Papers by Qi Gou
DDxTutor: Clinical Reasoning Tutoring System with Differential Diagnosis-Based Structured Reasoning (2025.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled various medical educational applications, but they often provide direct answers that could reduce students’ cognitive engagement and lead to fragmented learning. |
| Approach: | They propose a framework that follows differential diagnosis principles to decompose clinical reasoning into teachable components. |
| Outcome: | The proposed framework decomposes clinical reasoning into teachable components and generates structured teaching references and conducts diagnostic tutoring dialogues. |
Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)
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| Challenge: | Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context. |
| Approach: | They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer. |
| Outcome: | The proposed framework outperforms baselines on two popular QG datasets. |
EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video (2026.findings-acl)
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Yuanmin Tang, Jue Zhang, Xiaoting Qin, Jing Yu, Meikang Qiu, Gaopeng Gou, Gang Xiong, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Wu
| Challenge: | Existing egocentric video datasets do not support the personalization and long-context reasoning required for episodic memory retrieval. |
| Approach: | They propose a benchmark framework that uses MLLMs and reflective Chain-of-Thought to ground user queries in personal memory explicitly. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on three benchmarks . it can be used to generate detailed target video descriptions in long-context contexts based on user-specific object annotations enriched with user-specified object annotation data . |
Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)
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| Challenge: | Existing question generation systems focus on the internal knowledge within the textual passage or the semantic word space for diverse content planning. Existing solutions focus on relying on the knowledge of the text and the semantic words, but have not considered the potential of external knowledge for expression diversity. |
| Approach: | They propose a framework for Retrieval-Augmented Style Transfer that utilizes the style of diverse templates for question generation. |
| Outcome: | The proposed framework outperforms baselines on diversity while being comparable in terms of consistency scores. |
HealthCards: Exploring Text-to-Image Generation as Visual Aids for Healthcare Knowledge Democratizing and Education (2025.emnlp-main)
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| Challenge: | Text-to-image (T2I) generation has the potential to advance knowledge democratization and education. |
| Approach: | They explore ways to harness T2I models for generating health knowledge flashcards . they curated a high-quality healthcare knowledge flash card dataset . |
| Outcome: | The proposed models can generate health knowledge flashcards with appealing images . the results show that the open-source models can be fine tuned to generate health content . |
Advancing E-commerce Merchants Telemarketing with Synthetic Data-Driven LLMs (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) are proving broadly applicable across diverse industries, including e-commerce. |
| Approach: | They propose a hybrid data synthesis framework that unifies the input schema with profile and strategy designed by top sales and extracts them via a Multi-task paradigm. |
| Outcome: | The proposed model reaches the performance level of the top 25% of human sales in terms of the final marketing results. |