Papers by Qiannan Zhu
Planning-Guided Tutoring with Assessment-Driven Memory for Pedagogical LLM Tutors (2026.acl-long)
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| Challenge: | Existing approaches to simulate tutor behaviors or preferences fail to sustain high-quality pedagogical conversations that provide explicit stepwise scaffolding and adapt to learners’ evolving cognitive states. |
| Approach: | They propose a planning-guided tutoring framework with an assessment-driven memory for multi-turn math dialogue tutoring. |
| Outcome: | Experiments on multi-turn math tutoring benchmarks show that ScaffoldLM significantly improves pedagogical tutoring quality over strong baselines. |
Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval (2025.coling-main)
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| Challenge: | Existing large language models (LLMs) generate redundant output, which generates irrelevant information about the user’s preferences on candidate items from user behavior sequences. |
| Approach: | They propose a framework that enhances reranking for recommendation with large language models through user preference retrieval. |
| Outcome: | The proposed framework improves reranking for recommendation with large language models through user preference retrieval on three real-world public datasets. |
Federated Incremental Named Entity Recognition (2025.coling-main)
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| Challenge: | Existing methods for named entity recognition are based on pre-fixed entity types, resulting in catastrophic forgetting. |
| Approach: | They propose a model which allows for catastrophic forgetting of old entity types . they propose adaptive pseudo labeling and a prototypical relation distillation loss . |
| Outcome: | The proposed model overcomes catastrophic forgetting problem on old entity types with semantic shift. |
Few-Shot Learning for Cold-Start Recommendation (2024.lrec-main)
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| Challenge: | Existing methods for cold-start learning and recommendation are brittle to scenarios with few interactions. |
| Approach: | They propose a Few-shot learning method for Cold-Start recommendation that consists of three hierarchical structures that are local and global . |
| Outcome: | The proposed method improves on two public real-world datasets and is stable compared with the state-of-the-art. |