Papers by Yu Xia

Hallucination Diversity-Aware Active Learning for Text Summarization (2024.naacl-long)

Challenge:	Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved.
Approach:	They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion.
Outcome:	The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0.

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Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan Rossi, Anup Rao, Tung Mai, Shuai Li

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness .
Approach:	They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling.
Outcome:	The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs.

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)

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Jianfei Yu, Ziyan Li, Jieming Wang, Rui Xia

Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction.
Approach:	They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems .
Outcome:	The proposed framework outperforms baseline systems on the GMNER task.

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Fanfan Wang, Xiangqing Shen, Jianfei Yu, Rui Xia

Dialogue Systems & Conversational Agents

Challenge:	Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality.
Approach:	They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality.
Outcome:	The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy.

CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages (2025.findings-acl)

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Shangda Wu, Guo Zhancheng, Ruibin Yuan, Junyan Jiang, SeungHeon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun

Multimodality

Challenge:	Music information retrieval (MIR) is a field that aims at developing computational tools for processing, organizing, and accessing music data.
Approach:	They propose a framework that aligns music modalities with multilingual text in a shared representation space.
Outcome:	Experiments show CLaMP 3 performs state-of-the-art on multiple MIR tasks . it surpasses baselines and shows excellent generalization in multimodal and multilingual contexts .

HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding (2025.coling-main)

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Peng Xia, Xingtong Yu, Ming Hu, Lie Ju, Zhiyong Wang, Peibo Duan, Zongyuan Ge

Multimodality

Challenge:	Object categories are typically organized into a multi-granularity taxonomic hierarchy . traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios.
Approach:	They propose a framework that combines vision-language models with a deeper exploitation of the hierarchy.
Outcome:	The proposed framework shows significant improvements on 11 diverse visual recognition benchmarks.

Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)

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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing (2026.acl-long)

Challenge:	Existing methods for integrating information across multiple modalities are suboptimal for multi-page, multimodal documents.
Approach:	They propose an adaptive iterative framework that balances information gain and uncertainty reduction at each step.
Outcome:	The proposed framework captures relevant multimodal content and achieves strong performance on complex QA tasks.

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Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley

Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent (2025.findings-emnlp)

Challenge:	Existing Large Language Models exhibit critical vulnerability to indirect prompt injection attacks, where instructions injected within in the prompt context can override the user's intent.
Approach:	They propose a neural pruning algorithm that prunes neurons associated with instruction-following during KV cache encoding of the prompt context.
Outcome:	The proposed approach significantly reduces the attack success rate while preserving the model's ability to follow user instructions.

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Junda Wu, Yuxin Xiong, Xintong Li, Yu Xia, Ruoyu Wang, Yu Wang, Tong Yu, Sungchul Kim, Ryan A. Rossi, Lina Yao, Jingbo Shang, Julian McAuley

Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding (2023.eacl-main)

Challenge:	Existing fine-tuning and continual learning methods compress visual representations and emphasize task alignment over visual retention.
Approach:	They propose a modality-decoupled gradient descent (MDGD) that regulates gradient updates to preserve effective rank of visual features and explicitly disentangles visual learning from task-specific alignment.
Outcome:	The proposed model reduces visual forgetting and improves visual retention . it disentangles visual learning from task-specific alignment and preserves effective rank .

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Mengnan Du, Subhabrata Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu, Ahmed Hassan Awadallah

A Survey on LLM-based Conversational User Simulation (2026.eacl-long)

Challenge:	Recent studies have focused on compressing pre-trained language models (PLMs) however, few studies have examined the impact of compression on generalizability and robustness of compressed models for out-of-distribution data.
Approach:	They propose to use knowledge distillation and pruning to reduce model generalization and generalization on out-of-distribution data.
Outcome:	The proposed compression techniques overfit on shortcut samples and generalize poorly on hard ones.

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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi

Dialogue Systems & Conversational Agents

Challenge:	Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation.
Approach:	They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments .
Outcome:	The proposed model enables high-fidelity generation of synthetic user conversation.

