Papers by Rui Zheng

Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective (2022.coling-1)

Challenge:	Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
Approach:	They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks.
Outcome:	The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting.

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Shihan Dou, Rui Zheng, Ting Wu, SongYang Gao, Junjie Shan, Qi Zhang, Yueming Wu, Xuanjing Huang

Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)

Challenge:	Existing methods to debiase samples with biased features obstructs the model in learning from non-biased parts of the samples.
Approach:	They propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective by using Random Fourier Features and weighted re-sampling to decorrelate dependencies between features.
Outcome:	The proposed method eliminates spurious correlations in a fine-grained manner from a feature space perspective.

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Shu Zhou, Rui Zhao, Zhengda Zhou, Haohan Yi, Xuhui Zheng, Hao Wang

Natural Language Interfaces

Challenge:	Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance.
Approach:	They propose a memory network with logical inference for extractive QA in multiparty dialogues.
Outcome:	The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks.

CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language (2026.acl-long)

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Rui Zhao, Xuewen Zhong, Xiaoyun Zheng, Jinsong Su, Yidong Chen

CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models (2024.findings-acl)

Challenge:	CNSL-bench is the first comprehensive Chinese National Sign Language benchmark . current MLLMs are inferior to human performance, despite advances in multimodal modeling .
Approach:	They propose a Chinese National Sign Language benchmark to evaluate multimodal large language models in sign language understanding.
Outcome:	The proposed benchmark evaluates 21 open-source and proprietary MLLMs . results show that current models are inferior to human performance .

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Qian Lou, Xin Liang, Jiaqi Xue, Yancheng Zhang, Rui Xie, Mengxin Zheng

Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding (2022.findings-acl)

Challenge:	Existing certified robustness methods for certifying input-specific text perturbations have shown promise in certifyling UTPs, but masking only adversarial words can eliminate the attack.
Approach:	They propose a method to certify a language model’s robustness against UTPs by using random smoothing.
Outcome:	The proposed method achieves high certified accuracy under extensive masking and achieves state-of-the-art results in multiple settings.

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Rui Cao, Yihao Wang, Yuxin Liang, Ling Gao, Jie Zheng, Jie Ren, Zheng Wang

Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning (2024.emnlp-main)

Challenge:	Unsupervised contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data.
Approach:	They propose a momentum contrastive learning model with negative sample queue for sentence embedding with a simulated model with EMA update mechanism.
Outcome:	The proposed model achieves a Spearman’s correlation of 77.27% on the semantic text similarity task and a maximum traceable distance metric.

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Lu Chen, Rui Zheng, Binghai Wang, Senjie Jin, Caishuang Huang, Junjie Ye, Zhihao Zhang, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)

Challenge:	Current methods rely on ranking losses to teach reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions.
Approach:	They propose a method that incorporates contrastive learning into the reward modeling process to enhance generalization and stabilize the reinforcement learning training process.
Outcome:	The proposed method enhances generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences.

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Ning Li, Zheng Zhang, Zhenya Huang, Rui Li, Yi Zhan, Yinbo Luo, Qi Liu, Enhong Chen

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning.
Approach:	They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion .
Outcome:	The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

PQR: Improving Dense Retrieval via Potential Query Modeling (2025.acl-long)

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Junfeng Kang, Rui Li, Qi Liu, Yanjiang Chen, Zheng Zhang, Junzhe Jiang, Heng Yu, Yu Su

Information Retrieval

Challenge:	Existing training data is sparse, with each document associated with one or a few labeled queries.
Approach:	They propose a training-free potential query retrieval framework to address this problem . they use a Gaussian mixture distribution to model all potential queries for a document .
Outcome:	The proposed method is able to capture comprehensive semantic information from a document with multiple queries.

