Faisal Ladhak

Hi! I'm a Ph.D. student in the Computer Science Department at Columbia University. I am part of the Natural Language Processing (NLP) group and I am advised by Kathleen McKeown. I am also a Visiting Student Researcher at Stanford University working with Tatsu Hashimoto and Dan Jurafsky. I am interested in building safe, reliable, and truthful generation systems and improving upon existing evaluation methods in natural language generation. My current research focuses on faithfulness and bias in generation systems. Prior to joining Columbia, I worked as a Senior Applied Scientist at Amazon Web Services AI.

Contact: faisal[at]cs[dot]columbia[dot]edu

Publications

• When do Pre-training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization
  Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, Tatsunori Hashimoto
  To Appear in EACL, 2023.
  
• Benchmarking Large Language Models for News Summarization
  Tianyi Zhang*, Faisal Ladhak*, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto
  Preprint, 2023.
  [paper] [bib]
• Tracing and Removing Data Errors in Natural Language Generation Datasets
  Faisal Ladhak, Esin Durmus, Tatsunori Hashimoto
  Preprint, 2022.
  [paper] [bib]
• Holistic Evaluation of Language Models
  Preprint, 2022.
  [paper] [bib]
• Evaluating Human-Language Model Interaction
  Preprint, 2022.
  [paper] [bib]
• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
  Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, Aylin Caliskan
  Preprint, 2022.
  [paper] [bib]
• ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
  Badr AlKhamissi*, Faisal Ladhak*, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab
  Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
  [paper] [bib]
• Improving Faithfulness by Augmenting Negative Summaries from Fake Documents
  Tianshu Wang, Faisal Ladhak, Esin Durmus, He He
  Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
  [paper] [bib]
• Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
  Faisal Ladhak*, Esin Durmus*, He He, Claire Cardie, Kathleen McKeown
  Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
  [paper] [bib]
• Spurious Correlations in Reference-Free Evaluation of Text Generation
  Esin Durmus*, Faisal Ladhak*, Tatsunori Hashimoto
  Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
  [paper] [bib]
• Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization
  Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
  Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022.
  [paper] [bib]
• CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing
  Proceedings of The Workshop on Automatic Summarization for Creative Writing, 2022.
  [team] [paper] [bib]
• On the opportunities and risks of foundation models
  Preprint, 2021.
  [paper] [bib]
• Segmenting Subtitles for Correcting ASR Segmentation Errors
  David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
  Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021.
  [paper] [bib]
• The Gem Benchmark: Natural Language Generation, its Evaluation and Metrics
  Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), 2021.
  [team] [paper] [bib]
• Incorporating Terminology Constraints in Automatic Post-Editing
  David Wan, Chris Kedzie, Faisal Ladhak, Marine Carpuat and Kathleen McKeown.
  WMT, 2020.
  [paper] [bib]
• WikiLingua: A New Benchmark Dataset for Crosslingual Abstractive Summarization
  Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown.
  Findings of EMNLP, 2020.
  [paper] [bib]
• To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
  Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak and Yaser Al-Onaizan.
  Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
  [paper] [bib]
• Exploring Content Selection in Summarization of Novel Chapters
  Faisal Ladhak, Bryan Li, Yaser Al-Onaizan and Kathleen McKeown.
  Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2020.
  [paper] [bib]
• Determining Relative Argument Specificity and Stance for Complex Argumentative Structures
  Esin Durmus, Faisal Ladhak and Claire Cardie.
  Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2019.
  [paper] [bib]
• The Role of Pragmatic and Discourse Context in Determining Argument Impact
  Esin Durmus, Faisal Ladhak and Claire Cardie.
  Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
  [paper] [bib]
• A neural interlingua for multilingual machine translation
  Yichao Lu, Phillip Keung, Faisal Ladhak, Vikas Bhardwaj, Shaonan Zhang and Jason Sun.
  Proceedings of the Conference on Machine Translation (WMT), 2018.
  [paper] [bib]
• LatticeRnn: Recurrent Neural Networks Over Lattices
  Faisal Ladhak, Ankur Gandhe, Markus Dreyer, Lambert Mathias, Ariya Rastrow and Björn Hoffmeister.
  Interspeech, 2016.
  [paper] [bib]
• Using Deep Learning to Enhance Cancer Diagnosis and Classification
  Rasool Fakoor, Faisal Ladhak, Azade Nazi, Manfred Huber.
  In Proceedings of the ICML Workshop on the Role of Machine Learning in Transforming Healthcare, 2013.
  [paper] [bib]