释性研究提供了结构化视角。

Recommended citation: Hengyuan Zhang, Zhihao Zhang, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Ercong Nie, Xufeng Duan, Feijiang Han, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schütze, Xuanjing Huang, Qi Zhang, Ngai Wong. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models. Findings of ACL 2026: 10317-10362. Extended version: Computer Science Review, 62: 101011, 2026. https://doi.org/10.18653/v1/2026.findings-acl.502

Revealing emergent human-like conceptual representations from language prediction

Published in Proceedings of the National Academy of Sciences (PNAS), 2025

People acquire concepts through rich physical and social experiences and use them to understand and navigate the world. In contrast, large language models (LLMs), trained solely through next-token prediction on text, exhibit strikingly human-like behaviors. Are these models developing concepts akin to those in humans?

Recommended citation: Ningyu Xu, Qi Zhang, Chenyang Du, Qinan Luo, Xipeng Qiu, Xuanjing Huang, Menghan Zhang. Revealing emergent human-like conceptual representations from language prediction. Proceedings of the National Academy of Sciences (PNAS), 2025. https://doi.org/10.1073/pnas.2512514122

预训练模型与经典 NLP

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

Published in Findings of the Association for Computational Linguistics: ACL-IJCNLP, 2021

The paper proposes a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused model.

Recommended citation: Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou: K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters. ACL/IJCNLP (Findings) 2021: 1405-1418 http://xuanjing-huang.github.io/files/K-Adapter.pdf

FLAT: Chinese NER Using Flat-Lattice Transformer

Published in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans.

Recommended citation: Xiaonan Li, Hang Yan, Xipeng Qiu, Xuanjing Huang: FLAT: Chinese NER Using Flat-Lattice Transformer. ACL 2020: 6836-6842 http://xuanjing-huang.github.io/files/FLAT.pdf

Adversarial Multi-Criteria Learning for Chinese Word Segmentation

Published in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria.

Recommended citation: Xinchi Chen, Zhan Shi, Xipeng Qiu, Xuanjing Huang: Adversarial Multi-Criteria Learning for Chinese Word Segmentation. ACL (1) 2017: 1193-1203 http://xuanjing-huang.github.io/files/cws.pdf

Adversarial Multi-task Learning for Text Classification

Published in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

The paper proposed an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other.

Recommended citation: Pengfei Liu, Xipeng Qiu, Xuanjing Huang: Adversarial Multi-task Learning for Text Classification. ACL (1) 2017: 1-10 http://xuanjing-huang.github.io/files/AMT.pdf

著作

大规模语言模型：从理论与实践（第2版）

Published in 电子工业出版社, 2025

本书将介绍大语言模型的基础理论包括语言模型、分布式模型训练以及强化学习，并以Deepspeed-Chat框架为例介绍实现大语言模型和类ChatGPT系统的实践。

Recommended citation: 张奇、桂韬、郑锐、黄萱菁：大规模语言模型：从理论与实践（第2版），电子工业出版社，2025 https://intro-llm.github.io/

自然语言处理导论

Published in 电子工业出版社, 2023

随着自然语言处理的广泛应用以及以深度学习为代表的机器学习算法的快速进步，近年来自然语言处理算法和研究任务也在快速发展中。作者自2003年起，在复旦大学计算机科学技术学院针对本科生、硕士生和博士生先后分别开设了自然语言处理课程。本书对多年教学和研究进行总结梳理，希望使得读者对自然语言处理有更加系统性且全面的了解。

Recommended citation: 张奇、桂韬、黄萱菁：自然语言处理导论，电子工业出版社，2023 https://intro-nlp.github.io/