And thank you for stopping by! I am Lj Flores, an MSc thesis student at the Montreal Institute for Learning Algorithms and McGill University, with Professor Jackie Cheung.

I also collaborated with Prof. Arman Cohan on projects in summarization and simplification. Previously, I completed a joint bachelors and masters in statistics at Yale University, where I got to work with Prof. Dragomir Radev and Linyong Nan (LILY Lab) on NLP tasks over tabular data.

I am deeply interested in natural language processing, particularly with regards to generation tasks (e.g. summarization, simplification), and machine learning broadly applied for social good (especially in the Philippines)!

Selected Publications

• Lorenzo Flores and Arman Cohan. On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization, EACL 2024 [Paper, Video, Code]
• Lorenzo Flores, Heyuan Huang, Kejian Shi, Sophie Chheang, and Arman Cohan. 2023. Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding, EMNLP 2023 Findings [Paper, Video, Code, Demo]
• Lorenzo Flores, Dragomir Radev. 2022. Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino, EMNLP 2022 SustaiNLP Workshop [Paper, Video, Code]
• Linyong Nan, Lorenzo Flores, Yilun Zhao, Yixin Liu, Luke Benson, Weijin Zou, and Dragomir Radev. 2022. R2D2: Robust Data-to-Text with Replacement Detection, EMNLP 2022 [Paper]
• Lorenzo Flores, Yiding Hao. 2022. Adversarial Benchmark for Fake News Classification. AAAI 2022 AdvML Workshop [Paper, Code]
• Chiara Ledesma, Oshean Lee Garonita, Lorenzo Flores, Isabelle Tingzon, and Danielle Dalisay. 2020. Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information, ML4D Workshop, NeurIPS 2020, Best Workshop Paper Award [Paper]

Projects I’m Working On (!)

• LossLibrary: a repository that consolidates loss functions from NLP literature and helps users integrate it into training [Here!]