Papers by He Cheng

Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)

Challenge:	Simile interpretation is a crucial task in natural language processing.
Approach:	They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions.
Outcome:	The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans.

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Zehan Li, Fu Zhang, Tianyue Peng, He Liu, Jingwei Cheng

CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)

Challenge:	Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data.
Approach:	They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters.
Outcome:	The proposed model demonstrates comparable performance on multiple benchmarks.

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Fu Zhang, He Liu, Zehan Li, Jingwei Cheng

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Existing methods to predict relationships with given entity pairs are lacking in supervised methods.
Approach:	They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation.
Outcome:	The proposed framework shows competitive performance on two ZSRE datasets.

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

Multilinguality

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)

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Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si

On the Universal Truthfulness Hyperplane Inside LLMs (2024.emnlp-main)

Challenge:	Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning.
Approach:	They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task.
Outcome:	The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings.

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Junteng Liu, Shiqi Chen, Yu Cheng, Junxian He

RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)

Challenge:	Recent studies have explored hallucinations through the lens of internal representations, proposing mechanisms to decipher LLMs’ adherence to facts.
Approach:	They propose to train a universal truthfulness hyperplane that distinguishes the model’s factually correct and incorrect outputs on a diverse collection of over 40 datasets and examine its cross-task, cross-domain, and in-domain generalization.
Outcome:	The proposed model is able to distinguish factual outputs from incorrect outputs on a diverse collection of over 40 datasets.

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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning (2026.findings-acl)

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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Qi He, Cheng Qian, Xiusi Chen, Bingxiang He, Yi R. Fung, Heng Ji

RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to online claim verification rely on prompt engineering or pre-designed reasoning workflows.
Approach:	They propose an online reinforcement learning framework that enables an LLM to interact with a search engine and receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors.
Outcome:	Empirical results show that Veri-R1 improves joint accuracy by 30% and doubles evidence score, often surpassing larger-scale model counterparts.

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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)

Challenge:	Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair.
Approach:	They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks.
Outcome:	The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks.

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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng

The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020.acl-demos)

Challenge:	Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts.
Approach:	They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions.
Outcome:	The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field.

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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao

Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)

Challenge:	MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models .
Approach:	They present MT-DNN, an open-source natural language understanding toolkit . it is designed to facilitate rapid customization for a broad spectrum of NLU tasks . MT supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop.
Outcome:	The proposed model can significantly compress a large model without significant performance drop.

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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)

Challenge:	Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
Approach:	They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction.
Outcome:	The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency.

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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin

Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)

Challenge:	Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0.
Approach:	They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes.
Outcome:	The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation.

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Keqing He, Jinchao Zhang, Yuanmeng Yan, Weiran Xu, Cheng Niu, Jie Zhou

BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)

Challenge:	Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness.
Approach:	They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations .
Outcome:	The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings.

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Yuhang He, Jihai Zhang, Jianzhu Bao, Fangquan Lin, Cheng Yang, Bing Qin, Ruifeng Xu, Wotao Yin

Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)

Challenge:	Existing methods for interactive theorem proving in formal logic lack robustness and robustness.
Approach:	They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps.
Outcome:	The proposed framework improves on the miniF2F benchmark.

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Jiahao Cheng, Tiancheng Su, Jia Yuan, Guoxiu He, Jiawei Liu, Xinqi Tao, Jingwen Xie, Huaxia Li

MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models (2026.acl-long)

Challenge:	Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored.
Approach:	They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods.
Outcome:	The proposed method significantly affects the internal states and token probability distributions of the LLM.

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Chenyang Gu, Jiahao Cheng, Meicong Zhang, Pujun Zheng, Jinquan Zheng, Guoxiu He

Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt (2024.emnlp-industry)

Challenge:	Existing LLMs emulate human research workflows but lack scientific grounding . empirical results show that MoRI outperforms strong commercial LLM models .
Approach:	They propose a framework that explicitly learns scientific reasoning from research motivations to methodologies.
Outcome:	The proposed framework outperforms commercial LLMs and agentic baselines in novelty, technical rigor, and feasibility.

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Peter Anderson, Mano Vikash Janardhanan, Jason He, Wei Cheng, Charlie Flanagan

Representation Learning

Challenge:	Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings.
Approach:	They propose to fine tune financial text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs including both public and proprietary financial documents.
Outcome:	The proposed embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embeddING from OpenAI.

EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)

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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji

TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)

Challenge:	Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments.
Approach:	They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals.
Outcome:	The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels.

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Shiqiang Tian, Cheng Ding, Qin Chen, Jie Zhou, Liang He

Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)

Challenge:	Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting.
Approach:	They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory.
Outcome:	Experiments show that TamEdit outperforms baselines in continual editing while preserving general capabilities.

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu

Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026.acl-industry)

Challenge:	Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models .
Approach:	They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision.
Outcome:	The proposed framework improves LLM reasoning without supervision without external supervision.

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Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen

Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	a production-grade pricing system for tourism is challenging due to unstructured nature of travel orders and ever-evolving pricing policies.
Approach:	They propose a production-grade pricing system with a strict decision boundary . they propose to combine structured extraction and bounded policy/path selection with interpretable condition trees .
Outcome:	The proposed system processed 3,960 orders in six months and reduced the order management team from 15-20 to 3 . the system reduced the per-order handling time from 10 minutes to 2 minutes.

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Zhenxiao Cheng, Jie Zhou, Wen Wu, Qin Chen, Liang He

Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)

Challenge:	Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) however, in the context of Aspect-based Sentiment Analysis, only specific dimensions are pertinent.
Approach:	They propose a Gradient-based explanation framework that leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information.
Outcome:	The proposed framework improves both the models’ performance and explanations’ clarity by identifying sentiment-aware features.

