Papers by Qingyang Zhao
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)
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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu
| Challenge: | Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data. |
| Approach: | They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach. |
| Outcome: | The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts. |
CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent Layers (2024.acl-long)
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| Challenge: | Existing methods to improve inference efficiency target to reduce per-layer latency, but ignore cumulative latency due to number of layers. |
| Approach: | They propose to identify quasi-independent layers that can be concurrently computed to significantly decrease inference latency. |
| Outcome: | Empirical results show that the proposed method reduces latency by 48.3% on LLaMA-33B while maintaining close level of performance. |
PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics (2026.findings-acl)
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Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, Zongyuan Ge
| Challenge: | Mental disorders affect nearly one in seven people worldwide, yet the vast majority do not receive adequate care. |
| Approach: | They propose a framework to evaluate LLMs' ethical knowledge and behavioral responses through multiple-choice and open-ended tasks with fine-grained ethicality annotations. |
| Outcome: | Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness. |
DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration (2026.findings-acl)
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| Challenge: | Existing training-free acceleration approaches for text-to-speech models are constrained by training costs. |
| Approach: | They propose a training-free acceleration framework that compresses computations in DiT-based TTS models . they propose Temporal Skipping and Branch Skipping to eliminate redundant computations . |
| Outcome: | Experimental results show that the proposed framework reduces FLOPs and improves RTF by 37.1%. |
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. |