Orthogonal Subspace Learning for Language Model Continual Learning (2023.findings-emnlp)

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Xiao Wang, Tianze Chen, Qiming Ge, Han Xia, Rong Bao, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)

Challenge:	Existing methods for continual learning in language models suffer catastrophic forgetting when learning sequential tasks.
Approach:	They propose an orthogonal low-rank adaptation approach for continual learning in language models that uses orthogons to learn sequentially.
Outcome:	The proposed approach outperforms state-of-the-art methods on continual learning benchmarks and preserves generalization ability of LLMs on unseen tasks.

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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)

Challenge:	Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials.
Approach:	They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch.
Outcome:	The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks.

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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang

Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph (2021.findings-acl)

Challenge:	Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems.
Approach:	They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models.
Outcome:	The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models.

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Rui Liu, Zheng Lin, Yutong Tan, Weiping Wang

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

Challenge:	Existing methods for stance detection are not applicable to zero-shot and few-shot scenarios.
Approach:	They propose a model that integrates commonsense knowledge into a stance detection model.
Outcome:	The proposed model outperforms state-of-the-art methods on zero-shot and few-shot stance detection tasks.

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts (2025.acl-long)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Haoyuan Wu, Rui Ming, Haisheng Zheng, Zhuolun He, Bei Yu

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Large language models have shown significant promise in question-answering tasks . noisy reference documents hinder performance of LLMs, causing disproportionate attention to irrelevant content .
Approach:	They propose an adaptive large language model that allocates disproportionate attention to irrelevant documents . they use transformers to train the model and integrate it into pre-trained Transformer blocks .
Outcome:	The proposed model outperforms state-of-the-art models on noisy-context benchmarks.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng

Information Extraction & Text Mining

Challenge:	Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency.
Approach:	They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment .
Outcome:	The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples.

Uncertainty Aware Learning for Language Model Alignment (2024.acl-long)

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Yikun Wang, Rui Zheng, Liang Ding, Qi Zhang, Dahua Lin, Dacheng Tao

Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild (2024.acl-long)

Challenge:	Existing alignment strategies that focus on diverse and high-quality data often overlook the intrinsic uncertainty of tasks, learning all data samples equally.
Approach:	They propose to introduce the sample uncertainty into the alignment of different task scenarios by a simple fashion by setting the label smoothing value of training according to the uncertainty of individual samples.
Outcome:	The proposed model outperforms standard supervised fine-tuning on high-entropy tasks and complex low-entropic tasks.

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Ting Wu, Jingyi Liu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation (2024.acl-srw)

Challenge:	Existing methods for continual relation extraction (CRE) excel in preserving old knowledge but falter when confronted with contaminated data streams.
Approach:	They propose a noise-resistant contrastive framework for continual relation extraction (CRE) that preserves old knowledge while learning incremental corrupted relations.
Outcome:	The proposed framework outperforms state-of-the-art methods on various benchmarks with increasing noise rates.

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Yikun Wang, Rui Zheng, Haoming Li, Qi Zhang, Tao Gui, Fei Liu

Text Generation

Challenge:	Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. Obtaining a large volume of expert-annotated data is costly for most tasks.
Approach:	They propose a method that trains the model to prioritize the best responses from a pool of candidates created for a task using ranking metrics.
Outcome:	The proposed method is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods.

GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization (2025.acl-demo)

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Wenliang Zheng, Sarkar Snigdha Sarathi Das, Yusen Zhang, Rui Zhang

When Generative Adversarial Networks Meet Sequence Labeling Challenges (2024.emnlp-main)

Challenge:	Recent advances in prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations.
Approach:	They propose a framework that unifies different methods under a unified, customizable API while delivering highly effective prompts for different tasks.
Outcome:	The proposed framework unifies multiple methods under a unified, customizable API while delivering highly effective prompts for different tasks.

