Template Directed Development of Web-Based Information Systems. - Ajit Singh; Raymond Li (IKE, 2003)

Towards Deep Conversational Recommendations. - Raymond Li; Samira Ebrahimi Kahou; Hannes Schulz; Vincent Michalski; Laurent Charlin; Chris Pal (NeurIPS, 2018)

On Extractive and Abstractive Neural Document Summarization with Transformer Language Models. - Jonathan Pilault; Raymond Li; Sandeep Subramanian; Chris Pal (EMNLP (1), 2020)

Video Monitoring Queries. - Nick Koudas; Raymond Li; Ioannis Xarchakos (ICDE, 2020)

T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP. - Raymond Li; Wen Xiao; Lanjun Wang; Hyeju Jang; Giuseppe Carenini (EMNLP (Demos), 2021)

DuoRAT: Towards Simpler Text-to-SQL Models. - Torsten Scholak; Raymond Li; Dzmitry Bahdanau; Harm de Vries; Chris Pal (NAACL-HLT, 2021)

NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions. - Rindranirina Ramamonjison; Timothy T. L. Yu; Raymond Li; Haley Li; Giuseppe Carenini; Bissan Ghaddar; Shiqi He; Mahdi Mostajabdaveh; Amin Banitalebi-Dehkordi; Zirui Zhou; Yong Zhang 004 (NeurIPS (Competition and Demos), 2021)

ConVIScope: Visual Analytics for Exploring Patient Conversations. - Raymond Li; Enamul Hoque; Giuseppe Carenini; Richard Lester; Raymond Chau (IEEE VIS (Short Papers), 2021)

byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings. - Xiang Zhang; Alexandre Drouin; Raymond Li (CoRR, 2021)

T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP. - Raymond Li; Wen Xiao; Lanjun Wang; Hyeju Jang; Giuseppe Carenini (CoRR, 2021)

Human Interpretation and Exploitation of Self-attention Patterns in Transformers: A Case Study in Extractive Summarization. - Raymond Li; Wen Xiao; Lanjun Wang; Giuseppe Carenini (CoRR, 2021)

Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation. - Raymond Li; Wen Xiao; Linzi Xing; Lanjun Wang; Gabriel Murray; Giuseppe Carenini (EMNLP, 2022)

OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction. - Raymond Li; Ilya Valmianski; Li Deng; Xavier Amatriain; Anitha Kannan (ML4H@NeurIPS, 2022)

Evaluating the Text-to-SQL Capabilities of Large Language Models. - Nitarshan Rajkumar; Raymond Li; Dzmitry Bahdanau (CoRR, 2022)

OSLAT: Open Set Label Attention Transformer for Medical Entity Span Extraction. - Raymond Li; Ilya Valmianski; Li Deng; Xavier Amatriain; Anitha Kannan (CoRR, 2022)

Diversity-Aware Coherence Loss for Improving Neural Topic Models. - Raymond Li; Felipe González-Pizarro; Linzi Xing; Gabriel Murray; Giuseppe Carenini (ACL (2), 2023)

Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models. - Raymond Li; Gabriel Murray; Giuseppe Carenini (EMNLP (Findings), 2023)

Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks. - David Valencia; John Jia; Raymond Li; Alex Hayashi; Megan Lecchi; Reuel Terezakis; Trevor Gee; Minas V. Liarokapis; Bruce A. MacDonald; Henry Williams (ICRA, 2023)

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)

Visual Analytics for Generative Transformer Models. - Raymond Li; Ruixin Yang; Wen Xiao; Ahmed AbuRa'ed; Gabriel Murray; Giuseppe Carenini (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)

SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder. - Mohammadreza Pourreza; Davood Rafiei; Yuxi Feng; Raymond Li; Zhenan Fan; Weiwei Zhang (CoRR, 2024)

DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning. - Yuxi Feng; Raymond Li; Zhenan Fan; Giuseppe Carenini; Mohammadreza Pourreza; Weiwei Zhang; Yong Zhang 004 (CoRR, 2024)

Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection. - Chuyuan Li; Raymond Li; Thalia Shoshana Field; Giuseppe Carenini (ACL (1), 2025)

Explicit Bayesian Inference to Uncover the Latent Themes of Large Language Models. - Raymond Li; Chuyuan Li; Gabriel Murray; Giuseppe Carenini (ACL (Findings), 2025)

DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning. - Raymond Li; Yuxi Feng; Zhenan Fan; Giuseppe Carenini; Weiwei Zhang; Mohammadreza Pourreza; Yong Zhang (COLING, 2025)

Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing. - Juntai Cao; Xiang Zhang; Raymond Li; Chuyuan Li; Shafiq Joty; Giuseppe Carenini (CoRR, 2025)

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training. - Oleksiy Ostapenko; Charles Guille-Escuret; Luke Kumar; Max Tian; Denis Kocetkov; Gopeshh Subbaraj; Raymond Li; Joel Lamy-Poirier; Sébastien Paquet; Torsten Scholak (CoRR, 2025)

Apriel-Nemotron-15B-Thinker. - Shruthan Radhakrishna; Soham Parikh; Gopal Sarda; Anil Turkkan; Quaizar Vohra; Raymond Li; Dhruv Jhamb; Kelechi Ogueji; Aanjaneya Shukla; Oluwanifemi Bamgbose; Toby Liang; Luke Kumar; et al. (CoRR, 2025)

CEMTM: Contextual Embedding-based Multimodal Topic Modeling. - Amirhossein Abaskohi; Raymond Li; Chuyuan Li; Shafiq Joty; Giuseppe Carenini (CoRR, 2025)

Apriel-H1: Towards Efficient Enterprise Reasoning Models. - Oleksiy Ostapenko; Luke Kumar; Raymond Li; Denis Kocetkov; Joel Lamy-Poirier; Shruthan Radhakrishna; Soham Parikh; Shambhavi Mishra; Sébastien Paquet; Srinivas Sunkara; Valérie Bécaert; Sathwik Tejaswi Madhusudhan; et al. (CoRR, 2025)