Composed Variational Natural Language Generation for Few-shot Intents (2020.findings-emnlp)

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Congying Xia, Caiming Xiong, Philip Yu, Richard Socher

Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)

Challenge:	Existing methods to detect spoken language deteriorate drastically in discriminating the few-shot intents.
Approach:	They propose a composed variational natural language generator (CLANG) that generates natural examples for few-shot intents in imbalanced scenarios.
Outcome:	The proposed model achieves state-of-the-art on two real-world intent detection datasets.

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Jianfei Yu, Chenggong Gong, Rui Xia

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)

Challenge:	Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data.
Approach:	They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain.
Outcome:	The proposed approach is superior to state-of-the-art domain adaptation methods.

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Shilin Zhou, Qingrong Xia, Zhenghua Li, Yu Zhang, Yu Hong, Min Zhang

Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models (2026.acl-long)

Challenge:	Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries.
Approach:	They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph .
Outcome:	The proposed model can parse 669/252 sentences per second without and with pre-trained models.

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Hao Fang, Xiaohang Sui, Hongyao Yu, Kuofeng Gao, Jiawei Kong, Sijin Yu, Bin Chen, Shu-Tao Xia

Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation (2025.acl-long)

Challenge:	Retrieval-augmented diffusion models (RDMs) have been developed to enhance performance with reduced parameters.
Approach:	They propose to integrate retrieval-augmented diffusion models with Retrieval-augmented generation (RAG) that enhances performance with reduced parameters.
Outcome:	The proposed framework achieves outstanding attack effects while maintaining benign utility.

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Mingxuan Xia, Haobo Wang, Yixuan Li, Zewei Yu, Jindong Wang, Junbo Zhao, Runze Wu

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	Existing methods for data annotation use an aggressive approach prompting LLMs to determine a single gold label for each unlabeled sample.
Approach:	They propose a teacher-student framework that distills candidate annotations with a Small Language Model (SLM) they propose to use LLMs to generate and distill candidate annotation with slms to ensure unique labels are provided for downstream tasks.
Outcome:	The proposed method outperforms existing methods due to uncertainty in LLMs and is noisetolerant.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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Benlu Wang, Iris Xia, Yifan Zhang, Junda Wang, null Feiyun Ouyang, Shuo Han, Arman Cohan, Hong Yu, Zonghai Yao

Reasoning

Challenge:	Existing benchmarks assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments.
Approach:	They propose a new step-by-step evaluation pipeline that assesses formula selection, entity extraction, and arithmetic computation.
Outcome:	The proposed method improves the accuracy of large language models on medical benchmarks from 16.35% to 53.19%.

Language Scaling for Universal Suggested Replies Model (2021.naacl-industry)

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Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang

Multilinguality

Challenge:	We consider scaling automated suggested replies (SR) to multiple languages for a commercial email application.
Approach:	They propose a multi-lingual multi-task continual learning framework with auxiliary tasks and language adapters to train universal language representation across regions.
Outcome:	The proposed model reduces catastrophic forgetting and improves cross-lingual transfer across languages while reducing training costs.

MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)

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Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing

Challenge:	Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases.
Approach:	They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript.
Outcome:	The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty.

Investigating Value-Reasoning Reliability in Small Large Language Models (2025.emnlp-main)

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Xia Du, Shuhan Sun, Pengyuan Liu, Dong Yu

Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	sLLMs have been widely deployed in practical applications, but little attention has been paid to their value-reasoning abilities, particularly in terms of reasoning reliability.
Approach:	They propose a systematic evaluation framework for assessing the Value-Reasoning Reliability of small Large Language models (sLLMs) . framework includes three core tasks: Repetition Consistency task, Interaction Stability task, and Open-ended Expression Consistencies task.
Outcome:	The proposed framework incorporates self-reported confidence scores to evaluate the model’s value reasoning reliability from two perspectives: the model's self awareness of its values, and its value-based decision-making.

