Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT. - Lichao Sun 001; Kazuma Hashimoto; Wenpeng Yin 001; Akari Asai; Jia Li 015; Philip S. Yu; Caiming Xiong (CoRR, 2020)

On the Diversity and Explainability of Recommender Systems: A Practical Framework for Enterprise App Recommendation. - Wenzhuo Yang; Jia Li 015; Chenxi Li; Latrice Barnett; Markus Anderle; Simo Arajärvi; Harshavardhan Utharavalli; Caiming Xiong; Steven C. H. Hoi (CIKM, 2021)

CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers. - Shiyang Li; Semih Yavuz; Kazuma Hashimoto; Jia Li 015; Tong Niu; Nazneen Fatema Rajani; Xifeng Yan; Yingbo Zhou; Caiming Xiong (ICLR, 2021)

Contrastive Self-supervised Sequential Recommendation with Robust Augmentation. - Zhiwei Liu 001; Yongjun Chen; Jia Li 015; Philip S. Yu; Julian J. McAuley; Caiming Xiong (CoRR, 2021)

Modeling Dynamic Attributes for Next Basket Recommendation. - Yongjun Chen; Jia Li 015; Chenghao Liu; Chenxi Li; Markus Anderle; Julian J. McAuley; Caiming Xiong (CoRR, 2021)

Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution. - Wenpeng Yin 001; Shelby Heinecke; Jia Li 015; Nitish Shirish Keskar; Michael Jones 002; Shouzhong Shi; Stanislav Georgiev; Kurt Milich; Joseph Esposito; Caiming Xiong (CoRR, 2021)

ConTinTin: Continual Learning from Task Instructions. - Wenpeng Yin 001; Jia Li 015; Caiming Xiong (ACL (1), 2022)

ELECRec: Training Sequential Recommenders as Discriminators. - Yongjun Chen; Jia Li 015; Caiming Xiong (SIGIR, 2022)

RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems. - Zohreh Ovaisi; Shelby Heinecke; Jia Li 015; Yongfeng Zhang; Elena Zheleva; Caiming Xiong (WSDM, 2022)

Intent Contrastive Learning for Sequential Recommendation. - Yongjun Chen; Zhiwei Liu 001; Jia Li 015; Julian J. McAuley; Caiming Xiong (WWW, 2022)

Improving Contrastive Learning with Model Augmentation. - Zhiwei Liu 001; Yongjun Chen; Jia Li 015; Man Luo; Philip S. Yu; Caiming Xiong (CoRR, 2022)

MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation. - Wenzhuo Yang; Jia Li 015; Caiming Xiong; Steven C. H. Hoi (CoRR, 2022)

Generating Negative Samples for Sequential Recommendation. - Yongjun Chen; Jia Li 015; Zhiwei Liu 001; Nitish Shirish Keskar; Huan Wang 016; Julian J. McAuley; Caiming Xiong (CoRR, 2022)

SantaCoder: don't reach for the stars! - Loubna Ben Allal; Raymond Li; Denis Kocetkov; Chenghao Mou; Christopher Akiki; Carlos Muñoz Ferrandis; Niklas Muennighoff; Mayank Mishra; Alex Gu; Manan Dey; Logesh Kumar Umapathi; Carolyn Jane Anderson; et al. (CoRR, 2023)

The Stack: 3 TB of permissively licensed source code. - Denis Kocetkov; Raymond Li; Loubna Ben Allal; Jia Li 015; Chenghao Mou; Yacine Jernite; Margaret Mitchell; Carlos Muñoz Ferrandis; Sean Hughes; Thomas Wolf 008; Dzmitry Bahdanau; Leandro von Werra; et al. (Trans. Mach. Learn. Res., 2023)

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 015; Jenny Chim; Qian Liu 012; Evgenii Zheltonozhskii; et al. (Trans. Mach. Learn. Res., 2023)

StarCoder 2 and The Stack v2: The Next Generation. - Anton Lozhkov; Raymond Li; Loubna Ben Allal; Federico Cassano; Joel Lamy-Poirier; Nouamane Tazi; Ao Tang; Dmytro Pykhtar; Jiawei Liu 004; Yuxiang Wei 003; Tianyang Liu 003; Max Tian; et al. (CoRR, 2024)