Papers by Xue Jiang

S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models (2026.acl-long)

Challenge:	Experimental results show that pretrained language models generate inconsistent factual knowledge in many conversational tasks.
Approach:	They propose a method which explicitly introduces extended feedforward networks (FFNs) in Transformers to enhance factual knowledge expressions given the specific patterns of knowledge-grounded dialogue inputs.
Outcome:	The proposed methods improve the factual expression capability of feedforward networks (FFNs) in knowledge-grounded dialogue systems by knowledge enhancement and alignment respectively.

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Feng Jiang, Zhiyu Lin, Yiyang Liu, Liumeng Xue, Fan Bu, Yuhao Du, Xiangying Chen, Benyou Wang, Haizhou Li

CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending (2024.acl-long)

Challenge:	Existing benchmarks rely heavily on text-based evaluation and largely ignore paralinguistic cues such as prosody, emotion, and speaker traits.
Approach:	They propose a speech-native benchmark for evaluating instruction-following S2S models with explicit assessment of both semantic understanding and paralinguistic expression.
Outcome:	The proposed system enables more natural, robust, and human-aligned speech agents.

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Shiyi Zhu, Jing Ye, Wei Jiang, Siqiao Xue, Qi Zhang, Yifan Wu, Jianguo Li

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)

Challenge:	Existing models that use self-attention and position embedding have anomalous behavior that hinder long context window extrapolation.
Approach:	They propose a collinear constraint between Q and K to integrate RoPE and self-attention.
Outcome:	The proposed model integrates self-attention and position embedding into LLMs without fine-tuning.

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Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law

Information Retrieval

Challenge:	Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling.
Approach:	They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks.
Outcome:	The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments.

Improving Preference Alignment of LLM with Inference-Free Self-Refinement (2025.findings-emnlp)

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Fukun Ma, Kaibin Tian, Jieting Xue, Xiaoyi Wang, Ye Ma, Quan Chen, Peng Jiang, Lijie Wen

Challenge:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Approach:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Outcome:	Experiments show that incorporating IFSR into preference alignment yields performance improvement over 10%.

PACE: Improving Prompt with Actor-Critic Editing for Large Language Model (2024.findings-acl)

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Yihong Dong, Kangcheng Luo, Xue Jiang, Zhi Jin, Ge Li

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)

Challenge:	Prompt with Actor-Critic Editing (PACE) for LLMs improves performance of different human-written prompts, resulting in significant performance discrepancies.
Approach:	They propose to use LLMs as actors and critics to enable automatic prompt editing by taking feedback from both actors performing prompt and criticizing response into account.
Outcome:	The proposed model improves the performance of human-written prompts by 98% and compares to high-quality human-writing prompts.

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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng

ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)

Challenge:	Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting.
Approach:	They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance.
Outcome:	The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity.

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Yuan Chen, Zhenyu Hu, Mengge Xue, Cao Te, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)

Challenge:	Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent.
Approach:	They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency.
Outcome:	The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

Challenge:	Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Approach:	They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Outcome:	The proposed framework maps incomplete learning to causes using observable training and inference signals.

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

Responsible & Trustworthy NLP

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

Reinforcement Learning on Pre-Training Data (2026.acl-long)

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Responsible & Trustworthy NLP

Challenge:	Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs.
Approach:	They propose an efficient generative reward modeling framework grounded in model-internal uncertainty.
Outcome:	The proposed framework reduces inference cost while improving answer accuracy.

LoRA Meets Dropout under a Unified Framework (2024.findings-acl)

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Sheng Wang, Liheng Chen, Jiyue Jiang, Boyang Xue, Lingpeng Kong, Chuan Wu

Green & Sustainable NLP

Challenge:	Parameter-efficientfinetuning (PEFT) has gained popularity as a lightweight approach for model customization.
Approach:	They propose a parameter-efficient dropout method that is overfitting-prone and parameter-freezed.
Outcome:	The proposed method is superior to existing methods and compares with transformer-specific methods.