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Yurun Song, Xiangqing Shen, Jianfei Yu, Rui Xia

A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)

Challenge:	Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens.
Approach:	They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability.
Outcome:	The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations.

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Nan Song, Hongjie Cai, Rui Xia, Jianfei Yu, Zhen Wu, Xinyu Dai

Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)

Challenge:	Aspect-based sentiment analysis (ABSA) has received wide attention in NLP for nearly two decades . previous studies focused on sentence-level ABSA, but document-level research has not received enough attention.
Approach:	They propose a Sequence-to-Structure approach to address the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments.
Outcome:	The proposed approach outperforms baselines on six domains on the document-level targeted sentiment analysis task.

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Zehua Xia, Qi Gou, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Text Generation

Challenge:	Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context.
Approach:	They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer.
Outcome:	The proposed framework outperforms baselines on two popular QG datasets.

UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)

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Zinong Yang, Feng Xu, Jianfei Yu, Rui Xia

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks (2020.coling-main)

Challenge:	Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages.
Approach:	They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence.
Outcome:	The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments.

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Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip Yu, Lifang He

ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)

Challenge:	Recent work on data augmentation techniques that interpolate inputs and labels shows strong effectiveness in image classification.
Approach:	They propose to integrate mixup to transformer-based pre-trained architecture for NLP tasks while keeping the whole end-to-end training system.
Outcome:	The proposed framework improves on GLUEbenchmark and transformer-based learning models while keeping the whole end-to-end training system.

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Zixiang Ding, Rui Xia, Jianfei Yu

Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)

Challenge:	a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs .
Approach:	They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme.
Outcome:	The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws .

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Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, Shuai Li

Challenge:	Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT).
Approach:	They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions.
Outcome:	The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks.

Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020.coling-main)

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Hongjie Cai, Yaofeng Tu, Xiangsheng Zhou, Jianfei Yu, Rui Xia

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)

Challenge:	Aspect-based sentiment analysis studies focus on identifying sentiment polarities toward explicit aspects but ignore implicit aspects in text.
Approach:	They propose a hierarchy-sentiment hierarchy prediction problem to capture explicit and implicit aspects of aspect-based sentiment analysis.
Outcome:	The proposed model can consistently achieve the best results on four benchmarks.

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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu

Emotion Cause Extraction on Social Media without Human Annotation (2023.findings-acl)

Challenge:	Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models.
Approach:	They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning.
Outcome:	The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.

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Debin Xiao, Rui Xia, Jianfei Yu

Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)

Challenge:	Existing studies have focused on extracting emotion causes from news articles, but lack of fine-grained annotations has limited the ECE task.
Approach:	They propose a new ECE framework that extracts emotion causes from social media data without relying on human annotations.
Outcome:	The proposed framework achieves high extraction performance and generalizability without relying on human annotations.

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Junjie Li, Jianfei Yu, Rui Xia

Tooling or Not Tooling? The Impact of Tools on Language Agents for Chemistry Problem Solving (2025.findings-naacl)

Challenge:	Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations.
Approach:	They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain.
Outcome:	The proposed framework is more effective than previous domain adaptation methods on three datasets.

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Botao Yu, Frazier N. Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, Huan Sun

Challenge:	Existing evaluations of large language models (LLMs) with tools are limited and qualitative . existing evaluations have been limited and only focus on 14 tasks focusing on compound synthesis.
Approach:	They propose to develop an enhanced chemistry agent over ChemCrow to improve chemistry problem solving by integrating tools into LLMs.
Outcome:	The proposed agent does not consistently outperform its base LLMs without tools on specialized chemistry tasks and general chemistry questions.

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)

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Wenjie Zheng, Jianfei Yu, Rui Xia, Shijin Wang

Reimagining Safety Alignment with An Image (2025.emnlp-main)

Challenge:	Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes.
Approach:	They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence.
Outcome:	The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset.

