Adobe

This page collects publications from collaborations with Adobe researchers and collaborators. The work spans question answering, personalization, evaluation, multimodal systems, and human-centered interactive AI.

It serves as the local publication hub for papers tagged under the Adobe collaboration on this site.

• Jordan Boyd-Graber
• Ishani Mondal
• Nishant Balepur
• Adobe collaborators across multiple projects

<< back to top

• Ishani Mondal, Meera Bharadwaj, Ayush Roy, Aparna Garimella, and Jordan Boyd-Graber. SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity. European Association for Computational Linguistics, 2026. [Bibtex]
  
  

  @inproceedings{Mondal:Bharadwaj:Roy:Garimella:Boyd-Graber-2026,
  	Title = {SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity},
  	Author = {Ishani Mondal and Meera Bharadwaj and Ayush Roy and Aparna Garimella and Jordan Boyd-Graber},
  	Booktitle = {European Association for Computational Linguistics},
  	Year = {2026},
  }
  

• Feng Gu, Zongxia Li, Carlos R. Colon, Benjamin Evans, Ishani Mondal, and Jordan Boyd-Graber. Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators. Findings of the Association for Computational Linguistics, 2026. [Bibtex]
  
  

  @article{Gu:Li:Colon:Evans:Mondal:Boyd-Graber-2026,
  	Title = {Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators},
  	Author = {Feng Gu and Zongxia Li and Carlos R. Colon and Benjamin Evans and Ishani Mondal and Jordan Lee Boyd-Graber},
  	Journal = {Findings of the Association for Computational Linguistics},
  	Year = {2026},
  }
  

• Nishant Balepur, Alexa Siu, Nedim Lipka, Franck Dernoncourt, Tong Sun, Jordan Boyd-Graber, and Puneet Mathur. MoDS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections. Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, 2025. [Bibtex]
  
  

  @inproceedings{Balepur:Siu:Lipka:Dernoncourt:Sun:Boyd-Graber:Mathur-2025,
  	Title = {MoDS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections},
  	Author = {Nishant Balepur and Alexa Siu and Nedim Lipka and Franck Dernoncourt and Tong Sun and Jordan Lee Boyd-Graber and Puneet Mathur},
  	Booktitle = {Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics},
  	Year = {2025},
  	Location = {Albuquerque},
  	Url = {http://cs.umd.edu/~jbg//docs/2025_naacl_mods.pdf},
  }
  

  Accessible Abstract: When you ask ChatGPT for advice on questions with multiple perspectives (e.g. "Is pineapple good on pizza?"), you likely want a response that fairly represents all viewpoints. We formulate this task, collect a dataset to test it, and develop MoDS—a system where multiple ChatGPT's debate like a panel discussion—to generate balanced answers for questions based on multiple sources.

• Nishant Balepur, Feng Gu, Abhilasha Ravichander, Shi Feng, Jordan Boyd-Graber, and Rachel Rudinger. Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer? Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, 2025. [Bibtex]
  
  

  @inproceedings{Balepur:Gu:Ravichander:Feng:Boyd-Graber:Rudinger-2025,
  	Title = {Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?},
  	Author = {Nishant Balepur and Feng Gu and Abhilasha Ravichander and Shi Feng and Jordan Boyd-Graber and Rachel Rudinger},
  	Booktitle = {Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics},
  	Year = {2025},
  	Location = {Albuquerque},
  	Url = {http://cs.umd.edu/~jbg//docs/2025_naacl_reverseqa.pdf},
  }
  

  
  

  Accessible Abstract: Language models like ChatGPT are pretty good at answering questions (e.g. "What is 12 * 12?"), but we show they can surprisingly struggle when asked to do the reverse task: generating questions for answers (e.g. "Give me a question with the answer 144"). We study when these errors happen, what might be causing them, and how they can be addressed.

• Nishant Balepur, Vishakh Padmakumar, Fumeng Yang, Shi Feng, Rachel Rudinger, and Jordan Boyd-Graber. Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas. Association for Computational Linguistics, 2025. [Code/Data] [Bibtex]
  
  

  @inproceedings{Balepur:Padmakumar:Yang:Feng:Rudinger:Boyd-Graber-2025,
  	Title = {Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas},
  	Author = {Nishant Balepur and Vishakh Padmakumar and Fumeng Yang and Shi Feng and Rachel Rudinger and Jordan Lee Boyd-Graber},
  	Booktitle = {Association for Computational Linguistics},
  	Location = {Vienna, Austria},
  	Year = {2025},
  	Url = {http://cs.umd.edu/~jbg//docs/2025_acl_boat.pdf},
  }
  

  
  

  Accessible Abstract: Language models are optimized to learn which responses you prefer, but they don't learn why you preferred a particular response. This limits their ability to tailor to personalized requests (e.g., "What should I eat for dinner? I'm vegetarian"), so we introduce a simple fix: have models infer personas that explain why users could prefer responses. We show training on these inferred personas leads to responses that are significantly more personalized for user needs.

• Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, and Jordan Boyd-Graber. A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. Empirical Methods in Natural Language Processing, 2024. [Code and Data] [Research Talk] [Bibtex]
  
  

  @inproceedings{Balepur:Shu:Hoyle:Robey:Feng:Goldfarb-Tarrant:Boyd-Graber-2024,
  	Title = {A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick},
  	Author = {Nishant Balepur and Matthew Shu and Alexander Hoyle and Alison Robey and Shi Feng and Seraphina Goldfarb-Tarrant and Jordan Boyd-Graber},
  	Booktitle = {Empirical Methods in Natural Language Processing},
  	Year = {2024},
  	Location = {Miami},
  	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_mnemonic.pdf},
  }
  

  
  

  Accessible Abstract: Learning vocabulary (e.g., benevolent) can be tedious, but using mnemonics (e.g., benevolent sounds like "benefits," and a kind boss gives benefits) makes it more engaging and effective. This paper introduces SMART, a large language model trained to produce mnemonics based on feedback from flashcard learners. Students struggle to predict which mnemonics will help them most. Still, by training SMART on both student preferences and learning outcomes, we can generate mnemonics as effectively as GPT-4, but at a much lower cost.

• Ishani Mondal, Shwetha S, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Boyd-Graber. Presentations by the People, for the People: Harnessing LLMs for Generating Persona-Aware Slides from Documents. European Association for Computational Linguistics, 2024. [Bibtex]
  
  

  @inproceedings{Mondal:S:Natarajan:Garimella:Bandyopadhyay:Boyd-Graber-2024,
  	Title = {Presentations by the People, for the People: Harnessing LLMs for Generating Persona-Aware Slides from Documents},
  	Author = {Ishani Mondal and Shwetha S and Anandhavelu Natarajan and Aparna Garimella and Sambaran Bandyopadhyay and Jordan Boyd-Graber},
  	Booktitle = {European Association for Computational Linguistics},
  	Year = {2024},
  	Url = {http://cs.umd.edu/~jbg//docs/2024_eacl_slides.pdf},
  }
  

• Ishani Mondal, Zongxia Li, Yufang Hou, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Boyd-Graber. SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement. Findings of the Empirical Methods in Natural Language Processing, 2024. [Bibtex]
  
  

  @article{Mondal:Li:Hou:Natarajan:Garimella:Bandyopadhyay:Boyd-Graber-2024,
  	Title = {SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement},
  	Author = {Ishani Mondal and Zongxia Li and Yufang Hou and Anandhavelu Natarajan and Aparna Garimella and Sambaran Bandyopadhyay and Jordan Boyd-Graber},
  	Year = {2024},
  	Journal = {Findings of the Empirical Methods in Natural Language Processing},
  	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_diagramgen.pdf},
  }
  

• Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, and Jordan Boyd-Graber. Adapting Coreference Resolution Models through Active Learning. Association for Computational Linguistics, 2022. [Code] [Bibtex]
  
  

  @inproceedings{Yuan:Xia:May:Durme:Boyd-Graber-2022,
  	Author = {Michelle Yuan and Patrick Xia and Chandler May and Benjamin Van Durme and Jordan Boyd-Graber},
  	Title = {Adapting Coreference Resolution Models through Active Learning},
  	Booktitle = {Association for Computational Linguistics},
  	Year = {2022},
  	Location = {Dublin},
  	Url = {http://cs.umd.edu/~jbg//docs/2022_acl_alcoref.pdf},
  }
  

• Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, and Jordan Boyd-Graber. Automatic Song Translation for Tonal Languages. Findings of the Association for Computational Linguistics, 2022. [Translation Examples (with sound)] [Code] [Bibtex]
  
  

  @article{Guo:Zhang:Zhang:He:Zhang:Xie:Boyd-Graber-2022,
  	Author = {Fenfei Guo and Chen Zhang and Zhirui Zhang and Qixin He and Kejun Zhang and Jun Xie and Jordan Boyd-Graber},
  	Title = {Automatic Song Translation for Tonal Languages},
  	Journal = {Findings of the Association for Computational Linguistics},
  	Year = {2022},
  	Location = {Dublin},
  	Url = {http://cs.umd.edu/~jbg//docs/2022_acl_ast.pdf},
  }