Papers by Jing Liang

Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts (2026.acl-long)

Challenge:	Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear.
Approach:	They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer .
Outcome:	The proposed applications improve hallucination detection performance by integrating two different inputs.

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Sijia Luo, Xiaokang Zhang, Yuxuan Hu, Bohan Zhang, Ke Wang, Jinbo Su, Mengshu Sun, Lei Liang, Jing Zhang

Challenge:	Existing methods for storing key-value caches during long-horizon rollouts cause performance collapses.
Approach:	They propose a new training paradigm that empowers stable RL training under sparse rollouts.
Outcome:	The proposed model reduces rollout overhead while maintaining the performance.

Synergizing Large Language Models and Pre-Trained Smaller Models for Conversational Intent Discovery (2024.findings-acl)

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Jinggui Liang, Lizi Liao, Hao Fei, Jing Jiang

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)

Challenge:	Current attempts at CID rely on pretrained Small Language Models (SLMs) this lacks the ability to label new intents and is a challenge for small language models.
Approach:	They propose to combine Large Language Models (LLMs) with pre-trained SLMs for CID to enhance the semantic comprehension of LLMs.
Outcome:	The proposed approach improves the semantic comprehension of LLMs and the operational agility of SLMs by realigning existing descriptors within the SLM’s feature space to correct cluster distortion and promote robust learning of representations.

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Chen Liang, Jing Xu, Yangkun Lin, Chong Yang, Yongliang Wang

Multimodality

Challenge:	Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields.
Approach:	They propose a novel GNN-based ERC model that captures speaker and position information.
Outcome:	The proposed model captures speaker and position-aware conversation structure information.

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, Richard Yi Da Xu, Yunya Song, Xian Yang

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)

Challenge:	Existing models struggle to balance predictive accuracy with human-understandable rationales.
Approach:	They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation.
Outcome:	Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation.

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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong

Explainability & Interpretability in NLP

Challenge:	Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored.
Approach:	They propose a survey structured around the pipeline to identify and improve MI models.
Outcome:	The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency.

MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning (2026.findings-acl)

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Xuchen Li, Jing Chen, Xuzhao Li, Hao Liang, Xiaohuan Zhou, Taifeng Wang, Wentao Zhang

Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models (2025.findings-emnlp)

Challenge:	Existing data synthesis methods suffer from limited diversity and lack precise control over problem difficulty, making them insufficient for efficient training paradigms such as curriculum learning.
Approach:	They propose a data synthesis paradigm that generates high-quality, difficulty-controllable mathematical reasoning problems through hybrid and decomposed strategies.
Outcome:	The proposed paradigm outperforms existing methods and improves mathematical reasoning abilities.

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Zonghao Ying, Deyue Zhang, Zonglei Jing, Yisong Xiao, Quanchen Zou, Aishan Liu, Siyuan Liang, Xiangzheng Zhang, Xianglong Liu, Dacheng Tao

Robustness in NLP

Challenge:	Existing methods for implementing multi-turn jailbreaks struggle to balance semantic coherence with attack effectiveness, resulting in benign semantic drift or ineffective detection evasion.
Approach:	They propose a framework that reformulates harmful queries into benign reasoning tasks and leverages LLMs’ strong reasoning capabilities to compromise safety alignment.
Outcome:	The proposed framework achieves state-of-the-art attack effectiveness in complex conversational scenarios, with average ASRs increasing by up to 96%.

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

LLM Agents for Education: Advances and Applications (2025.findings-emnlp)

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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)

Challenge:	Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes.
Approach:	This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems .
Outcome:	The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings.

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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu

A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (2023.emnlp-main)

Challenge:	Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning.
Approach:	They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.
Outcome:	The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.

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Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, Ruifeng Xu

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)

Challenge:	Existing studies in Emotion Recognition in Conversations (ERC) focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data.
Approach:	They propose a training-free debiasing framework that extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations.
Outcome:	Experiments on three public datasets show that the proposed framework improves generalization ability and fairness across different ERC models.

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Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, Jing Tang

Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control (2025.acl-long)

Challenge:	Existing LLMs are delicate and elusive in prompt words and styles.
Approach:	They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning .
Outcome:	The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality.

