Papers by Lan Zhang

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)

Challenge:	Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem.
Approach:	They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario.
Outcome:	The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets.

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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data.
Approach:	They propose a framework that imposes strong typing constraints and incorporates key relationships from schema.
Outcome:	The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider.

Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications (2026.findings-acl)

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Fangping Lan, Qi Zhang, Eduard Dragut

Challenge:	Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content.
Approach:	They synthesize text revision research through the lens of edit intentions . they review prior work across the revision workflow including corpus construction, edit intention taxonomies, edit intentions, and edit intention identification.
Outcome:	The proposed approach synthesizes datasets, taxonomies, identification methods, and applications and highlights key open research directions.

How Do LLMs "Trust" Unknown Knowledge? An Unknown Knowledge Based Jailbreak Attack (2026.findings-acl)

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Yixiao Huang, Lan Zhang, Chaoran Wang

Robustness in NLP

Challenge:	Existing research on how to effectively utilize unknown knowledge has focused on how it can be used to enhance LLMs' performance in specialized fields.
Approach:	They propose a completely unrestricted and fully randomized jailbreak attack that embeds malicious queries within trust-enhanced unknown knowledge.
Outcome:	The proposed method achieves 99% to 100% ASR on all tested LLMs, including the latest GPT-5.1, and becomes SOTA.

Generative Gamer: Learning Equilibrium Strategy by LLM-driven Dynamic Deduction (2026.acl-long)

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Yadong Zhang, Xinshu Shen, Yupei Ren, Shangqing Zhao, Man Lan

Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)

Challenge:	Large Language Models (LLMs) falter in domains requiring deep strategic reasoning.
Approach:	They propose a framework that trains LLMs to reason like an expert player . they propose action pruning based on policy confidence, state pruning via value estimation and branch pruning inspired by alpha-beta principles to train the model effectively.
Outcome:	Experiments on Tic-Tac-Toe and Leduc Poker show that GenGamer significantly improves the strategic capabilities of large language models.

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Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew Lan

Reasoning

Challenge:	Existing approaches to solving math word problems focus on obtaining the correct answer.
Approach:	They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps.
Outcome:	The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation.

FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean (2026.acl-long)

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Jordan Meadows, Lan Zhang, Andre Freitas

Programming Languages in NLP

Challenge:	Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models.
Approach:	They propose a domain-agnostic human-in-the-loop agentic pipeline to aid autoformalisation in scientific domains.
Outcome:	The proposed system produces syntactically correct and semantically aligned proofs for low cost.

Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)

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Qi Zhang, Fangping Lan, Cornelia Caragea, Longin Jan Latecki, Eduard Dragut

Low-Resource NLP

Challenge:	Existing studies on ICL for Named Entity Recognition (NER) have mainly explored few-shot settings, but the potential of scaling to hundreds of demonstrations has not been thoroughly investigated.
Approach:	They evaluate various LLMs across multiple domains using hundreds of ICL examples and then assess the feasibility of using many-shot ICL as a data annotation framework.
Outcome:	The proposed framework can be scaled to hundreds of examples and annotate and refining data for low-resource NER tasks.

Consistent Autoformalization for Constructing Mathematical Libraries (2024.emnlp-main)

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Lan Zhang, Xin Quan, Andre Freitas

Syntactic Text Processing

Challenge:	Autoformalization is the task of automatically translating mathematical content written in natural language to a formal language expression.
Approach:	They propose to use three mechanisms to improve autoformalization quality . they propose to combine most-similar retrieval augmented generation, denoising steps and auto-correction with syntax error feedback to improve syntactic, terminological and semantic control.
Outcome:	The proposed mechanisms can deliver syntactically, terminologically and semantically more consistent results across different models.

Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)

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Xinshu Shen, Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai

Syntactic Text Processing

Challenge:	Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors.
Approach:	They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability.
Outcome:	The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections.

OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization (2025.acl-long)

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Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Hongming Zhang, Tianqing Fang, Zhenzhong Lan, Dong Yu

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)

Challenge:	Existing studies focus on building text-only agents in synthetic environments where the reward signals are clearly defined.
Approach:	They propose a multimodal web agent that can autonomously conduct real-world exploration and improve itself after each iteration.
Outcome:	The proposed agent improves itself after each iteration, demonstrating strong performance across multiple test sets.

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Zhenwen Liang, Jipeng Zhang, Lei Wang, Wei Qin, Yunshi Lan, Jie Shao, Xiangliang Zhang

Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)

Challenge:	Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning.
Approach:	They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma.
Outcome:	The proposed model can solve MWP problems on English and Chinese benchmarks.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

Text Generation

Challenge:	Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build.
Approach:	They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.
Outcome:	The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition (2021.naacl-main)

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Haolan Zhan, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Yongjun Bao, Yanyan Lan

Tailored Sequence to Sequence Models to Different Conversation Scenarios (P18-1)

Challenge:	Existing knowledge-grounded dialogue models lack fine-grained control over knowledge selection and integration with dialogues.
Approach:	They propose to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags.
Outcome:	The proposed model outperforms baseline models on knowledge-grounded dialogue benchmarks.

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Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xueqi Cheng

Understanding Client Reactions in Online Mental Health Counseling (2023.acl-long)

Challenge:	Sequence to sequence (Seq2Sequeq) models fail to meet the diverse requirements for different conversation scenarios, such as customer service and chatbot.
Approach:	They propose two optimized criteria for Sequence to sequence (Seq2Sequeq) to meet different conversation scenarios, i.e., maximum generated likelihood for specific-requirement scenario, and conditional value-at-risk for diverse-requrement scenarios.
Outcome:	The proposed models satisfies diverse requirements for different conversation scenarios and yields better performances than existing models.

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Anqi Li, Lizhi Ma, Yaling Mei, Hongliang He, Shuai Zhang, Huachuan Qiu, Zhenzhong Lan

VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)

Challenge:	Communication success relies heavily on reading participants’ reactions, but little research is on how listeners' reactions shape trajectories and outcomes of conversations.
Approach:	They propose to use client reactions to predict counseling outcomes by using an annotation framework that encompasses counselors’ strategies and client reaction behaviors.
Outcome:	The proposed framework can predict counselors' strategies and client reaction behaviors against a large-scale text-based counseling dataset.

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Xiaoman Wang, Dan Yuan, Xin Liu, Yike Zhao, Xiaoxiao Zhang, Xizhi Chen, Yunshi Lan

Visual Data in NLP

Challenge:	Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios.
Approach:	They propose to benchmark Chinese grammatical error correction for Chinese as a foreign language learner (CFL) using a dataset, they propose to use two CGEC frameworks to conduct experiments .
Outcome:	The proposed approach achieves an F 0.5 score of only 28.9%.

WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models (2024.acl-long)

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Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, Dong Yu

ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)

Challenge:	Existing web agents only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios.
Approach:	They propose a large multimodal model-powered web agent that can complete user instructions end-to-end by interacting with real-world websites.
Outcome:	The proposed agent achieves 59.1% task success rate, surpassing both GPT-4 and WebVoyager setups.

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Hainan Zhang, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)

Challenge:	Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process.
Approach:	They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context.
Outcome:	The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations.

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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth

On the Effect of Isotropy on VAE Representations of Text (2022.acl-short)

Challenge:	Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains.
Approach:	They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control .
Outcome:	The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control.

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Lan Zhang, Wray Buntine, Ehsan Shareghi

Representation Learning

Challenge:	Injecting desired geometric properties into text representations has attracted a lot of attention due to its better utilisation of representation space.
Approach:	They propose to use an isotropic Gaussian posterior instead of the ellipsoidal Gausssian priori to inject isotropy into text representations.
Outcome:	The proposed method improves classification performance, robustness to input perturbation, and generative behavior compared to the ellipsoidal Gaussian posterior.

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)

Challenge:	Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
Approach:	They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories.
Outcome:	The proposed system integrates LLM agents with graph database interfaces extracted from code repositories.

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Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

Challenge:	Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective.
Approach:	They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space .
Outcome:	The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches.

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Xu Wang, Hainan Zhang, Shuai Zhao, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Bo Cheng, Yanyan Lan

Natural Language Interfaces

Challenge:	Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response.
Approach:	They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation.
Outcome:	The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets.

You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives (2025.coling-main)

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Junyi Chen, Leyuan Liu, Tian Lan, Fan Zhou, Xiaosong Zhang

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

Challenge:	Existing rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Existing research mainly combats this with content and response-based detection methods.
Approach:	They propose a Large Language Model with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses.
Outcome:	The proposed approach can indicate the model’s uncertainty (i.e., reliability) of the results.

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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations (2023.findings-acl)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

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Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Yang Wang, Zhiheng Huang

Adaptive Bridge between Training and Inference for Dialogue Generation (2021.emnlp-main)

Challenge:	Existing methods for multitask learning typically use a dataset name as input prefix, which limits the effectiveness of multitask training.
Approach:	They propose compositional task configurations, a set of prompts prepended to the encoder to improve cross-task generalization of unified models.
Outcome:	The proposed model outperforms the UnifiedSKG baseline by noticeable margins in both in-domain and zero-shot settings.

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Haoran Xu, Hainan Zhang, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Yanyan Lan

Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation (2023.acl-long)

Challenge:	Experimental results show that our model can achieve a significant improvement in terms of metric-based evaluation and human evaluation compared with the state-of-the-art exposure bias approaches.
Approach:	They propose a novel adaptive switching mechanism which automatically transits between ground-truth learning and generated learning regarding the word-level matching score.
Outcome:	The proposed model improves on Chinese and English reddit datasets compared with state-of-the-art models on the word-level matching score.

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Hongyi Wu, Hao Zhou, Man Lan, Yuanbin Wu, Yadong Zhang

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback (2025.findings-acl)

Challenge:	Existing methods for implicit discourse relation recognition (IDRR) lack connectives, which is a major challenge in discourse analysis research.
Approach:	They propose a method to predict latent correlations between connectives and discourse relations using a knowledge distillation approach.
Outcome:	The proposed method outperforms state-of-the-art models on coarse-grained and fine-grain discourse relations and can be transferred to explicit discourse relation recognition and achieve acceptable performance.

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Thai Quoc Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles

Challenge:	Large Action Models (LAMs) face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback.
Approach:	They propose a framework for online exploration of agentic tasks with high-quality feedback . they use a dynamic task query generator and an extensive collection of tools to create a high-level feedback environment for LLM Agents.
Outcome:	The proposed framework achieves 49.3% performance improvement over baselines on toolbench and CRMArena.

Learning to Control the Specificity in Neural Response Generation (P18-1)

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Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, Xueqi Cheng

Text Generation

Challenge:	Existing generative conversational models tend to favor general and trivial responses which appear frequently.
Approach:	They propose a controlled response generation mechanism to handle different utterance-response relationships in terms of specificity.
Outcome:	The proposed model outperforms state-of-the-art models under automatic and human evaluations.

History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling (2023.acl-long)

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Hao Sun, Yang Li, Liwei Deng, Bowen Li, Binyuan Hui, Binhua Li, Yunshi Lan, Yan Zhang, Yongbin Li

Natural Language Interfaces

Challenge:	Existing methods for conversational KBQA assume the independence of utterances and model them in isolation.
Approach:	They propose a History Semantic Graph Enhanced KBQA model that models long-range semantic dependencies in conversation history while maintaining low computational cost.
Outcome:	The proposed model outperforms baselines on a widely used question type dataset.

V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang

RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering (2024.emnlp-main)

Challenge:	Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach:	They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome:	The proposed framework provides quantitative analyses and superior deciphering capability.

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Rujun Han, Yuhao Zhang, Peng Qi, Yumo Xu, Jenyuan Wang, Lan Liu, William Yang Wang, Bonan Min, Vittorio Castelli

Robustness in NLP

Challenge:	Existing datasets for question answering based on retrieval augmented generation (RAG-QA) are either constructed using a single source corpus or consist of short extractive answers, which fall short of evaluating large language model (LLM) based RAG-QA systems on cross-domain generalization.
Approach:	They propose a dataset that integrates short extractive answers from multiple documents into a single coherent narrative.
Outcome:	The proposed dataset integrates short extractive answers from multiple documents into a single coherent narrative, covering 26K queries and large corpora across seven different domains.

Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)

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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen

Multi-Operational Mathematical Derivations in Latent Space (2024.naacl-long)

Challenge:	Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer.
Approach:	They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs).
Outcome:	The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation.

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Marco Valentino, Jordan Meadows, Lan Zhang, Andre Freitas

Numerical Reasoning

Challenge:	Using a symbolic engine, we investigate the possibility of approximating multiple mathematical operations in latent space for expression derivation.
Approach:	They propose to model mathematical operations as explicit geometric transformations by leveraging a symbolic engine and a large-scale dataset.
Outcome:	The proposed paradigms can be used to approximate multiple mathematical operations in latent space, while discriminating the conclusions for a single operation is achievable in the original expression encoder.

Learning to Select In-Context Demonstration Preferred by Large Language Model (2025.findings-acl)

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Zheng Zhang, Shaocheng Lan, Lei Song, Jiang Bian, Yexin Li, Kan Ren

Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)

Challenge:	In-context learning (ICL) enables large language models to perform tasks with only a few examples as demonstrations.
Approach:	They propose a generative preference learning framework that leverages LLM feedback to directly optimize demonstration selection for ICL.
Outcome:	Experiments on 19 datasets across 11 task categories show that GenICL achieves superior performance than existing methods in selecting the most effective demonstrations.

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Chen Wang, Lai Wei, Yanzhi Zhang, Chenyang Shao, Zedong Dan, Weiran Huang, Ge Lan, Yue Wang

Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time (2025.emnlp-main)

Challenge:	Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability.
Approach:	They propose a framework that provides targeted mechanisms for exploration and stabilization.
Outcome:	The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably.

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Yifan Lan, Yuanpu Cao, Weitong Zhang, Lu Lin, Jinghui Chen

SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection (2025.acl-long)

Challenge:	Recent advances in Multimodal Large Language Models have raised serious safety concerns.
Approach:	They propose a method for manipulating the output preference of MLLMs using a preference hijacked image.
Outcome:	The proposed method works at inference time and requires no model modifications.

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Yi-Fan Lu, Xian-Ling Mao, Tian Lan, Tong Zhang, Yu-Shi Zhu, Heyan Huang

Information Extraction & Text Mining

Challenge:	Existing evaluation methods for Open Domain Event Detection (ODED) lack representative representations of the real world, making it difficult to accurately reflect performance of various ODED methods in real-world scenarios.
Approach:	They propose a scalable and reliable Semantic-level Evaluation framework for Open domain event detection by constructing a more representative evaluation benchmark and introducing a semantic evaluation metric.
Outcome:	The proposed framework first constructs a more representative evaluation benchmark that currently includes 564 event types covering 7 major domains, with a cost-effective supplementary annotation strategy to ensure the benchmark’s representativeness.

Contrastive Learning of Sentence Embeddings from Scratch (2023.emnlp-main)

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Junlei Zhang, Zhenzhong Lan, Junxian He

Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)

Challenge:	Existing approaches to learn sentence embeddings with unlabeled data are limited due to copyright restrictions, data distribution issues, and messy formats.
Approach:	They propose a contrastive learning framework that trains sentence embeddings with synthetic data.
Outcome:	The proposed framework produces positive and negative annotations given unlabeled sentences and generates sentences along with their corresponding annotations from scratch.

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Chiyu Song, Zhanchao Zhou, Jianhao Yan, Yuejiao Fei, Zhenzhong Lan, Yue Zhang

Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)

Challenge:	Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models.
Approach:	They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs.
Outcome:	The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities.

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Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, Min Zhang, Jinsong Su

Multilinguality

Challenge:	In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language.
Approach:	They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages .
Outcome:	The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate.

Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music (2026.acl-long)

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Hongju Su, Ke Li, Lan Yang, Honggang Zhang, Yi-Zhe Song

Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)

Challenge:	Existing symbolic music generation models represent musical notes as a sequence of attribute tokens with fixed unidirectional dependencies.
Approach:	They propose a symbolic music generation framework that adopts a autoregressive and a discrete diffusion architectures for note attributes.
Outcome:	The proposed framework improves state-of-the-art models across objective and subjective metrics.

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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen

Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)

Challenge:	Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate.
Approach:	They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation.
Outcome:	The proposed method outperforms the state-of-the-art by 1.20% on four public datasets.

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Zhibin Lan, Jiawei Yu, Xiang Li, Wen Zhang, Jian Luan, Bin Wang, Degen Huang, Jinsong Su

K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning (2025.naacl-long)

Challenge:	Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition.
Approach:	They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation.
Outcome:	The proposed model can associate the image with relevant texts, providing useful supplementary information for translation.

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Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Yan Xia, Man Lan, Furu Wei

Autoformalization in the Wild: Assessing LLMs on Real-World Mathematical Definitions (2025.emnlp-main)

Challenge:	Strategic reasoning requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments.
Approach:	They propose a framework that enables Large Language Models to achieve varying levels of strategic depth by recursive mechanisms that allow agents to form higher order beliefs about others' beliefs.
Outcome:	The proposed framework enables LLMs to achieve varying levels of strategic depth, allowing agents to form higher order beliefs—beliefs about others’ beliefs.

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Lan Zhang, Marco Valentino, Andre Freitas

Numerical Reasoning

Challenge:	Large Language Models (LLMs) have demonstrated remarkable potential in assisting with mathematical reasoning on different downstream tasks.
Approach:	They propose two new tools for autoformalizing real-world mathematical definitions from Wikipedia and arXiv papers.
Outcome:	The proposed methods improve definitions by up to 16% and undefined errors by 43%.

Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks (2023.findings-emnlp)

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Kaiser Sun, Peng Qi, Yuhao Zhang, Lan Liu, William Wang, Zhiheng Huang

Question Answering

Challenge:	Pretrained sequence-to-sequence (seq2sequ) models have been widely used to solve extractive tasks, where parts of the input are extracted to form the desired output.
Approach:	They propose a simple fix to tokenization inconsistency that damages extractive nature of generative models by causing performance drop and hallucination.
Outcome:	The proposed model performs better in both in-domain and out-of-domain datasets with a notable average of +1.7 F1 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets.

Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs (2024.findings-emnlp)

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Anqi Li, Yu Lu, Nirui Song, Shuai Zhang, Lizhi Ma, Zhenzhong Lan

Challenge:	In traditional face-to-face therapy, the assessment of therapeutic alliance is not directly translated to text-based settings.
Approach:	They propose an automatic approach to understand the development of therapeutic alliance in text-based counseling by using large language models.
Outcome:	The proposed approach demonstrates that the framework is effective in identifying the therapeutic alliance in text-based counseling.

RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service (2025.findings-acl)

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Yihang Cheng, Lan Zhang, Junyang Wang, Mu Yuan, Yunhao Yao

MASA: LLM-Driven Multi-Agent Systems for Autoformalization (2025.emnlp-demos)

Challenge:	Large language models (LLMs) have a tendency to generate factually incorrect or purely fictional responses, a phenomenon known as hallucination.
Approach:	They propose to use remote RAG to protect user query from privacy leakage . they introduce (n,)-DistanceDP to characterize privacy leakages of user query .
Outcome:	The proposed solution can resist embedding inversion attacks while achieving no loss in retrieval under various settings.

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Lan Zhang, Marco Valentino, Andre Freitas

Reasoning

Challenge:	This paper presents a framework for building multi-agent systems for autoformalization driven by Large Language Models.
Approach:	They propose a framework for building multi-agent systems for autoformalization driven by Large Language Models.
Outcome:	The proposed framework leverages collaborative agents to convert natural language statements into formal representations.

xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)

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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

ToMELP: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model (2026.findings-acl)

Challenge:	Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks.
Approach:	They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance.
Outcome:	The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks.

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Ruirui Wang, Haoran Zhang, Tian Lan, Zehua Duo, Jiang Li, Guanglai Gao, Xiangdong Su

Linguistics & Cognitive NLP

Challenge:	Theory of Mind (ToM) is widely regarded as central to effective persuasion, yet existing evaluations fail to capture the infer–apply loop that arises in real-world dialogue.
Approach:	They propose a benchmark that conditions on the audience persona p and the Elaboration Likelihood Model (ELM) route r within persuasive conversations.
Outcome:	The proposed model can model the interlocutor's mental states over multiple turns and adapt strategy and tone accordingly.

From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)

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Yue Fang, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Bo Long, Yanyan Lan, Yanquan Zhou

Text Generation

Challenge:	Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem.
Approach:	They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content .
Outcome:	The proposed model outperforms baseline models on both SAMSum and DialSum datasets.

StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)

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Xiaoming Liu, Chen Liu, Zhaohan Zhang, Chengzhengxu Li, Longtian Wang, Yu Lan, Chao Shen

KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions (2026.acl-long)

Challenge:	Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting.
Approach:	They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization.
Outcome:	Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average.

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Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, Shuo Zhang, Sen Hu, Silin Wu, Qizhen Lan, Huacan Wang, Ronghao Chen

Information Retrieval

Challenge:	Existing long-horizon memory benchmarks use multi-turn dialogues or synthetic user histories . despite rapid progress on long-term memory evaluation, there are gaps in existing benchmarks .
Approach:	They propose a long-form autobiographical narrative benchmark that reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions.
Outcome:	The proposed benchmarks build from long-form autobiographical narratives . they show that retrieval-augmented systems improve factual accuracy while errors persist on temporally grounded explanations and higher-level inferences.

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan

SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support (2024.findings-emnlp)

Challenge:	Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback.
Approach:	They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations.
Outcome:	The proposed method significantly improves both automatic and human evaluations across four diverse LLMs.

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Huachuan Qiu, Hongliang He, Shuai Zhang, Anqi Li, Zhenzhong Lan