Papers by Zhiyu Shen
CoE: A Clue of Emotion Framework for Emotion Recognition in Conversations (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are limited in interpreting complex conversational streams. |
| Approach: | They propose a Clue of Emotion framework which integrates key conversational clues to enhance the ERC task. |
| Outcome: | The proposed framework outperforms EmoryNLP, MELD, and IEMOCAP in the role-playing, speaker identification, and emotion reasoning tasks. |
PATIENT-๐: Using Large Language Models to Simulate Patients for Training Mental Health Professionals (2024.emnlp-main)
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Ruiyi Wang, Stephanie Milani, Jamie Chiu, Jiayin Zhi, Shaun Eack, Travis Labrum, Samuel Murphy, Nev Jones, Kate Hardy, Hong Shen, Fei Fang, Zhiyu Chen
| Challenge: | Mental illness remains one of the most critical public health issues. |
| Approach: | They propose a patient simulation framework for cognitive behavior therapy training that uses large language models to act as a simulated therapy patient. |
| Outcome: | The proposed framework improves the skill acquisition and confidence of mental health trainees beyond textbooks, videos, and role-play with non-patients. |
HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions (2026.acl-long)
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| Challenge: | Multi-Hop Question Answering (MHQA) is a critical benchmark for evaluating the modelโs ability to integrate information from diverse sources. |
| Approach: | They propose a framework that synthesizes authentic multi-hop questions without manual annotation without the need for manual guidance. |
| Outcome: | The proposed framework synthesizes bridge and comparison questions without human intervention and achieves comparable or superior quality to human-annotated datasets at a lower cost. |
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection (N19-1)
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| Challenge: | Using a pre-defined vocabulary is a common approach to selecting text inputs . however, using a large vocabulary is not economical, as it limits the model's applicability on computation-or memoryconstrained scenarios. |
| Approach: | They propose a more sophisticated variational vocabulary dropout to perform vocabulary selection . they propose two new metrics to measure area under accuracy-vocab curve and Vocab Size under X% accuracy drop . |
| Outcome: | The proposed framework outperforms the baselines on the vocabulary selection problem on multiple NLP classification tasks. |
CARE: A Disagreement Detection Framework with Concept Alignment and Reasoning Enhancement (2025.emnlp-main)
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| Challenge: | Existing approaches to disagreement detection are limited by conceptual gap and reasoning gap. |
| Approach: | They propose a conceptual alignment and reasoning enhancement framework to address the conceptual gap and the reasoning gap in disagreement detection. |
| Outcome: | The proposed framework shows superior performance in zero-shot and supervised learning settings, both within and across domains. |
MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards (2026.acl-long)
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| Challenge: | Memory-augmented frameworks fail to capture temporal evolution of historical states, limiting consistency in long-term dialogues. |
| Approach: | They propose a framework that trains models to orchestrate multi-dimensional memory construction with attributed dense rewards. |
| Outcome: | The proposed framework outperforms state-of-the-art closed-source models and generalizes well to OOD benchmarks. |