Low-probability Tokens Sustain Exploration in Reinforcement Learning with Verifiable Reward (2026.findings-acl)

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Guanhua Huang, Tingqiang Xu, Mingze Wang, Qi Yi, Xue Gong, Siheng Li, Ruibin Xiong, Kejiao Li, Yuhao Jiang, Bo Zhou

Reasoning

Challenge:	Recent studies show that RLVR training is slow and results plateau as policy entropy collapses . low-probability regularization (Lp-Reg) reduces the number of low-quality exploratory tokens induced by RL training .
Approach:	They propose a method to reduce RLVR over-penalization by eliminating low-probability exploratory tokens . they propose 'Low-provability Regularization' to reduce the gradual elimination of low-quality exploratory entropy tokens.
Outcome:	The proposed method eliminates low-probability exploratory tokens and prevents suppression of potentially valuable low-property candidates.

Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)

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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo

Question Answering

Challenge:	Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model.
Approach:	They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering.
Outcome:	The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies.

Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)

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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang

Multilinguality

Challenge:	Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections.
Approach:	They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models.
Outcome:	The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages.

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts (2023.findings-emnlp)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Zhang, Jun Zhou, Defu Lian, Ying Wei

Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach (2020.findings-emnlp)

Challenge:	Continual pre-training has been used for a multitude of domains and tasks . a continually pre-trained model can show a non-decreasing performance on unseen domains .
Approach:	They propose a method that generates domain-specific prompts by agreement and disagreement losses.
Outcome:	The proposed method achieves improvements of 3.57% and 3.4% on two real-world datasets.

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Maosen Zhang, Nan Jiang, Lei Li, Yexiang Xue

Text Generation

Challenge:	Generating natural language under complex constraints is a principled formulation towards controllable text generation.
Approach:	They propose a method to specify combinatorial constraints for sentence generation . they use a tree search algorithm embedded into the proposal process of the Markov Chain Monte Carlo .
Outcome:	The proposed method achieves consistent and significant improvement on multiple language generation tasks.

Breakthrough from Nuance and Inconsistency: Enhancing Multimodal Sarcasm Detection with Context-Aware Self-Attention Fusion and Word Weight Calculation. (2024.lrec-main)

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Hongfei Xue, Linyan Xu, Yu Tong, Rui Li, Jiali Lin, Dazhi Jiang

Sentiment Analysis

Challenge:	Existing methods for sarcasm detection rely on feature concatenation to fuse different modalities or model inconsistencies among modalités.
Approach:	They propose to use Context-Aware Self-Attention Fusion to integrate local and momentary multimodal information into specific words to illustrate the inconsistencies between connotation and denotation.
Outcome:	The proposed method achieves an accuracy of 76.9 and an F1 score of 76.1 on the MUStARD dataset, surpassing the current state-of-the-art IWAN model by 1.7 and 1.6 respectively.

Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation (2026.acl-long)

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Yihong Dong, Zhaoyu Ma, Xue Jiang, Zhiyuan Fan, Jiaru Qian, Yongmin Li, Jianha Xiao, Zhi Jin, Ge Li

Programming Languages in NLP

Challenge:	Diffusion language models (DLMs) offer advantages in parallel generation and bidirectional context modeling, but they face a critical trade-off between inference speed and output quality for tasks with strict structural constraints such as code generation.
Approach:	They propose an efficient sampling algorithm that reduces the number of tokens unmasked per step based on the model’s evolving confidence.
Outcome:	The proposed method improves Pass@1 accuracy by 1.9% while achieving 251.4% inference speedup.

Benchmarking Fine-Grained Error Detection in Multimodal Reasoning (2026.acl-long)

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Chi-Min Chan, Han Zhu, Chunyang Jiang, Jiaming Ji, Juntao Dai, Wei Xue, Sirui Han, Yike Guo

Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)

Challenge:	Multimodal Process Reward Models (MPRMs) have emerged as a pivotal framework for enhancing the reasoning capabilities of Multimodal Large Language Models.
Approach:	They propose a benchmark specifically designed to evaluate MPRMs’ proficiency in detecting erroneous reasoning steps across diverse error categories.
Outcome:	The proposed model achieves up to 4.8% performance improvement through test-time scaling.