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Yifan Xia, Guorui Chen, Wenqian Yu, Zhijiang Li, Philip Torr, Jindong Gu

ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)

Challenge:	Existing approaches to large language models face inefficiency, fragility, or architectural constraints, resulting in inefficient performance and heightened over-refusal in cross-modal tasks.
Approach:	They propose an optimization-driven visual prompt framework that enhances security and reduces over-refusal at the same time.
Outcome:	The proposed framework enhances security and reduces over-refusal while maintaining robust safety while reducing unnecessary denials.

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Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao

Green & Sustainable NLP

Challenge:	Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability.
Approach:	They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training.
Outcome:	The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks.

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)

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Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang

Reasoning

Challenge:	Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning.
Approach:	They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations.
Outcome:	The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models.

Promoting Pre-trained LM with Linguistic Features on Automatic Readability Assessment (2022.aacl-short)

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Shudi Hou, Simin Rao, Yu Xia, Sujian Li

RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation (2025.emnlp-main)

Challenge:	Automatic readability assessment (ARA) aims at classifying the readability level of a text automatically.
Approach:	They propose to integrate linguistic features with pre-trained language models to improve the accuracy of ARA.
Outcome:	The proposed algorithm improves on the long passage characteristic of ARA using commonly used linguistic features and abundant datasets.

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Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang

Programming Languages in NLP

Challenge:	Existing tree search methods neglect the underlying reasoning process, resulting in poor search quality.
Approach:	They propose a framework that systematically explores and refines the reasoning process for code generation by using a tree search engine and a reflection mechanism.
Outcome:	The proposed framework outperforms existing methods in the code generation domain.

Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification (2021.emnlp-main)

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Hao Chen, Rui Xia, Jianfei Yu

Low Resource Style Transfer via Domain Adaptive Meta Learning (2022.naacl-main)

Challenge:	Existing approaches to improve generalization ability by augmenting training data with synonymous examples or adding random noises to word embeddings cannot address spurious association problem.
Approach:	They propose an end-to-end reinforcement learning framework which jointly performs counterfactual data generation and dual sentiment classification.
Outcome:	The proposed framework outperforms strong data augmentation baselines on several benchmark sentiment classification datasets.

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Xiangyang Li, Xiang Long, Yu Xia, Sujian Li

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

Challenge:	Existing unsupervised text style transfer methods suffer from performance degradation when fine-tuning the model in new domains.
Approach:	They propose a domain adaptive meta-learning approach with an adversarial style training approach for better content preservation and style transfer.
Outcome:	The proposed approach generalizes well on unseen low-resource domains against ten strong baselines.

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Tao Zhang, Congying Xia, Philip S. Yu, Zhiwei Liu, Shu Zhao

AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)

Challenge:	Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift.
Approach:	They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability.
Outcome:	The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles.

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Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun

Robustness in NLP

Challenge:	Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor.
Approach:	They propose a reward-based generalizable reward model to guide the policy model for effective test-time search.
Outcome:	The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average.

Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization (2026.acl-srw)

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Shiyan Liu, Qifeng Xia, Qiyun Xia, Yisheng Liu, Xinyu Yu, Rui Qu

A State-independent and Time-evolving Network for Early Rumor Detection in Social Media (2020.emnlp-main)

Challenge:	Automatic prompt optimization (APO) is a powerful paradigm for improving LLM performance without manual prompt engineering.
Approach:	They propose a framework that decouples hypothesis generation from prompt rewriting . they propose VISTA framework that recovers accuracy to 87.57% on same defective seed .
Outcome:	The proposed framework outperforms baselines on GSM8K and AIME2025 on a defective seed.

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Rui Xia, Kaizhou Xuan, Jianfei Yu

Challenge:	Existing methods to rumor detection ignored dynamical evolution of an event and failed to capture its unique features in different states.
Approach:	They propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation.
Outcome:	The proposed framework can significantly improve the rumor detection accuracy in comparison with some strong baseline systems.