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Yu Tong, Ge Chen, Guokai Zheng, Rui Li, Jiang Dazhi

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models (2025.acl-long)

Challenge:	Existing approaches for sequence labeling use a feature extractor and sequence tagger . a recent study shows that SLGAN is versatile and highly effective .
Approach:	They propose a framework that harnesses the capabilities of Generative Adversarial Networks to address sequence labeling challenges.
Outcome:	The proposed framework exhibits strong adaptability to various sequence labeling tasks.

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Zihan Qiu, Zeyu Huang, Bo Zheng, Kaiyue Wen, Zekun Wang, Rui Men, Ivan Titov, Dayiheng Liu, Jingren Zhou, Junyang Lin

Programming Languages in NLP

Challenge:	Existing Mixture-of-Experts training frameworks use a micro-batch to calculate LBL . micro-batches are restricted to a single sequence, preventing expert specialization .
Approach:	They propose to use a global-batch to loosen the load balance constraint for MoEs models . they propose to synchronize fi across micro-batches and then use it to calculate the LBL .
Outcome:	The proposed global-batch LBL improves the domain specialization of experts . the micro-battery LBL is almost at the sequence level, and the router is pushed to distribute the token evenly .

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)

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Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

Challenge:	Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available.
Approach:	They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN.
Outcome:	The proposed approach significantly improves learning effectiveness when a small number of training examples are available.

Evaluating LLMs’ Assessment of Mixed-Context Hallucination Through the Lens of Summarization (2025.findings-acl)

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Siya Qi, Rui Cao, Yulan He, Zheng Yuan

Text Generation

Challenge:	Large language models generate coherent text and follow instructions across diverse tasks, but a critical challenge in scaling LLM applications is hallucination, where the generated content lacks factual grounding or deviates from the intended discourse context.
Approach:	They use summarization as a representative task to evaluate LLMs' capability in detecting mixed-context hallucinations, specifically distinguishing between factual and non-factual hallucinos.
Outcome:	The proposed model distinguishes between factual and non-factual hallucinations, and their performance bottlenecks.

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

Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization (2023.findings-acl)

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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Ting Wu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Existing methods for minimizing the worst-case loss of annotated groups are lacking in practice due to expensive annotations and privacy issues.
Approach:	They propose a distributionally robust optimization framework that relaxes group identification into direct parameterization by using an interactive training mode.
Outcome:	The proposed method outperforms state-of-the-art methods on synthetic and real-world text classification tasks.

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Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang

PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)

Challenge:	Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities.
Approach:	They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments.
Outcome:	The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments.

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Rui Zheng, Rong Bao, Qin Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals (2024.lrec-main)

Challenge:	Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples.
Approach:	They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen.
Outcome:	The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters.

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Rui Zheng, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)

Challenge:	Existing models that extract discrete inputs into fixed-length representations are vulnerable to adversarial attacks that place perturbations on clean inputs to fool DNNs.
Approach:	They propose to inspect the subspaces of sample features through spectral analysis to better understand adversarial attacks.
Outcome:	The proposed strategy enables the model to inherently suppress adversaries, which boosts model robustness and motivates new directions of effective adversarial defense.

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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang

Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)

Challenge:	Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials.
Approach:	They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located.
Outcome:	The proposed method outperforms previous detection methods by large margins on three text classification tasks.

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Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

Information Extraction & Text Mining

Challenge:	Distant supervision models suffer from high label noise and are not reliable for DS.
Approach:	They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF.
Outcome:	The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function.

Connectivity Patterns are Task Embeddings (2023.findings-acl)

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Zhiheng Xi, Rui Zheng, Yuansen Zhang, Xuanjing Huang, Zhongyu Wei, Minlong Peng, Mingming Sun, Qi Zhang, Tao Gui

ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020.acl-main)

Challenge:	Existing methods for predicting inter-task transferability are sparse and task-specific.
Approach:	They propose a method that uses connectivity patterns of neurons as a unique identifier associated with a task.
Outcome:	The proposed method outperforms baselines in predicting inter-task transferability across data regimes and transfer settings while keeping high efficiency in computation and storage.

