Papers by Zheng Wen

IterAlign: Iterative Constitutional Alignment of Large Language Models (2024.naacl-long)

Challenge:	Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses.
Approach:	They propose a method to incorporate conversational context and knowledge into dialogue generation models . they use Latent Vectors to capture the relationship between context and knowing .
Outcome:	The proposed approach improves performance with two standard datasets and human evaluations.

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Xiusi Chen, Hongzhi Wen, Sreyashi Nag, Chen Luo, Qingyu Yin, Ruirui Li, Zheng Li, Wei Wang

Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token’s Nature (2026.acl-long)

Challenge:	Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty, improving the LLM alignment by up to 13.5% in harmlessness.
Approach:	They propose a data-driven constitution discovery and self-alignment framework called IterAlign to overcome these drawbacks by leveraging red teaming to uncover weaknesses of an LLM.
Outcome:	Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty by up to 13.5%.

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Zheng Liu, Mengjie Liu, Siwei Wen, Mengzhang Cai, Bin Cui, Conghui He, Wentao Zhang

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)

Challenge:	Existing methods that use entropy as a discrete filter or post-hoc regulator are limited in their ability to optimize for reasoning tasks.
Approach:	They propose a token-aware algorithm that continuously adapts optimization dynamics based on token-level entropy throughout the entire training process.
Outcome:	Extensive experiments on mathematical reasoning, code, and logic tasks across multiple models demonstrate HAPO’s consistent superiority over DAPO.

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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Approach:	They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions.
Outcome:	The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks.

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Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang

Listen, Watch, and Learn to Feel: Retrieval-Augmented Emotion Reasoning for Compound Emotion Generation (2025.findings-acl)

Challenge:	Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions.
Approach:	They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm.
Outcome:	The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model.

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Zhuofan Wen, Zheng Lian, Shun Chen, Hailiang Yao, Longjiang Yang, Bin Liu, Jianhua Tao

Information Retrieval

Challenge:	Existing methods to assess human emotion are limited by the subjective nature of emotion perception, limiting the robustness of existing models.
Approach:	They propose a plug-and-play module that enhances MLLMs’ ability to tackle compound and context-rich emotion tasks.
Outcome:	The proposed framework improves MLLMs' ability to tackle compound and context-rich emotion tasks and the Compound Emotion QA dataset shows it performs well across both benchmarks and evaluation frameworks.

Instant Personalized Large Language Model Adaptation via Hypernetwork (2026.acl-long)

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Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, Meng Jiang

R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling (2021.acl-long)

Challenge:	Existing parameter-efficient fine-tuning methods require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates.
Approach:	They propose a scalable framework that maps a user's profile directly to a full set of adapter parameters.
Outcome:	The proposed framework outperforms prompt-based personalization and OPPU while using substantially fewer computational resources at deployment.

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Xiang Hu, Haitao Mi, Zujie Wen, Yafang Wang, Yi Su, Jing Zheng, Gerard de Melo

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

Challenge:	Existing models with stacked layers do not explicitly model hierarchical structure of language understanding.
Approach:	They propose a recursive Transformer model based on differentiable CKY style binary trees to emulate hierarchical composition process.
Outcome:	The proposed model can predict words given their left and right abstraction nodes.

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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

Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)

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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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation (2024.acl-long)

Challenge:	Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities.
Approach:	They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation.
Outcome:	The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement.

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Shilong Pan, Zhiliang Tian, Liang Ding, Haoqi Zheng, Zhen Huang, Zhihua Wen, Dongsheng Li

Low-Resource NLP

Challenge:	Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data.
Approach:	They propose a method that uses a dynamic graph to organize auxiliary languages in prompts to improve LRL translations.
Outcome:	The proposed method improves translation accuracy in low-resource languages (LRLs) using auxiliary language pairs and synthetic pseudo-parallel data.

Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods (2026.acl-long)

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Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng, Yiyu Wang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Xin Zou, Yuqian Fu, Bin Ren, Linfeng Zhang, Xuming Hu

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

Challenge:	Recent efforts to accelerate inference in Multimodal Large Language Models have focused on visual token compression.
Approach:	They propose a framework that leverages downsampling as a discriminator to denoise existing benchmarks.
Outcome:	The proposed evaluation framework leverages downsampling as a discriminator to denoise existing benchmarks.

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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.

Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)

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Jinwen Chen, Hainan Zhang, Fei Sun, Qinnan Zhang, Sijia Wen, Ziwei Wang, Zhiming Zheng

Robustness in NLP

Challenge:	Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers.
Approach:	They propose to efficiently remove poisoned examples before or during fine-tuning .
Outcome:	The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset.

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

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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 .

Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search (2021.findings-emnlp)

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Shuxian Bi, Chaozhuo Li, Xiao Han, Zheng Liu, Xing Xie, Haizhen Huang, Zengxuan Wen

Information Retrieval

Challenge:	Existing relevance models rely on query-keyword pairs but keywords are usually short texts with scarce semantic information, which may not accurately reflect the underlying advertising purposes.
Approach:	They propose a bidding-graph augmented triple-based relevance model with three towers to deeply fuse the bidding graphs and semantic textual data.
Outcome:	The proposed model outperforms existing models on a large industry dataset and consistently outperformed existing models.

OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)

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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang

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

Challenge:	Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge .
Approach:	They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM.
Outcome:	The proposed model can model human conversation behaviors with low latency and natural interactions with low delay.

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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.

All Information is Valuable: Question Matching over Full Information Transmission Network (2022.findings-naacl)

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Le Qi, Yu Zhang, Qingyu Yin, Guidong Zheng, Wen Junjie, Jinlong Li, Ting Liu

Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)

Challenge:	Existing methods for question matching only transmit one kind of information while failing to utilize both kinds of information simultaneously.
Approach:	They propose a question matching network that can transmit both representation and interactive information together in a simultaneous fashion.
Outcome:	The proposed approach outperforms strong baseline models on two standard benchmarks.

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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang

Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation (2025.findings-acl)

Challenge:	Existing approaches to reduce label noise rely on heuristics and sample losses.
Approach:	They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features.
Outcome:	Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition.

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Tong Zheng, Yan Wen, Huiwen Bao, Junfeng Guo, Heng Huang

Multilinguality

Challenge:	Existing work in LLM-based MMT typically mitigates the Curse of Multilinguality . asymmetric phenomenon in linguistic conflicts and synergy varies in different translation directions .
Approach:	They propose a direction-aware training approach to address asymmetry in linguistic conflicts and synergy . they propose X-ALMA-13B-Pretrain with multilingual pre-training to achieve comparable performance .
Outcome:	The proposed method achieves comparable performance to X-ALMA-13B-Pretrain (only SFT) with fewer pretraining tokens and 17B parameters.

Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)

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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang

Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)

Challenge:	Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments.
Approach:	They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization.
Outcome:	The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings .

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Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang

Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020.findings-emnlp)

Challenge:	Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks.
Approach:	They propose an end-to-end neural model to learn algebraic recombination for compositional generalization.
Outcome:	The proposed model is based on two realistic and comprehensive compositional generalization benchmarks.

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Wen Zheng, Natasa Milic-Frayling, Ke Zhou

Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis (2024.findings-emnlp)

Challenge:	Recent studies have focused on improving dialogue generation models that include knowledge related to the posts.
Approach:	They propose to use a novel method to generate responses from posts and related knowledge by injecting knowledge into dialogue generation models.
Outcome:	The proposed method outperforms baseline models in terms of knowledge relevance and quality.

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Hanfeng Liu, Minping Chen, Zhenya Zheng, Zeyi Wen

Sentiment Analysis

Challenge:	Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of text.
Approach:	They propose a pipeline to predict fine-grained sentiments for specific aspects of text . it decomposes the learning problem into multiple view subproblems and dynamically selects and constructs features with reinforcement learning.
Outcome:	The proposed pipeline surpasses SVM-based methods in predictive accuracy while maintaining a faster inference speed and significantly reducing the number of model parameters.

Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization (2025.emnlp-main)

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Yutao Zhu, Jiajie Jin, Hongjin Qian, Zheng Liu, Zhicheng Dou, Ji-Rong Wen

Information Retrieval

Challenge:	Existing studies have optimized retrieval-augmented generation (RAG) across sub-tasks, but integrating these optimizations into a unified framework remains challenging.
Approach:	They propose a unified retrieval-augmented generation framework that optimizes role-specific tokens for multi-task processing.
Outcome:	The proposed framework achieves efficient multi-task processing through role-specific token optimization.

MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)

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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng

Challenge:	Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data.
Approach:	They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data.
Outcome:	The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard.

Dial BeInfo for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning (2024.findings-emnlp)

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Evgeniia Razumovskaia, Ivan Vulić, Pavle Marković, Tomasz Cichy, Qian Zheng, Tsung-Hsien Wen, Paweł Budzianowski

Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval (2026.findings-acl)

Challenge:	Pretrained large language models (LLMs) are prone to hallucinations, that is, they generate incoherent or generic responses to queries.
Approach:	They propose a method that applies 'behavioural tuning' on the LLMs to aid information-seeking dialogue by comparing three standard datasets.
Outcome:	The proposed method improves accuracy on real-life conversations with real users by allowing the models to perform better on the data.

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Ruiran Yan, Wen Xiong, Ze Liu, Chaozhuo Li, Hao Liao, Defu Lian, Zheng Liu

Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)

Challenge:	Large language models have demonstrated that explicit step-by-step thinking can substantially improve performance on complex tasks.
Approach:	They propose a model that generates preliminary thoughts for input queries before document retrieval.
Outcome:	The proposed model generates preliminary thoughts for input queries before document retrieval.

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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

Challenge:	In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation.
Approach:	They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns.
Outcome:	The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy.

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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang

Structured Data in NLP

Challenge:	Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach:	They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome:	The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)

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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang

Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation (2025.findings-emnlp)

Challenge:	Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning.
Approach:	They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings.
Outcome:	Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks.

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Shiwen Zhang, Lingxiang Wang, Hainan Zhang, Ziwei Wang, Sijia Wen, Zhiming Zheng

Text Generation

Challenge:	Existing retrieval models emphasize surface-level semantic similarity, neglecting deeper solution-level logical similarities.
Approach:	They propose a solution-aware ranking model empowered by synthetic data for competitive programming tasks.
Outcome:	The proposed ranking model outperforms existing retrieval models in precision and recall metrics.

Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022.acl-long)

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Zheng Gong, Kun Zhou, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)

Challenge:	a fundamental challenge in modeling math problems is how to fuse semantics of textual description and formulas.
Approach:	They propose a method to continually pre-train language models for improving understanding of math problems with syntax-aware memory networks.
Outcome:	The proposed approach outperforms competitive baselines on four math tasks.

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Xiaoyong Mei, Tingting Zuo, Da Chen, Guangyu Hu, Xiangyu Wen, Chao Duan, Mingyan Zhang, Fudan Zheng

ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control (2025.acl-long)

Challenge:	Existing methods for multi-role dialogue summarization favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences.
Approach:	They propose a framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization.
Outcome:	The proposed framework matches strong baselines on ROUGE and BERTScore, while in-depth analysis on SAMSum shows clear gains in factual faithfulness and model-based preference alignment.

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Shengpeng Ji, Qian Chen, Wen Wang, Jialong Zuo, Minghui Fang, Ziyue Jiang, Hai Huang, Zehan Wang, Xize Cheng, Siqi Zheng, Zhou Zhao

SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS systems cannot perform speaker-specific voice generation.
Approach:	They propose a style control module that captures codec representations corresponding to timbre, content, and style in a discrete decoupling codec space.
Outcome:	The proposed system can fully clone the speaker's voice and perform speech-specific adjustment and control functions.

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Wen Yin, Cencen Liu, Yi Xu, Ahmad Raza Wahla, Huang Yiting, Dezhang Zheng

A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges (2025.findings-acl)

Challenge:	Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple.
Approach:	They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree.
Outcome:	The proposed method exploits the syntactic knowledge embedded in PLMs and achieves favorable results on three benchmark datasets.

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Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu

ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (2023.findings-emnlp)

Challenge:	This survey provides the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models . integrating large language model with mathematical reasoning tasks is becoming significant as AI advances .
Approach:	They review over 200 studies published since 2021 and examine the state-of-the-art developments in Math-LLMs . they identify five major challenges hindering the realization of AGI in this domain .
Outcome:	The authors examine the state-of-the-art developments in Math-LLMs with a focus on multimodal settings.

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Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Xin Zhao, Ji-Rong Wen

AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation (2023.findings-acl)

Challenge:	Large language models (LLMs) have excellent performance in evaluation benchmarks, but struggle in complex reasoning tasks.
Approach:	They propose a tool-augmented chain-of-thought reasoning framework for chat-based LLMs . they model chain- of-thoughting reasoning as multi-turn conversations to utilize tools .
Outcome:	The proposed framework can outperform state-of-the-art models on complex reasoning tasks.

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Chujie Zheng, Sahand Sabour, Jiaxin Wen, Zheng Zhang, Minlie Huang

DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect (2023.findings-acl)

Challenge:	Crowdsourced dialogue corpora are limited in scale and topic coverage due to the expensive cost of data curation.
Approach:	They construct an augmented dataset for the emotional support conversation task using large language models for dialogue augmentation.
Outcome:	The proposed approach outperforms baselines of dialogue augmentation and improves the model's generalization ability to open-domain topics.

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Jinglin Liu, Zhenhui Ye, Qian Chen, Siqi Zheng, Wen Wang, Zhang Qinglin, Zhou Zhao

Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)

Challenge:	Existing methods for binaural audio synthesis are limited in phase estimation, which is crucial for spatial hearing.
Approach:	They propose a method to explicitly address the Doppler effect of the moving speaker . it calculates the radial relative velocity of the speaker in spherical coordinates .
Outcome:	The proposed method improves the representative WarpNet and BinauralGrad backbones in phase error metric and reaches a new state of the art (SOTA) it is compared with the current method which is limited in phase estimation .

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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen

LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

Challenge:	Recent efforts to integrate large language models into English education lack adaptability to language learning.
Approach:	They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks .
Outcome:	The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education.

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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen

Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)

Challenge:	a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach:	They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome:	The proposed library is based on extensive experiments in a variety of evaluation settings.

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Jiachen Zhu, Congmin Zheng, Jianghao Lin, Kounianhua Du, Ying Wen, Yong Yu, Jun Wang, Weinan Zhang

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)

Challenge:	Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues.
Approach:	They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup .
Outcome:	The proposed framework outperforms baseline models on multiple real-world datasets.

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Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

Challenge:	Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies.
Approach:	They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature .
Outcome:	The proposed framework outperforms existing systems at long and short answer criteria.

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

Low-Resource NLP

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

Improving Adversarial Text Generation by Modeling the Distant Future (2020.acl-main)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin

Robustness in NLP

Challenge:	Recent work has shown excellent performance on text generation tasks by combining reinforcement learning (RL) and generative models.
Approach:	They propose a model-based imitation-learning approach to improve text generation performance by focusing on a long horizon.
Outcome:	The proposed model improves on a number of text-generation tasks and provides intermediate rewards for generator optimization.

ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)

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Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen

Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning (2021.emnlp-main)

Challenge:	Recent years have featured a trend towards Transformer based pretrained language models (PLMs) in natural language processing systems.
Approach:	They propose to use four evaluation dimensions to evaluate ten widely-used PLMs . they find that pretrained language models are good at different ability tests .
Outcome:	The results show that pretrained language models are good at different ability tests and have excellent transferability between tasks.

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Weijiang Yu, Yingpeng Wen, Fudan Zheng, Nong Xiao

Knowledge-Grounded Dialogue Generation with Term-level De-noising (2021.findings-acl)

Challenge:	Existing algorithms for math word problems only capture word-level relationship and ignore to build hierarchical reasoning like the human being.
Approach:	They propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure network that uses outside knowledge to build hierarchical reasoning like the human being.
Outcome:	The proposed method outperforms state-of-the-art methods on two large-scale datasets and boosts performance.

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Wen Zheng, Natasa Milic-Frayling, Ke Zhou