Papers by Yujie Zhu

HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)

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Shuyu Zhang, Yifan Wei, Xinru Wang, Yanmin Zhu, Yangfan He, Yixuan Weng, Yujie Liu, Bin Li

Low-Resource NLP

Challenge:	Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training.
Approach:	They propose a framework that enhances zero-shot slot inference through robust prompt alignment.
Outcome:	Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST.

LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)

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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang

Responsible & Trustworthy NLP

Challenge:	Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens.
Approach:	They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains.
Outcome:	The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy.

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)

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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen

Semantic Text Processing

Challenge:	Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods.
Approach:	They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation.
Outcome:	The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs.

DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)

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Shuyu Zhang, Yujie Liu, Xinru Wang, Cheng Zhang, Yanmin Zhu, Bin Li

Dialogue Systems & Conversational Agents

Challenge:	Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions .
Approach:	They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework.
Outcome:	The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution.

HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection (2025.findings-emnlp)

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Guang Yang, Yujie Zhu

Language Models

Challenge:	Pre-trained language models (PLMs) are increasingly being applied to code-related tasks.
Approach:	They propose a hypergraph-based adapter to capture high-order data correlations in code tokens . they improve hypergraph neural networks and combine it with adapter tuning to propose adapter .
Outcome:	The proposed adapter can encode high-order data correlations and be inserted into PLMs to enhance performance.

Multimodal Procedural Planning via Dual Text-Image Prompting (2024.findings-emnlp)

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Yujie Lu, Pan Lu, Zhiyu Chen, Wanrong Zhu, Xin Wang, William Yang Wang

Multimodality

Challenge:	Embodied agents have demonstrated performance in following instructions informed by texts and images . however, the potential of models providing useful guidelines for humans to complete tasks remains underexplored .
Approach:	They propose a multimodal procedural planning task that generates paired text-image plans . this task provides more complementary and informative guidance than unimodal plans a . authors propose modality prompting methods that leverage zero-shot reasoning ability .
Outcome:	The proposed method improves the interaction in dual modalities and provides more information than unimodal plans.

Automated Generation of Accurate & Fluent Medical X-ray Reports (2021.emnlp-main)

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Hoang Nguyen, Dong Nie, Taivanbat Badamdorj, Yujie Liu, Yingying Zhu, Jason Truong, Li Cheng

Multimodality

Challenge:	Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts.
Approach:	They propose to automate the generation of medical reports from chest X-ray image inputs . medical reports are the primary medium, which physicians communicate findings from scans - authors say .
Outcome:	The proposed method achieves fluency and clinical accuracy on common metrics.

Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025.acl-long)

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Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang

Semantic Text Processing

Challenge:	Existing methods for integrating internal and external knowledge lack effective control mechanisms for generating hallucinations and dealing with outdated knowledge.
Approach:	They propose a framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness.
Outcome:	The proposed framework decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness.

Imagination-Augmented Natural Language Understanding (2022.naacl-main)

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Yujie Lu, Wanrong Zhu, Xin Wang, Miguel Eckstein, William Yang Wang

Multimodality

Challenge:	Existing methods for Natural Language Understanding focus on textual signals, which hinders models from learning efficiently from limited data samples.
Approach:	They propose an Imagination-Augmented Cross-modal Encoder to solve natural language understanding tasks from a novel learning perspective.
Outcome:	The proposed learning paradigm bridges the gap between human and agent language understanding in both linguistic and perceptual procedures.

DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual Systems (2026.acl-long)

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Shuyu Zhang, Yifan Wei, Jialuo Yuan, Xinru Wang, Yanmin Zhu, Yujie Liu, Bin Li

Dialogue Systems & Conversational Agents

Challenge:	Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts.
Approach:	They propose a dialog policy learning framework that formalizes the exploration challenge through a structured cognitive state space C.
Outcome:	The proposed framework achieves SOTA performance in success rate, efficiency, and generalization.

Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation (2023.emnlp-main)

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Wanrong Zhu, Xinyi Wang, Yujie Lu, Tsu-Jui Fu, Xin Wang, Miguel Eckstein, William Wang

Responsible & Trustworthy NLP

Challenge:	Experimental results show that GPT-k models focus more on inserting modifiers than predicting spontaneous changes in the primary subject matter.
Approach:	They compare the common edits made by humans and GPT-k models to examine their performance in prompting T2I.
Outcome:	The proposed models improve the prompt editing process by 20-30%, the authors show . they show that humans tend to replace words and phrases with modifiers .

Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)

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Wanrong Zhu, An Yan, Yujie Lu, Wenda Xu, Xin Wang, Miguel Eckstein, William Yang Wang

Multimodality

Challenge:	Existing tools for text-to-image synthesis can visualize machine imaginations for a given context.
Approach:	They propose a framework that uses machine-generated images to guide language models in open-ended text generation.
Outcome:	The proposed framework is effective on open-ended text generation tasks while showing minor degeneration.

Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)

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Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai

Question Answering

Challenge:	Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP)
Approach:	They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic.
Outcome:	The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism.

CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)

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Yue Cui, Junhui Zhu, Liner Yang, Xuezhi Fang, Xiaobin Chen, Yujie Wang, Erhong Yang

Semantic Text Processing

Challenge:	Existing tools to analyze linguistic complexity are limited and different because of different research purposes.
Approach:	They propose to integrate Chinese component into CTAP to analyze linguistic complexity . they propose to use 196 linguistic complex indexes to calculate linguistic characteristics .
Outcome:	The proposed indexes are compared with three linguistic complexity tools for Chinese . the proposed index sets include four levels of 196 linguistic complex indexe .