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Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev

Explainability & Interpretability in NLP

Challenge:	Neural networks lack the ability to reason about qualitative physics and cannot generalize to scenarios and tasks unseen during training.
Approach:	They propose a framework for reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events.
Outcome:	The proposed framework generates explanations of how the physical simulation will causally evolve so that an agent or a human can reason about a solution using interpretable descriptions.

Debt Collection Negotiations with Large Language Models: An Evaluation System and Optimizing Decision Making with Multi-Agent (2025.findings-acl)

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Xiaofeng Wang, Zhixin Zhang, Jin Guang Zheng, Yiming Ai, Rui Wang

Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)

Challenge:	Debt collection negotiations (DCN) are vital for managing non-performing loans (NPLs) prior systems lacking dynamic negotiation and real-time decision-making capabilities.
Approach:	They propose a framework for debt negotiation that incorporates planning and judging modules to improve decision rationality.
Outcome:	The proposed framework improves decision rationality and integrates planning and judging modules to improve decision rationalness.

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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values.
Approach:	They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values .
Outcome:	The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets.

Characterizing the Impacts of Instances on Robustness (2023.findings-acl)

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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge

Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)

Challenge:	Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances.
Approach:	They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.
Outcome:	The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.

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Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao

Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language (2025.findings-acl)

Challenge:	Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs .
Approach:	They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained .
Outcome:	The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency.

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Jipeng Zhang, Jianshu Zhang, Yuanzhe Li, Renjie Pi, Rui Pan, Runtao Liu, Zheng Ziqiang, Tong Zhang

LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) excel at generating code for high-resource programming languages (HRPLs) however, they struggle significantly with low-resourced programming languages such as D, exacerbating the digital divide.
Approach:	They propose a method to generate LRPL data using LLM's general knowledge, HRPL proficiency, and in-context learning capabilities.
Outcome:	The proposed method improves on R, D, Racket, and Bash, while maintaining the same quality.

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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning (2024.findings-naacl)

Challenge:	Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS).
Approach:	They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning.
Outcome:	The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks.

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Yinger Zhang, Hui Cai, Xierui Song, Yicheng Chen, Rui Sun, Jing Zheng

Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)

Challenge:	Existing approaches to enable large language models to implement function calling are limited in their tool-use capabilities.
Approach:	They propose a controllable, target-driven approach to empower LLMs to operate external APIs only via prompts.
Outcome:	The proposed approach limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion.

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Rui Zheng, Bao Rong, Yuhao Zhou, Di Liang, Sirui Wang, Wei Wu, Tao Gui, Qi Zhang, Xuanjing Huang

ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)

Challenge:	Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples.
Approach:	They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search.
Outcome:	The proposed method improves on previous work on adversarial robustness evaluation.

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Yuhao Zhou, Wenxiang Chen, Rui Zheng, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

Challenge:	Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness.
Approach:	They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks.
Outcome:	The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning.

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Rong Bao, Rui Zheng, Liang Ding, Qi Zhang, Dacheng Tao

A Survey on In-context Learning (2024.emnlp-main)

Challenge:	Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection.
Approach:	They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information.
Outcome:	The proposed method improves on three text classification tasks on four advanced attack algorithms.

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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui

Low-Resource NLP

Challenge:	In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples .
Approach:	They propose to explore ICL to evaluate and extrapolate the ability of large language models.
Outcome:	The proposed methods can be used to evaluate and extrapolate the ability of large language models.

Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)

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Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan

Efficient Adversarial Training with Robust Early-Bird Tickets (2022.emnlp-main)

Challenge:	Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability.
Approach:	They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification.
Outcome:	Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard.

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Zhiheng Xi, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Ranking and Sampling in Open-Domain Question Answering (D19-1)

Challenge:	Existing methods to improve the robustness of pre-trained language models are expensive because of the need to generate adversarial examples via gradient descent.
Approach:	They propose an adversarial optimization method that searches for robust tickets with structured sparsity in the early stage and fine-tunes tickets in the remaining time.
Outcome:	The proposed method achieves up to 7 13 training speedups while maintaining comparable or even better robustness compared to the most competitive state-of-the-art methods.