C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts (2026.findings-acl)

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Chenxi Qing, Junxi Wu, Zheng Liu, Yixiang Qiu, Hongyao Yu, Bin Chen, Hao Wu, Shu-Tao Xia

Aligning as Debiasing: Causality-Aware Alignment via Reinforcement Learning with Interventional Feedback (2024.naacl-long)

Challenge:	Recent efforts to develop algorithms for large language models (LLMs) have limited model diversity and data homogeneity in the Chinese corpora.
Approach:	They propose a Chinese Real-prompt AI-generated text Detection benchmark that can be generalized to unseen LLMs and external Chinese datasets.
Outcome:	The proposed benchmarks address critical gaps in model diversity, domain coverage, and prompt realism that have limited prior Chinese detection benchmarks.

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Yu Xia, Tong Yu, Zhankui He, Handong Zhao, Julian McAuley, Shuai Li

Robustness in NLP

Challenge:	Existing methods to reduce LLMs' biased outputs rely on reward signals from current model outputs without considering the source of biases.
Approach:	They propose to leverage the reward model in RL alignment as an instrumental variable to perform causal intervention on LLMs.
Outcome:	The proposed method reduces biases by using human feedback to fine tune LLMs to human values.

End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (2020.emnlp-main)

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Zixiang Ding, Rui Xia, Jianfei Yu

Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations (2020.emnlp-main)

Challenge:	Existing methods to extract potential pairs of emotions ignore the fact that the cause and the emotion it triggers are inseparable.
Approach:	They propose two frameworks that combine multi-label learning and multi-labeled learning to extract emotion clauses . they evaluate a benchmark emotion cause corpus and find the best performance .
Outcome:	The proposed frameworks achieve the best performance among all compared systems on the ECPE task.

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Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, Rui Xia

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)

Challenge:	Existing approaches to rumor verification and stance classification fail to exploit intertask dependencies .
Approach:	They propose a Hierarchical Transformer model which uses BERT to obtain thread representations . they propose 'coupled' transformer modules to capture intertask interactions and a post-level attention layer to use predicted stance labels for RV.
Outcome:	The proposed model outperforms existing methods on two benchmark datasets.

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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman

Debiasing Generative Named Entity Recognition by Calibrating Sequence Likelihood (2023.acl-short)

Challenge:	State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text.
Approach:	They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling.
Outcome:	The proposed model can be used to train sentences on language modeling tasks.

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Yu Xia, Yongwei Zhao, Wenhao Wu, Sujian Li

Robustness in NLP

Challenge:	Existing approaches to recognize flat, overlapped and discontinuous entities uniformly have been used for Named Entity Recognition.
Approach:	They propose a reranking-based approach that redistributes the likelihood among candidate sequences depending on their performance via a contrastive loss.
Outcome:	The proposed method boosts baseline and yields competitive or better results compared with the state-of-the-art methods on 8 widely-used datasets for Named Entity Recognition.

Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge (2023.findings-emnlp)

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Fanfan Wang, Jianfei Yu, Rui Xia

Reasoning

Challenge:	Existing studies on ECTEC focus on Causal Emotion Entailment and Emotion-Cause Pair Extraction in Conversations.
Approach:	They propose to decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner.
Outcome:	The proposed model outperforms competing systems on two benchmark datasets.

ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)

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Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi

Challenge:	Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential.
Approach:	They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling.
Outcome:	The proposed model improves performance on four benchmarks and significantly mitigates positional bias.

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)

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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Structured Data in NLP

Challenge:	Existing methods for QA in industrial environments are inherently relational and often updated.
Approach:	They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning.
Outcome:	Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity.

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026.findings-acl)

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Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Chen Yuhui, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao LU, Yanqing Ma, Shiyin Lu, Qifeng Chen

Challenge:	Existing strategies for spatial localization are limited due to their limited capacity to perceive positional data.
Approach:	They propose a location-based approach that leverages locational data to optimize interaction preferences.
Outcome:	The proposed approach achieves SOTA results across offline benchmarks and real-world evaluations.

A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)

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Hongjie Cai, Heqing Ma, Jianfei Yu, Rui Xia

Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)

Challenge:	Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities.
Approach:	They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT.
Outcome:	The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets.