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Jinggui Liang, Dung Vo, Yap Hong Xian, Hai Leong Chieu, Kian Ming A. Chai, Jing Jiang, Lizi Liao

Explainability & Interpretability in NLP

Challenge:	Existing methods for style transfer between Singlish and Standard English lack explainability and fine-grained control.
Approach:	They propose a multi-agent framework where large language models act as expert agents for each linguistic aspect.
Outcome:	The proposed model enables precise, interpretable transformations, advancing explainability in NLP for Singlish.

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)

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Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li, Jing Li, Ruifeng Xu, Min Zhang

Sentiment Analysis

Challenge:	Existing methods operate by learning to fuse modalities, leading to frequent misjudgments.
Approach:	They propose a paradigm shift from learning to fuse to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.
Outcome:	The proposed model significantly outperforms baseline models and exhibits strong generalization.

Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)

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Xinyu Zuo, Haijin Liang, Ning Jing, Shuang Zeng, Zhou Fang, Yu Luo

Information Extraction & Text Mining

Challenge:	Experimental results show that fine-grained entity typing (FET) can be used to deduce specific semantic types of entities.
Approach:	They propose a type-enriched hierarchical contrastive strategy to model type differences . their method can make type information directly perceptible and improve distinguishability .
Outcome:	The proposed method can model the differences between hierarchical types and distinguish multi-grained similar types at different granularities.

DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)

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Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang

You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)

Challenge:	Recent advances in neural theorem-proving resort to large language models and tree searches.
Approach:	They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values.
Outcome:	The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate.

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Ke Yang, Dongyang Liang, Jing Yu, Shuguang Yuan, Chi Chen

Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)

Challenge:	Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary.
Approach:	They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution.
Outcome:	The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity.

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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng

Challenge:	Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content.
Approach:	They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces .
Outcome:	The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining.

METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (2020.coling-main)

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Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, Liang Pang

Sentiment Analysis

Challenge:	Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences .
Approach:	They propose a mutual enhanced transformation network for the ABSA task . it improves representation learning of the aspect with contextual semantic features .
Outcome:	The proposed model improves representation learning of the aspect with contextual semantic features, giving the aspect more abundant information.

Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery (2024.naacl-long)

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Jinggui Liang, Lizi Liao, Hao Fei, Bobo Li, Jing Jiang

Challenge:	Generalized category discovery (GCD) is a crucial task in open-world computing, where new categories frequently emerge, necessitating models that can adapt and learn continually.
Approach:	They propose to integrate the feedback from LLMs into an active learning paradigm to simplify the labeling task and minimize the spread of inaccurate feedback.
Outcome:	The proposed approach significantly improves baseline models at a nominal average cost.

OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao

CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)

Challenge:	Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability.
Approach:	They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability.
Outcome:	Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA.

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Jingqian Zhao, Bingbing Wang, Geng Tu, Yice Zhang, Qianlong Wang, Bin Liang, Jing Li, Ruifeng Xu

An Alignment-Agnostic Model for Chinese Text Error Correction (2021.findings-emnlp)

Challenge:	Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance .
Approach:	They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge.
Outcome:	The proposed evaluation strategy can be used to update datasets with real-world knowledge.

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Liying Zheng, Yue Deng, Weishun Song, Liang Xu, Jing Xiao

Syntactic Text Processing

Challenge:	Existing models for Chinese text error correction can correct mistaken, missing and redundant characters, but they cannot handle missing or redundant characters.
Approach:	They propose an alignment-agnostic framework to correct Chinese text errors . framework detects missing and redundant characters and can be used as a cold start model .
Outcome:	The proposed framework can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided.

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang

Semantic Text Processing

Challenge:	Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge.
Approach:	They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types.
Outcome:	The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets.

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026.acl-long)

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Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang, Ngai Wong

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)

Challenge:	Existing studies show that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability.
Approach:	They propose a method that routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality.
Outcome:	The proposed method outperforms baselines on six benchmarks including instruct tuning and math reasoning settings.

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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin

Programming Languages in NLP

Challenge:	Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency.
Approach:	They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process.
Outcome:	The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing.

How Does the Disclosure of AI Assistance Affect the Perceptions of Writing? (2024.emnlp-main)

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Zhuoyan Li, Chen Liang, Jing Peng, Ming Yin

Semantic Text Processing

Challenge:	Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows.
Approach:	They conduct an experimental study to determine whether disclosure of AI assistance in the writing process would affect people's evaluation on the quality of the writing and ranking of different writings.
Outcome:	The disclosure of AI assistance decreases the average quality ratings for argumentative essays and creative stories, and increases the quality of the writings.

An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)

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Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li, Zhoujun Li