Papers by Jieyi Wang
Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model (2026.findings-acl)
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| Challenge: | Existing methods for multi-interest analysis of users rely on heuristic assumptions . however, the granularity of raw generation of LLMs is agnostic, leading to overly fine or coarse interest grouping. |
| Approach: | They propose an LLM-driven adaptive and representative multi-interest modeling framework that exploits the agnostic granularity of LLMs for multi-interest analysis. |
| Outcome: | The proposed model outperforms baselines on real-world datasets. |
SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation (2026.acl-long)
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| Challenge: | Recent advances in speech language models have enabled more natural speech-based interactions, but the scarcity of medical speech data and the inefficiency of fine-tuning on speech data hinder adoption of SpeechLMs in medical consultation. |
| Approach: | They propose a SpeechLM natively capable of conducting speech-based multi-turn interactions with patients. |
| Outcome: | The proposed model outperforms baselines in both effectiveness and robustness in most evaluation settings. |
MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)
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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang
| Challenge: | Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture. |
| Approach: | They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition. |
| Outcome: | The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module. |
Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding (2026.findings-acl)
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| Challenge: | Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors. |
| Approach: | They propose a large-scale dataset for **Perception-Aware Question Answering** that uses a hierarchical decoupling strategy to separate speech from environmental sounds and distinguishes among multiple speakers. |
| Outcome: | The proposed model improves on MMAU-mini, MMAR, and PAQA while maintaining comparable performance on multiple benchmarks. |