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Chengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao

Comparative Opinion Quintuple Extraction from Product Reviews (2021.emnlp-main)

Challenge:	Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model.
Approach:	They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs.
Outcome:	Experiments show that the proposed approach performs better than previous approaches on various benchmarks.

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Ziheng Liu, Rui Xia, Jianfei Yu

Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)

Challenge:	Comparative opinion mining is an important task in opinion mining.
Approach:	They propose a task to extract comparative opinion quintuples from product reviews . they propose supplementary annotations and construct three datasets for the task .
Outcome:	The proposed method outperforms baseline systems on three datasets and represents a strong benchmark for COQE.

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Qi Gou, Zehua Xia, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Text Generation

Challenge:	Existing question generation systems focus on the internal knowledge within the textual passage or the semantic word space for diverse content planning. Existing solutions focus on relying on the knowledge of the text and the semantic words, but have not considered the potential of external knowledge for expression diversity.
Approach:	They propose a framework for Retrieval-Augmented Style Transfer that utilizes the style of diverse templates for question generation.
Outcome:	The proposed framework outperforms baselines on diversity while being comparable in terms of consistency scores.

ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training (2026.acl-long)

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Yu Liang, Liangxin Liu, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Daiting Shi

Challenge:	ConsistRM is a self-training framework that enables effective and stable GRM training without human annotations.
Approach:	They propose a self-training framework that enables effective and stable GRM training without human annotations.
Outcome:	The proposed framework outperforms vanilla Reinforcement Fine-Tuning (RFT) by 1.5% on five benchmark datasets.

TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization (2026.acl-long)

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Sitong Wu, Haoru Tan, Xichen Zhang, Bin Xia, Wenhu Zhang, Xiaojuan Qi, Bei Yu, Jiaya Jia

Language Models can Evaluate Themselves via Probability Discrepancy (2024.findings-acl)

Challenge:	Current reward models for reinforcement learning (RL) rely on outcome rewards that propagate a single scalar value across all tokens based on final correctness.
Approach:	They propose a framework that derives dense token-level supervision from LLMs . they use a multi-granularity calibration mechanism to modulate teacher influence .
Outcome:	The proposed framework evaluates teacher reliability across problem-level expertise, trajectory-level discrimination, and token-level confidence.

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Tingyu Xia, Bowen Yu, Yuan Wu, Yi Chang, Chang Zhou

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)

Challenge:	Existing evaluation frameworks focus on superficial text differences and fail to align with human judgment.
Approach:	They propose a new method to evaluate the performance of Large Language Models (LLMs) by calculating probability discrepancies between original response generation and revised versions of LLMs.
Outcome:	The proposed method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger.

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Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, Yanghua Xiao

SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (2025.findings-emnlp)

Challenge:	Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability.
Approach:	They propose a method that provides sentence-level citations in LLM-generated responses.
Outcome:	The proposed method achieves 90% accuracy in long-form question-answering tasks.

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Hongfei Xia, Hongru Wang, Zeming Liu, Qian Yu, Yuhang Guo, Haifeng Wang

HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization (2021.emnlp-main)

Challenge:	Existing approaches fail to fully capture all risks in tool utilization, resulting in financial loss or privacy leaking.
Approach:	They propose a framework to assess the safety of LLM tool utilization in a prospective manner, covering malicious user instructions and diverse practical toolsets.
Outcome:	The proposed framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization.

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Ye Liu, Jianguo Zhang, Yao Wan, Congying Xia, Lifang He, Philip Yu

Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)

Challenge:	Existing methods for summarizing semantic graph structure from raw text are cumbersome and inefficient for long-text documents.
Approach:	They propose a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization.
Outcome:	The proposed model performs state-of-the-art on single- and multi-document summarization tasks while using less memory and fewer parameters.

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Jianfei Yu, Jing Jiang, Li Yang, Rui Xia

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)

Challenge:	Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks .
Approach:	They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions.
Outcome:	The proposed approach achieves state-of-the-art on two benchmark datasets.

