Papers by Ishani Mondal
Presentations by the Humans and For the Humans: Harnessing LLMs for Generating Persona-Aware Slides from Documents (2024.eacl-long)
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Ishani Mondal, Shwetha S, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, Jordan Boyd-Graber
| Challenge: | Existing efforts to automate document-to-slide generation have failed to adapt to the persona of target audience or duration of presentation. |
| Approach: | They propose a concept of end-user specification-aware document to slides conversion that incorporates end- user specifications into the conversion process. |
| Outcome: | The proposed model can create persona-aware presentations tailored to the persona of target audience and cognitive abilities of target audiences. |
Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic? (2022.coling-1)
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| Challenge: | Language Technology (LT) has been used in the COVID-19 pandemic, but only in a handful of languages. |
| Approach: | They propose to use conversational agents for information dissemination and basic diagnosis in 15 Asian and African languages with varying resource-availability to test their knowledge of LT. |
| Outcome: | The proposed research confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help prioritize research and investment strategies in LT for healthcare. |
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence (2024.findings-emnlp)
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| Challenge: | Current short-form QA evaluations lack diverse styles of evaluation data and rely on expensive and slow LLMs. |
| Approach: | They propose a rubric for machine QA that is more stable than an exact match and neural methods. |
| Outcome: | The proposed evaluations improve on the existing short-form QA evaluations using the Trivia community. |
Language Patterns and Behaviour of the Peer Supporters in Multilingual Healthcare Conversational Forums (2022.lrec-1)
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Ishani Mondal, Kalika Bali, Mohit Jain, Monojit Choudhury, Jacki O’Neill, Millicent Ochieng, Kagnoya Awori, Keshet Ronen
| Challenge: | a quantitative linguistic analysis of multilingual peer supporters in health-focused WhatsApp forums in Kenya is needed. |
| Approach: | They conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp forums in Kenya. |
| Outcome: | The proposed language analyzer can be used to analyze language usage patterns in two health-focused WhatsApp forums in Kenya. |
From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication (2026.acl-srw)
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| Challenge: | Modern generative AI produces fluent text,polished slides, and clean diagrams, yet fails when an artifact must serve a specificpurpose for a particular reader, used by aspecificpresenter. |
| Approach: | They propose to use persona-aware slide generation to help audiences reframe content rather than blanketsimplification to SMART-Editor to model user preferences. |
| Outcome: | The proposed system is based on persona-aware slide generation, sciDoc2Diagrammer-MAF, and SMART-Editor. |
End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)
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| Challenge: | a new schema for NLP knowledge about tasks, datasets and metrics is proposed. |
| Approach: | They propose a new schema that represents knowledge about tasks, datasets and metrics in the NLP domain. |
| Outcome: | The proposed framework can be automatically built into scientific leaderboards . the proposed system achieves reasonable results for all relation types on this small-scale graph . |
SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement (2024.findings-emnlp)
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| Challenge: | Current text-to-image models struggle with generating accurate diagrams from long-context inputs. |
| Approach: | They propose a task that extracts relevant information from scientific papers and generates diagrams based on user intentions using intermediate code generation. |
| Outcome: | The proposed task outperforms existing models on factual correctness and visual appeal and outperfies existing ones on automatic and human judgement. |
SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity (2026.findings-eacl)
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| Challenge: | SMART-EDITOR is a framework for compositional layout and content editing for structured visual domains. |
| Approach: | They introduce a framework for compositional editing for structured images like posters or websites . SMART-EDITOR maintains global coherence through two complementary strategies . |
| Outcome: | The proposed framework maintains global coherence through two complementary strategies. |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness (2025.naacl-long)
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| Challenge: | As models evolve, datasets can become outdated. |
| Approach: | They propose a human-grounded evaluation metric that assesses adversarialness by capturing models’ and humans’ varying abilities, while also identifying poor examples. |
| Outcome: | The proposed evaluation metric measures the accuracy of an adversarial question answering dataset and determines whether models are performing well on the dataset. |
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification (2021.naacl-main)
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| Challenge: | Recent efforts to generate adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the ever-increasing biomedical literature poses unique challenges. |
| Approach: | They propose a black-box attack algorithm for biomedical text classification that uses rule-based synonyms and BERT-MLMs to generate adversarial examples. |
| Outcome: | The proposed algorithm performs stronger with better language fluency and semantic coherence than previous work. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages (2023.findings-eacl)
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| Challenge: | Currently, there is a lack of data and technology for resource-poor languages in developing countries like India. |
| Approach: | They propose to use two different datasets to analyze query intents and entities in healthcare. |
| Outcome: | The proposed model is useful to identify query intents and entities in real-world scenarios. |
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators (2026.findings-acl)
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| Challenge: | State-of-the-art NLP models are expensive and inefficient for event annotation. |
| Approach: | They propose to integrate LLMs into a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only. |
| Outcome: | The proposed workflow integrates LLMs to alleviate human labor in a holistic pipeline. |
Group Preference Alignment: Customizing LLM Responses from In-Situ Conversations Only When Needed (2025.emnlp-industry)
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Ishani Mondal, Jack W. Stokes, Sujay Kumar Jauhar, Longqi Yang, Mengting Wan, Xiaofeng Xu, Xia Song, Jordan Lee Boyd-Graber, Jennifer Neville
| Challenge: | Existing methods for group-aware adaptation capture divergent preferences from real-world conversation logs into interpretable rubrics. |
| Approach: | They propose a group-aware personalization framework that captures context-specific preferences and steers LLMs accordingly. |
| Outcome: | The proposed framework improves group alignment without compromising perfomance on benchmarks. |
“#DisabledOnIndianTwitter” : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter (2022.findings-aacl)
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| Challenge: | a majority of disabled Indians exist at the margins of society with little to no access to social media . as access to ICTs and high-speed internet grows, Indian Twitter's user base is expanding to include disability influencers, activists, and everyday disabled users. |
| Approach: | They propose a hierarchical annotation taxonomy to classify tweets into various themes including discrimination, advocacy, and self-identification. |
| Outcome: | The proposed taxonomy classifies 2,384 tweets into various themes including discrimination, advocacy, and self-identification. |