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Yihong Dong, Yuchen Liu, Xue Jiang, Bin Gu, Zhi Jin, Ge Li

Programming Languages in NLP

Challenge:	Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation.
Approach:	They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions.
Outcome:	The proposed approach outperforms baselines and humanEval benchmarks on CodeRepetEval dataset and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.

Enhancing Air Quality Prediction with Social Media and Natural Language Processing (P19-1)

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Jyun-Yu Jiang, Xue Sun, Wei Wang, Sean Young

Information Extraction & Text Mining

Challenge:	predicting air quality is a major concern for human health, but the changes of air quality conditions are still difficult to monitor.
Approach:	They propose to exploit social media and natural language processing techniques to enhance air quality prediction.
Outcome:	The proposed approach improves air quality prediction over baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores.

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)

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Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li

RCTEA: Richness-guided Co-training for Temporal Entity Alignment (2026.findings-acl)

Challenge:	Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data.
Approach:	They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution.
Outcome:	The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution.

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Jiayun Li, Wen Hua, Shiqi Fan, Fengmei Jin, Haiyang Jiang, Xue Li

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)

Challenge:	Existing TEA models fail to capture the orthogonal yet complementary effect between structural and temporal features.
Approach:	They propose a framework that jointly models structural and temporal aspects of Temporal Knowledge Graphs for entity alignment.
Outcome:	The proposed framework achieves state-of-the-art on public TEA benchmarks.

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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong

DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models (2026.findings-acl)

Challenge:	Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge.
Approach:	They propose a benchmark to evaluate domain specialization methods in real-world software development.
Outcome:	KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development.

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Shaokai He, Kaiwen Wei, Xinyi Zeng, Xiang Chen, Xue Yang, Zhenyang Li, Jiang Zhong, Yu Tian

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)

Challenge:	Existing large language models exhibit unidirectional behavior when processing bidirectional relationships . authors propose a solution to alleviate the reversal curse in Diffusion LLMs .
Approach:	They propose a model that addresses the "reversal curse" of bidirectional behavior in large language models . they propose 'entity-aware training' and balanced data construction to alleviate asymmetry and missing relations .
Outcome:	The proposed model alleviates the "reversal curse" in Diffusion LLMs . the proposed model employs whole-entity masking to mitigate entity fragmentation .

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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev

Programming Languages in NLP

Challenge:	CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems.
Approach:	They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries .
Outcome:	The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains.

TrojFSP: Trojan Insertion in Few-shot Prompt Tuning (2024.naacl-long)

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Mengxin Zheng, Jiaqi Xue, Xun Chen, Yanshan Wang, Qian Lou, Lei Jiang

Responsible & Trustworthy NLP

Challenge:	Prompt tuning on a few data samples presents security issues, e.g., Trojan attacks.
Approach:	They propose a method to transfer established data poisoning attacks directly to few-shot prompt tuning, a technique to address the poisoned imbalance issue.
Outcome:	The proposed method achieves an ASR of over 99% while maintaining negligible decreases in CDA.

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li

Reasoning

Challenge:	Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities.
Outcome:	The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks.

PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA (2024.acl-long)

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Sheng Wang, Boyang Xue, Jiacheng Ye, Jiyue Jiang, Liheng Chen, Lingpeng Kong, Chuan Wu

Green & Sustainable NLP

Challenge:	Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA) is an intra-layer sharing mechanism that circumvents the drawbacks of peer parameter-sharing methods.
Approach:	They propose a partially rotation-enhanced low-rank adaptation (PRoLoRA) that shares four components to reduce the cost of LoRA and improves model capacity.
Outcome:	Empirical results show that PRoLoRA outperforms LoRA on multiple instruction tuning datasets.

Graceful Forgetting in Generative Language Models (2025.emnlp-main)

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Chunyang Jiang, Chi-Min Chan, Yiyang Cai, Yulong Liu, Wei Xue, Yike Guo