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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)

Challenge:	Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning.
Approach:	They propose a framework that introduces path-centric reward shaping for agentic RAG training.
Outcome:	The proposed framework improves on existing methods with an average accuracy gain of 7.7 points.

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Chenggong Gong, Jianfei Yu, Rui Xia

LightRAG: Simple and Fast Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task.
Approach:	They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features .
Outcome:	The proposed method improves on the state-of-the-art in two aspects of the ABSA task.

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Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang

Green & Sustainable NLP

Challenge:	Existing RAG systems rely on flat data representations and inadequate contextual awareness . lightRAG framework incorporates graph structures into text indexing and retrieval processes .
Approach:	LightRAG is a framework that integrates graph structures into text indexing and retrieval processes.
Outcome:	The proposed framework incorporates graph structures into text indexing and retrieval processes.

Text-to-ES Bench: A Comprehensive Benchmark for Converting Natural Language to Elasticsearch Query (2025.acl-long)

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DonggeXue DonggeXue, Zhili Pu, Zhentao Xia, Hongli Sun, Ruihui Hou, Guangya Yu, Yupian Lin, Yongqi Fan, Jingping Liu, Tong Ruan

GUI Agents: A Survey (2025.findings-acl)

Challenge:	Recent research on text-to-Query has explored using large language models to convert user query intent to executable code.
Approach:	They propose a novel semantic parsing task that leverages large language models to generate domain-specific language and post-processing code to support multi-index Elasticsearch queries.
Outcome:	The proposed model outperforms DeepSeek-R1 on the large Elasticsearch Dataset (LED) and BirdES datasets.

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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt

Challenge:	Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
Approach:	They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities.
Outcome:	The proposed framework delineates their perception, reasoning, planning, and acting capabilities.

Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023.acl-long)

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Jianfei Yu, Qiankun Zhao, Rui Xia

AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)

Challenge:	Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain.
Approach:	They propose a domain-adaptive language model to generate labeled data from a source domain.
Outcome:	The proposed approach outperforms existing methods on ABSA and Aspect Extraction tasks.

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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021.naacl-main)

Challenge:	AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery.
Approach:	They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows.
Outcome:	The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages.

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Congying Xia, Wenpeng Yin, Yihao Feng, Philip Yu

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection (2020.findings-emnlp)

Challenge:	Text classification is usually studied by labeling texts with relevant categories from a predefined set.
Approach:	They propose a task where a system incrementally handles multiple rounds of new classes . they propose two entailment approaches, ENTAILMENT and HYBRID, which show promise .
Outcome:	The proposed task is based on a few-shot text classification task in the NLP domain.

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Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip Yu

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)

Challenge:	Recent studies show that multi-level matching is difficult due to the scarcity of available annotated utterances.
Approach:	They propose a method where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints.
Outcome:	The proposed method improves representations of labeled and unlabeled instances while retaining high-level information.

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Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang

Syntactic Text Processing

Challenge:	Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.
Approach:	They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task.
Outcome:	The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks.

SAND: Boosting LLM Agents with Self-Taught Action Deliberation (2025.emnlp-main)

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Yu Xia, Yiran Jenny Shen, Junda Wu, Tong Yu, Sungchul Kim, Ryan A. Rossi, Lina Yao, Julian McAuley

Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022.emnlp-main)

Challenge:	Large Language Model (LLM) agents finetuned with supervised finetuning may over-commit towards seemingly plausible but suboptimal actions due to limited action space exploration.
Approach:	They propose a self-taught actioN deliberation framework that allows LLM agents to explicitly deliberate over candidate actions before committing to one.
Outcome:	The proposed framework outperforms state-of-the-art methods on two representative interactive agent tasks and achieves an average 20% improvement over initial finetuning.

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Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yu Xia, Yajuan Lyu, QiaoQiao She

Explainability & Interpretability in NLP

Challenge:	Existing models for multi-hop Question Answering have improved the implicit reasoning ability, but the black box nature of pure neural networks has hindered the construction of explainable intelligent systems.
Approach:	They propose a global differentiation strategy to explore optimal reasoning paths from latent probability space and a Dynamic Adaptive Reasoner to enhance generalization of unseen questions.
Outcome:	The proposed method achieves 17% improvements in F1-score against BreakRC and shows better interpretability.

