Papers by Dominik Glandorf
PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf (2026.acl-demo)
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Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, Xuanqiang Angelo Huang, Vilém Zouhar, Dominik Glandorf, Isabel Dahlgren, Rishit Dagli, Yuen Chen, Felix Leeb, Van Q. Truong, Punya Syon Pandey, Yves Bicker, Suvajit Majumder, Wenyuan Jiang, Zeju Qiu, Sankalan Pal Chowdhury, Mrinmaya Sachan, Bernhard Schölkopf, Mona T. Diab, Zhijing Jin
| Challenge: | Emerging AI-powered writing assistants focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. |
| Approach: | They propose a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. |
| Outcome: | The proposed system outperforms a baseline with the skill library and provides actionable suggestions while leaving the actual writing to human authors. |
Grammar Control in Dialogue Response Generation for Language Learning Chatbots (2025.naacl-long)
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| Challenge: | Existing language learning chatbots and research on second language acquisition benefit from these affordances. |
| Approach: | They ground a dialogue response generation model in a pedagogical repository of grammar skills and evaluate prompting, fine-tuning, and decoding strategies for grammar-controlled dialogue response generators. |
| Outcome: | The proposed model outperforms GPT-3.5 when tolerating minor response quality losses and predicts grammar-controlled responses to support grammar acquisition adapted to learner proficiency. |
SCRIBE: Structured Chain Reasoning for Interactive Behaviour Explanations using Tool Calling (2025.emnlp-main)
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| Challenge: | Language models can be used to provide personalized feedback in educational settings, but they face privacy concerns, limited computational resources, and the need for pedagogically valid responses. |
| Approach: | They propose a framework for multi-hop, tool-augmented reasoning to generate valid responses to student questions about feedback reports. |
| Outcome: | The proposed framework can generate valid responses to student questions about feedback reports using domain-specific tools and self-reflective inference pipelines. |