Qian Liu @ Sea AI Lab

Qian Liu (刘乾)

Resume

Scholar

Github

Twitter

I am a Research Scientist at

Sea AI Lab, and I work with many talented researchers.

My primary research interests are in natural language processing, particularly code generation, table pre-training and natural langauge reasoning.

I have been fortunate to work with an amazing set of researchers in the past. I did my Ph.D. thesis at the joint program of Beihang University and Microsoft Research Asia where I was advised by Jian-Guang Lou and Bei Chen. The research topic during my Ph.D. journey is semantic parsing, which aims to translate a user’s natural language sentences into machine-executable formal programming language to accomplish relevant tasks. My thesis revolves around my efforts to develop cost-effective, generalizable, and interactive semantic parsing systems.

During my Ph.D. time, there are three academic achievements that have been most significant to me:

In 2020, I was honored to be nominated for the Baidu Scholarship (20 worldwide), which motivated me to seek out significant research.
In 2021, we (together with 11 other young researchers from around the world) launched the MLNLP community to enhance communication among the Chinese ML and NLP communities.
In 2022, our paper TAPEX received the highest score (with 31 awesome papers) in the initial round of ICLR 2022 reviews.

Prior to my time in research, I served as teaching assistant (leader) for a number of courses, including computer organization, operating systems, and software engineering. Furthermore, I directed the writing of an operating system experiment course manual. In 2017, I formed the first undergraduate teaching assistant organization S.T.A.R. to share my expertise as a TA with younger TAs.

For more details, check my CV or hit me up on my email.

[2023.05] I am currently hiring research interns in Singapore and China. Please do not hesitate to contact me if you are interested in my background, with the subject line "Research Intern + Your Name" with your resume. You can find the email at this website!

[2023.05] Please checkout our TapTap paper to see how language models pre-trained on tables benefit machine learning models!

[2023.05] The best open-source code pre-training model StarCoder (15.5B) was out!

[2023.04] Our preprint From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning was out! You can do instruction tuning even with symbolic data!

[2023.01] Our paper on Graph + Language is accepted by ICLR 2023 as Oral (5%)!

[2022.12] Our open-sourced code pre-training model SantaCoder was released!

[2022.11] 1 paper using table as the eye 👀 for embodied agents 🤖️ is accepted by AAAI 2023!

[2022.10] 4 papers (numerical reasoning, spatial reasoning, table retrieval & semantic parsing) got accepted by EMNLP 2022!

[2022.05] 2 papers (open-domain question answering & task-oriented dialogue) got accepted by IJCAI 2022!

[2021.12] 1 paper on table pre-training got accepted by ICLR 2022!

Generative Table Pre-training Empowers Models for Tabular Prediction
Tianping Zhang, Shaowen Wang, Shuicheng Yan, Jian Li, Qian Liu
PDF | Code

StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
The Best Open-Access Code Pre-training Model
PDF | Code

From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning
Qian Liu*, Fan Zhou*, Zhengbao Jiang, Longxu Dou, Min Lin
PDF | Code

Learning on Large-scale Text-attributed Graphs via Variational Inference
Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang
ICLR 2023 (Oral) | International Conference on Learning Representations
PDF | Code

SantaCoder: don't reach for the stars!
Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra
Open-sourced Code Pre-training Model
PDF | Model

Reasoning Like Program Executors
Xinyu Pi*, Qian Liu*, Bei Chen, Morteza Ziyadi, Zeqi Lin, Yan Gao, Qiang Fu, Jian-Guang Lou, Weizhu Chen (* = Equal Contribution)
Distinguished Contribution Award (2/300+) on Microsoft 2022 MLADS Spring
EMNLP 2022 (Oral) | Empirical Methods in Natural Language Processing
PDF | Video

LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
Qi Shi, Qian Liu, Bei Chen, Yu Zhang, Ting Liu, Jian-Guang Lou EMNLP 2022 (Findings) | Empirical Methods in Natural Language Processing
PDF | Code

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing
Qian Liu*, Dejian Yang*, Jiahui Zhang*, Jiaqi Guo, Bin Zhou, Jian-Guang Lou (* = Equal Contribution)
ACL 2021 (Findings) | Association for Computational Linguistics
PDF| Slides | Cite | Video

ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
Shuang Chen*, Qian Liu*, Zhiwei Yu*, Chin-Yew Lin, Jian-Guang Lou, Feng Jiang (* = Equal Contribution)
ACL 2021 (Demo) | Association for Computational Linguistics
PDF | Code | Cite | Video

Compositional Generalization by Learning Analytical Expressions
Qian Liu*, Shengnan An*, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang (* = Equal Contribution)
First Paper to Achieve 100% Accuracy on SCAN
NeurIPS 2020 (Spotlight) | Advances in Neural Information Processing Systems
PDF | Slides | Code | Cite | Video | Video(Chinese) | Blog(Chinese)

[2022. 10-12] Language Pre-training without Natural Language (Invited Talk)
📖 Language model with large-scale textual data has been successful but lacks reasoning ability due to limited reasoning data. This talk suggests using programs instead of language for pre-training corpus to improve reasoning in tasks such as tabular, numerical, and spatial reasoning.
Slides | Video

Venue: Carnegie Mellon University (CMU) Host: Frank Xu
Venue: Sigma Computing Host: Madelon Hulsebos
Venue: National University Singapore (NUS) Host: Prof. Min-Yen Kan
Venue: Singapore University of Technology & Design (SUTD) Host: Prof. Wei Lu
Venue: Nanyang Technological University (NTU) Host: Prof. Luu Anh Tuan

[2022. 09] Introduction to Language Models (Tutorial)
📖 The tutorial will give a brief overview of mainstream language model architectures (ELMo, GPT, BERT), giant language models (GPT3, Chinchilla), retrieval-based language models (REALM, kNN-LM), and interesting trends (scaling law, instruction following, parameter efficiency).
Slides

Venue: Sea AI Lab (SAIL)

[2022. 06] Semantic Parsing of Natural Language from Weakly Labeled Data (Ph.D. Defense)
📖 Focus on compositional and domain generalization of semantic parsing, answer-driven semantic parsing under weak supervision, and conversational semantic parsing under semi-supervision.
Slides(Chinese) | Thesis(Chinese)

Venue: Beihang University (BUAA) Host: Prof. Maosong Sun

[2022.01-02] Towards Data-Efficient Semantic Parsing (Job Talk)
📖 Build methods to improve semantic parsers' performance and generalization capacity under program data, task data, or even no data, and integrated the research into real product PowerApp.
Slides

Venue: Sea AI Lab (SAIL) Host: Dr. Min Lin
Venue: Microsoft Research Asia (MSRA) Host: Dr. Jian-Guang Lou

[2022. 01] How to Find a Research Job in Industry (Seminar)
📖 Discuss the critical processes in seeking a good job, such as resume preparation, coding exercises, project discussions, and behavior questions.
Video(Chinese)| Slides(Chinese)

Venue: MLNLP Community Host: Bei Li

[2021.07] On the Future of Semantic Parsing (Seminar)
📖 Discuss the past, current and future of semantic parsing with other rising stars in semantic parsing.
Video(Chinese)| Blog(Chinese)

Venue: AI TIME Speaker: Dr. Pengcheng Yin, Dr.Ziyu Yao, Dr.Bailin Wang

Please feel free to contact me via my email (left) if you are interested in our papers, my experience, or you just have any problem on research which I may help.

Beihang University

2013 - 2017

Microsoft Research Asia

2017 - 2022

Sea AI Lab

2022 - Present