Papers by Yanan Zheng

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)

Challenge:	emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
Approach:	They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions.
Outcome:	The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions.

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Qingyue Wang, Liang Ding, Yanan Cao, Yibing Zhan, Zheng Lin, Shi Wang, Dacheng Tao, Li Guo

ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants (2026.acl-long)

Challenge:	Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system.
Approach:	They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism.
Outcome:	The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism.

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Pei Wang, Yanan Wu, Xiaoshuai Song, Weixun Wang, Gengru Chen, Zhongwen Li, Kezhong Yan, Qi Liu, Ken Deng, Shuaibing Zhao, Shaopan Xiong, Xuepeng Liu, Xuefeng Chen, Wanxi Deng, Wenbo Su, Bo Zheng

Challenge:	Existing studies on large language model-based agents focus on evaluation benchmarks without training support.
Approach:	They propose a large-scale Chinese shopping simulation environment that uses large language models to train agents.
Outcome:	The proposed model performs poorly in a large-scale and challenging shopping environment in China.

Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models (2026.findings-acl)

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Teng Wang, Jiang Zhangyi, Zhenqi He, Hailei Gong, Shenyang Tong, Wenhan Yang, Zeyu Li, Yanan Zheng, Zifan He, Zewen Ye, Shengjie Ma, Jianping Zhang

EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)

Challenge:	Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps.
Approach:	They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level.
Outcome:	Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models.

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Yixuan Nan, Xixun Lin, Yanmin Shang, Ge Zhang, Zheng Fang, Fang Fang, Yanan Cao

Explainability & Interpretability in NLP

Challenge:	Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object.
Approach:	They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object.
Outcome:	The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets.

Prompt-Based Metric Learning for Few-Shot NER (2023.findings-acl)

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Yanru Chen, Yanan Zheng, Zhilin Yang

Low-Resource NLP

Challenge:	Existing metric learning methods do not fully incorporate label semantics into modeling.
Approach:	They propose a method to largely improve metric learning for few-shot named entity recognition (NER) a pre-defined category is a key natural language understanding task .
Outcome:	The proposed method outperforms the previous state-of-the-art (SOTA) method with 16 of 18 settings outperformed previous methods by 9.12% and 34.51% .

LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models (2025.acl-long)

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Yan Wang, Ling Ding, Tien N Nguyen, Shaohua Wang, Yanan Zheng

Towards One-to-Many Visual Question Answering (2024.findings-emnlp)

Challenge:	Large Language Models often require significant computational resources, often constraining input word or code token lengths.
Approach:	They propose to use the encoder-decoder attention scores to represent the importance of a code token across multiple contexts to reduce training and prediction time.
Outcome:	The proposed approach outperforms the SOTAs DietCode and SlimCode in code search and summarization tasks.

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Huishan Ji, Qingyi Si, Zheng Lin, Yanan Cao, Weiping Wang

Multimodality

Challenge:	Existing Visual Question Answering systems are constrained to support domain-specific questions . a model trained on a single specific domain may not be competent for real-world application.
Approach:	They propose a task to enable a single model to answer as many different domains of questions as possible . they break the task down into the integration of three key abilities .
Outcome:	The proposed model can answer as many domains of questions as possible, the authors argue . the proposed model generalizes well to three extra zero-shot datasets, and the results are published.

Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)

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Ruipeng Jia, Xingxing Zhang, Yanan Cao, Zheng Lin, Shi Wang, Furu Wei

DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

Challenge:	Existing methods to translate sentences to other languages using heuristics are challenging.
Approach:	They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them.
Outcome:	The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics.

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Guanqun Bi, Lei Shen, Yanan Cao, Meng Chen, Yuqiang Xie, Zheng Lin, Xiaodong He

Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)

Challenge:	Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach:	They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome:	The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.

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Yubing Ren, Yanan Cao, Ping Guo, Fang Fang, Wei Ma, Zheng Lin

Information Retrieval

Challenge:	Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks.
Approach:	They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions .
Outcome:	The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation .

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)

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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang

Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)

Challenge:	Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress.
Approach:	They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability.
Outcome:	The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability.

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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang

Dialogue Systems & Conversational Agents

Challenge:	Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses.
Approach:	They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses.
Outcome:	The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses.

Deep Differential Amplifier for Extractive Summarization (2021.acl-long)

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Ruipeng Jia, Yanan Cao, Fang Fang, Yuchen Zhou, Zheng Fang, Yanbing Liu, Shi Wang

Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning (2022.findings-emnlp)

Challenge:	Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance.
Approach:	They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture.
Outcome:	The proposed model performs competitively against state-of-the-art methods on two benchmark datasets.

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Qingyi Si, Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Semantic Text Processing

Challenge:	Recent studies have shown that biased samples can be brittle for VQA models . however, the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data.
Approach:	They propose a contrastive learning approach that exploits biased samples for unbiased information that contributes to reasoning.
Outcome:	The proposed method achieves competitive performance on the OOD dataset while maintaining robustness on the ID dataset.

PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization (2025.acl-long)

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Yidan Wang, Yanan Cao, Yubing Ren, Fang Fang, Zheng Lin, Binxing Fang

Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training (2022.naacl-main)

Challenge:	Existing methods to evaluate privacy leakage in LLMs use memorized prefixes or simple instructions to extract data, which well-aligned models can easily block.
Approach:	They propose a framework targeting Personally Identifiable Information (PII) that uses in-context learning to build a privacy context and iteratively updates it with three gradient-based strategies to elicit target PII.
Outcome:	The proposed framework outperforms baseline methods and achieves state-of-the-art (SoTA) results on four white-box and two black-box LLMs.