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Yanfu Xu, Zheng Lin, Yuanxin Liu, Rui Liu, Weiping Wang, Dan Meng

Natural Language Interfaces

Challenge:	Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing.
Approach:	They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph.
Outcome:	Experiments on three datasets show that the proposed model advances the state of the art.

LoReFact: Bridging the Logic Gap in Fact-Checking (2026.findings-acl)

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Qiming Xie, Wenjie Zheng, Xiangqing Shen, Rui Xia

UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)

Challenge:	Existing fact-checking methods focus on verification of individual facts, overlooking logical dependencies . a recent study shows that text containing logical errors may still be misjudged as factual .
Approach:	They propose a content–logic coupled factuality evaluation paradigm that conceptualizes factual dimension along two complementary dimensions: content factualism and logic factuity.
Outcome:	The proposed paradigm bridges the gap between factual verification and content factuality . it incorporates the logical dimension and a logic-aware metric to expose and penalize logical fallacies.

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Rui Li, Liyang He, Qi Liu, Zheng Zhang, Heng Yu, Yuyang Ye, Linbo Zhu, Yu Su

Information Retrieval

Challenge:	Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios.
Approach:	They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks.
Outcome:	The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering.

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)

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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang

Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models (2025.acl-long)

Challenge:	Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data .
Approach:	They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation.
Outcome:	The proposed framework outperforms open-source baselines and is competitive with GPT-5.

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Junfeng Tian, Da Zheng, Yang Chen, Rui Wang, Colin Zhang, Debing Zhang

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Large language models (LLMs) have prioritized expanding the context window from which they can incorporate more information.
Approach:	They propose a data augmentation strategy to enable large language models to gain long-context capabilities without the need to modify existing data mixture.
Outcome:	The proposed model outperforms existing models on 20 billion tokens and achieves 75% and 84.5% accuracy on RULER at 128K context length.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (2022.coling-1)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, Weiping Wang

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

Challenge:	stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier .
Approach:	They propose a semi-supervised framework for few-shot stance detection that uses unlabeled data to learn more distinguishable representations for different targets.
Outcome:	The proposed framework achieves state-of-the-art performance on multiple benchmark datasets.

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Programming Languages in NLP

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

HAUNTATTACK: When Attack Follows Reasoning as a Shadow (2026.findings-acl)

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Jingyuan Ma, Rui Li, Zheng Li, Junfeng Liu, Heming Xia, Lei Sha, Zhifang Sui

RealBehavior: A Framework for Faithfully Characterizing Foundation Models’ Human-like Behavior Mechanisms (2023.findings-emnlp)

Challenge:	Emerging Large Reasoning Models (LRMs) excel in mathematical and reasoning tasks, showcasing remarkable capabilities.
Approach:	They propose a framework that embeds harmful instructions into reasoning questions . they evaluate 11 LRMs and observe an average attack success rate of over 70% .
Outcome:	The proposed framework improves reasoning models by 13 percentage points over baseline.

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Enyu Zhou, Rui Zheng, Zhiheng Xi, Songyang Gao, Xiaoran Fan, Zichu Fei, Jingting Ye, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing studies on human-like behaviors in foundation models do not verify their faithfulness . a simple application of psychological tools cannot faithfully characterize all human-type behaviors .
Approach:	They propose a framework to characterize humanoid behaviors in foundation models . they argue that a simple application of psychological tools cannot faithfully characterize all human-like behaviors .
Outcome:	The proposed framework assesses the faithfulness of results based on reproducibility, internal consistency, and generalizability.

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)

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Wei Shen, Rui Zheng, Wenyu Zhan, Jun Zhao, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

Challenge:	Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs .
Approach:	They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique.
Outcome:	The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias.

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang