Research Scientist Intern at
DeepMind
London, UK
2022.06 ~ 2022.10
Supervised by Arthur Mensch, Igor Babuschkin, and Laurent Sifre
Hi! I'm Linqing Liu, a software engineer working on the Applied AI Team in Databricks.
Before joining Databricks, I earned my PhD from University College London, where I was fortunate to be advised by Pontus Stenetorp and Sebastian Riedel in the Natural Language Processing Group. My work focuses on building accurate and efficient systems (e.g. question answering) that enable individuals to access the most up-to-date knowledge in real-world scenarios. My research spans across building large-scale retrieval-augmented language models, developing more generalizable open domain QA systems, and interpreting model behavior.
Here is my CV.
Selected Publications
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
Raphael Tang*, Linqing Liu*, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin and Ferhan Ture (*: equal contribution)
ACL, 2023
Best Paper Award
Query Expansion Using Contextual Clue Sampling with Language Models
Linqing Liu, Minghan Li, Jimmy Lin, Sebastian Riedel and Pontus Stenetorp
Arxiv Preprint, 2022
When Do Flat Minima Optimizers Work?
Jean Kaddour*, Linqing Liu*, Ricardo Silva and Matt J. Kusner (*: equal contribution)
NeurIPS, 2022
Challenges in Generalization in Open Domain Question Answering
Linqing Liu, Patrick Lewis, Sebastian Riedel and Pontus Stenetorp
NAACL Findings, 2022
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Kuttler, Aleksandra Piktus, Pontus Stenetorp, Sebastian Riedel
TACL, 2021
[Project]
Controllable Abstractive Dialogue Summarization with Sketch Supervision
Chien-Sheng Wu*, Linqing Liu*, Wenhao Liu, Pontus Stenetorp, Caiming Xiong (*: equal contribution)
ACL-IJCNLP Findings, 2021
[Project]
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models
Linqing Liu, Huan Wang, Jimmy Lin, Richard Socher and Caiming Xiong
Arxiv Preprint, 2020
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling
Linqing Liu, Wei Yang, Jinfeng Rao, Raphael Tang and Jimmy Lin
EMNLP, 2019
[Project]   [slides]
Bridging the Gap between Relevance Matching and Semantic Matching with Hierarchical Co-Attention Network
Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi and Jimmy Lin
EMNLP, 2019
[Project]
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
Raphael Tang*, Yao Lu*, Linqing Liu*, Lili Mou, Olga Vechtomova, Jimmy Lin (*: equal contribution)
Arxiv Preprint, 2019
Generative Adversarial Network for Abstractive Text Summarization
Linqing Liu, Yao Lu, Min Yang, Qiang Qu, and Jia Zhu
The 30th AAAI Conference on Artificial Intelligence (AAAI, student poster), 2018
[supplementary file][output summary]
Internship
Research Intern at
Salesforce Research
Palo Alto, USA
2019.09 ~ 2020.08
Supervised by Caiming Xiong
Research Intern at
Software Analysis and Intelligence Lab (SAIL)
Queen's University, Kingston, Canada
2016.06 ~ 2016.09
Supervised by Ahmed E. Hassan and
Cor-Paul Bezemer
Awards and Honors
- ACL Best Paper Award, 2023
- David R. Cheriton Graduate Scholarship, 2018
- AAAI Student Scholarship, 2018
- Google Anita Borg Scholarship: Asia Pacific, 2016
- NAACL Student Scholarship, 2016