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Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)

Challenge:	Recent studies show pre-trained language models contain matching subnetworks that have similar transfer learning performance as the original PLM.
Approach:	They propose to prune matching subnetworks using magnitude-based pruning . they propose to optimize the subnetwork structure towards the pre-training objectives .
Outcome:	The proposed method is more efficient in searching subnetworks and advantageous when fine-tuning within a range of data scarcity.

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Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu

Information Extraction & Text Mining

Challenge:	Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events.
Approach:	They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists .
Outcome:	The proposed model improves on two widely used DEE datasets on the Internet.

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization (2022.naacl-main)

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Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Weiran Xu, Huixing Jiang, Wei Wu, Yanan Wu

Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold (2022.naacl-main)

Challenge:	Existing methods for domain adaptation of abstractive dialogue summarization lack generalization ability on new domains.
Approach:	They propose a domain-oriented prefix-tuning model that uses a prefix module to alleviate domain entanglement and discrete prompts to guide the model to focus on key contents of dialogues.
Outcome:	The proposed model can be generalized to two multi-domain dialogue summarization datasets.

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Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu

Semantic Text Processing

Challenge:	Existing methods for OOD detection are based on labeled in-domain data . detecting out-of-domain (OOD) or unknown intents is challenging .
Approach:	They propose a novel reassigned contrastive learning method to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents.
Outcome:	The proposed method is effective for both aspects of overconfidence issues.

TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network (2021.emnlp-main)

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Zheng Fang, Yanan Cao, Tai Li, Ruipeng Jia, Fang Fang, Yanmin Shang, Yuhai Lu

Information Extraction & Text Mining

Challenge:	Existing methods to label data and identify entities require large amounts of manually annotated texts for training supervised models.
Approach:	They propose a dictionary extension method which extracts new entities through the type expanded model.
Outcome:	The proposed method outperforms state-of-the-art supervised systems on different types of datasets and surpasses supervised models.

Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking (2022.coling-1)

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Qingyue Wang, Yanan Cao, Piji Li, Yanhe Fu, Zheng Lin, Li Guo

Low-Resource NLP

Challenge:	Existing zero-shot learning methods ignore slot dependencies in a multidomain dialogue . experimental results show the effectiveness of our proposed method over existing state-of-art generation methods .
Approach:	They propose to use slot prompts combination, slot values demonstration and slot constraint object to model slot-slot dependency, slot-value dependency and slot-context dependency respectively.
Outcome:	The proposed method outperforms state-of-the-art methods under zero-shot/few-shot settings.

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)

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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng

Reasoning

Challenge:	ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets.
Approach:	They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models.
Outcome:	The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies.

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Programming Languages in NLP

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models (2025.acl-long)

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Junjie Wu, Gefei Gu, Yanan Zheng, Dit-Yan Yeung, Arman Cohan

Non-Autoregressive Chinese ASR Error Correction with Phonological Training (2022.naacl-main)

Challenge:	Long-context language models have impressive capabilities in long-contrast understanding tasks, but long-text referencing remains underexplored.
Approach:	They propose a benchmark to assess long-context referencing capability of LCLMs . they use three subsets to test the model's ability to identify key indexes based on contextual relationships .
Outcome:	The proposed benchmark assesses the long-context referencing capability of LCLMs.

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Zheng Fang, Ruiqing Zhang, Zhongjun He, Hua Wu, Yanan Cao

Speech & Audio in NLP

Challenge:	Existing methods to correct ASR errors focus on fixed-length corrections, but rarely consider variable-length ones.
Approach:	They propose a non-autoregressive method to correct Chinese ASR errors . they use phonological tokens to extend the source sentence for variable-length correction .
Outcome:	The proposed method improves word error rate and speeds up inference by 6.2 times compared with the autoregressive model.

Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation (2023.emnlp-main)

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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang

Semantic Text Processing

Challenge:	Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration.
Approach:	They propose a framework that allows the model to "cheat" the objective by duplicating knowledge segments in a superficial pattern matching based on overlap.
Outcome:	The proposed framework can be applied to a WoW dataset and shows that it works across models and decoding strategies.

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022.findings-emnlp)

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Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Question Answering

Challenge:	Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution.
Approach:	They propose a dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Outcome:	The proposed dataset considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.

ProgCo: Program Helps Self-Correction of Large Language Models (2025.acl-short)

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Xiaoshuai Song, Yanan Wu, Weixun Wang, Jiaheng Liu, Wenbo Su, Bo Zheng

Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering (2023.emnlp-main)

Challenge:	Existing LLMs fail to self-correct and generate correct feedback, leading to misleading refinement and failure of self-refinement.
Approach:	They propose a program-driven self-correction approach that uses program-based verification to self-refine initial responses without external feedback.
Outcome:	The proposed model achieves self-correction and can be further enhanced when combined with real program tools.

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Qingyi Si, Yuanxin Liu, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang

Robustness in NLP

Challenge:	Existing studies on VQA models have found that they suffer from dataset biases and inefficient memory footprints.
Approach:	They investigate whether a VLP can be compressed and debiased simultaneously by searching sparse and robust subnetworks.
Outcome:	The proposed compression and debiasing pipelines outperform the debiased full VLPs on VQA tasks.

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)

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Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, Zhilin Yang

A Universal Discriminator for Zero-Shot Generalization (2023.acl-long)

Challenge:	Existing methods for text data augmentation are limited to simple tasks and weak baselines.
Approach:	They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data.
Outcome:	The proposed method improves many tasks while not negatively affecting the others.

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Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng, Zhilin Yang