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)

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Zhongfen Deng, Hao Peng, Congying Xia, Jianxin Li, Lifang He, Philip Yu

A Transition-based Method for Complex Question Understanding (2022.coling-1)

Challenge:	Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process .
Approach:	They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders .
Outcome:	The proposed approach can be used to make an effective decision-making tool for the academic paper review process.

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Yu Xia, Wenbin Jiang, Yajuan Lyu, Sujian Li

Explainability & Interpretability in NLP

Challenge:	Existing work on complex question understanding does not model intermediate states and does not provide step-wise information.
Approach:	They propose a transition-based method where a decider predicts a sequence of actions to build the graph node-by-node.
Outcome:	The proposed method parses complex questions to QDMR using atomic operators . it has transparent and human-readable intermediate results, showing improved interpretability .

Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing (2024.lrec-main)

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Zhenyu Qian, Yiming Qian, Yuting Song, Fei Gao, Hai Jin, Chen Yu, Xia Xie

Contrastive Bootstrapping for Label Refinement (2023.acl-short)

Challenge:	Existing methods for graph processing rely on assumptions about data relations that are inadequate when handling large and complex graph data.
Approach:	They propose a large language model enhanced by an uncertainty-aware module to provide a confidence score on the generated graph data.
Outcome:	The proposed approach surpasses state-of-the-art algorithms by a substantial margin on ten datasets.

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Shudi Hou, Yu Xia, Muhao Chen, Sujian Li

Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)

Challenge:	Existing methods for fine-grained classification categorize texts into coarse-gritty classes, but they are suboptimal in real-world scenarios.
Approach:	They propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages.
Outcome:	The proposed method outperforms the state-of-the-art methods by a large margin on NYT and 20News datasets.

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Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip Yu

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (2020.coling-main)

Challenge:	Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved.
Approach:	They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available.
Outcome:	The proposed model can discriminate emerging intents when no labeled utterances are available in training data.

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Tao Zhang, Congying Xia, Chun-Ta Lu, Philip Yu

Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (2022.acl-long)

Challenge:	Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.
Approach:	They propose a memory-augmented FNET model to tackle unseen types in a zero-shot manner.
Outcome:	The proposed model outperforms the state-of-the-art models with up to 8% gain in Micro-F1 and Macro-F1.

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Yan Ling, Jianfei Yu, Rui Xia

Multimodality

Challenge:	Existing approaches to multimodal Aspect-Based Sentiment Analysis (MABSA) ignore crossmodalalignment and use pre-trained visual and textual models.
Approach:	They propose a multimodal multimodal encoder-decoder framework for MABSA that uses a unified multimodal decoder architecture for all the pretrainingand downstream tasks.
Outcome:	The proposed framework outperforms state-of-the-art approaches on three MABSA subtasks.

Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval (2025.naacl-long)

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Yu Xia, Junda Wu, Sungchul Kim, Tong Yu, Ryan A. Rossi, Haoliang Wang, Julian McAuley

Multi-grained Named Entity Recognition (P19-1)

Challenge:	Existing methods to generate query expansions focus on enhancing textual similarities between search queries and document corpus, overlooking document relations.
Approach:	They propose a knowledge-aware query expansion framework augmenting LLMs with structured document relations from knowledge graph (KG) they leverage document texts as rich KG node representations and use document-based relation filtering for their method.
Outcome:	The proposed framework augments LLMs with structured document relations from knowledge graph (KG) Extensive experiments on three datasets of diverse domains show the advantages compared against state-of-the-art methods on textual and relational semi-structured retrieval.

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Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task.
Approach:	They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Outcome:	The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

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Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi

Challenge:	Existing alignment paradigms for creative writing use static reward signals and supervised data.
Approach:	They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments.
Outcome:	The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references.

Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021.acl-long)

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Hongjie Cai, Rui Xia, Jianfei Yu