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Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

Challenge:	Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions.
Approach:	They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts .
Outcome:	The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics .

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

Information Extraction & Text Mining

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)

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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

Challenge:	Existing typography solutions lack adaptability, creativity, and computational efficiency.
Approach:	They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs.
Outcome:	The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO.

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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou

Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Approach:	They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework .
Outcome:	The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design .

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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi

Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)

Challenge:	Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable.
Approach:	This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning .
Outcome:	The proposed study examines the safety and security risks of large reasoning models.

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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian

Empowering Reliable Visual-Centric Instruction Following in MLLMs (2026.findings-acl)

Challenge:	Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics.
Approach:	They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities.
Outcome:	The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly.

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Weilei He, Feng Ju, Zhiyuan Fan, Rui Min, Minhao Cheng

The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)

Challenge:	Existing benchmarks for evaluating instruction-following capabilities focus on verbal instructions in the textual modality.
Approach:	They propose to incorporate vision-dependent constraints into instruction design to enable a more rigorous assessment of how well MLLMs align their outputs with both visual input and textual instructions.
Outcome:	The proposed benchmark incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions.

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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun

Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)

Challenge:	Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations.
Approach:	They propose a training data arrangement framework that allows for continual learning and loss reduction.
Outcome:	The proposed framework promotes continual learning and loss reduction on unseen tasks.

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Shan He, Runze Wang, Zhuoyun Du, Huiyu Bai, Zouying Cao, Yu Cheng, Bo Zheng

Structured Data in NLP

Challenge:	Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience.
Approach:	They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications .
Outcome:	a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance .

Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering (2025.findings-emnlp)

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Bolei He, Xinran He, Run Shao, Shanfu Shu, Xianwei Xue, MingQuan Cheng, Haifeng Li, Zhen-Hua Ling

Knowledge Representation

Challenge:	Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios.
Approach:	They propose a framework that internalizes domain knowledge through internal-external knowledge self-selection and selective supervised fine-tuning.
Outcome:	The proposed framework outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)

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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu

UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021.acl-long)

Challenge:	Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability.
Approach:	They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks.
Outcome:	The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity.

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Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao

Natural Language Interfaces

Challenge:	Recent work on open-domain question answering focuses on either extractive or generative readers exclusively.
Approach:	They propose a hybrid approach to extractive and generative readers that leverages both models.
Outcome:	The proposed approach outperforms state-of-the-art models on NaturalQuestions and TriviaQA respectively.

Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024.acl-long)

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Ming Li, Yong Zhang, Shwai He, Zhitao Li, Hongyu Zhao, Jianzong Wang, Ning Cheng, Tianyi Zhou

RD-MCSA: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations (2025.emnlp-main)

Challenge:	Earlier studies of instruction tuning on Large Language Models focus on creating large, varied, and high-quality datasets with responses curated by human experts.
Approach:	They propose to use a smaller and weaker model to fine tune a larger and stronger model . they find it can largely speed up the data filtering and improve performance .
Outcome:	The proposed model can filter instruction data faster and better on benchmarks.

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Haihua Xie, Yinzhu Cheng, Yaqing Wang, Miao He, Mingming Sun

Information Retrieval

Challenge:	Existing methods for multi-class sentiment analysis (MCSA) are difficult due to subtle semantic differences between adjacent sentiment levels and the scarcity of high-quality annotated data.
Approach:	They propose a framework to integrate classification rationales with adaptively selected demonstrations to enhance MCSA performance under limited supervision.
Outcome:	The proposed framework outperforms baseline and standard ICL methods on five benchmark datasets.

LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)

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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao

CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents (2026.acl-long)

Challenge:	Existing systems struggle to balance efficiency, scalability, and interpretability.
Approach:	They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs.
Outcome:	The proposed framework scales to billion-edge graphs without loss of retrieval fidelity.

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Jiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong, Shijue Huang, Bingxiang He, Yi R. Fung

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)

Challenge:	Existing evaluations of Large Language Models (LLMs) focus on task completion, but neglect a crucial capability: the ability to devise and adjust cost-optimal plans in response to changing environments.
Approach:	They propose a scalable, cost-centric benchmark to evaluate agents’ economic reasoning and replanning abilities.
Outcome:	Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning .

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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan

GraDaSE: Graph-Based Dataset Search with Examples (2025.emnlp-main)

Challenge:	Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities.
Approach:	They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever.
Outcome:	The proposed method surpasses the previous SOTA.

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Jing He, Mingyang Lv, Qing Shi, Gong Cheng

Information Retrieval

Challenge:	Existing methods address ad hoc dataset search, but dataset search presents in diverse and complex forms.
Approach:	They propose a graph-based approach to retrieve relevant datasets from textual queries . they identify provenance-based and topic-based relationships to construct a diagram .
Outcome:	The proposed approach outperforms strong baselines on two test collections.

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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang

Paraphrasing

Challenge:	Copyright Detective is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs.
Approach:	They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing .
Outcome:	The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs.

Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)

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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

Challenge:	Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states.
Approach:	They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM.
Outcome:	The proposed framework outperforms strong baselines in performance and efficiency.

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao

HookMoE: A learnable performance compensation strategy of Mixture-of-Experts for LLM inference acceleration (2025.emnlp-main)

Challenge:	Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems.
Approach:	They propose a hierarchical benchmark to evaluate large language models on engineering problems.
Outcome:	The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields.

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Cheng Longkai, Along He, Mulin Li, Xie Xueshuo, Tao Li

Green & Sustainable NLP

Challenge:	Mixture of Experts (MoE) models have been a promising paradigm for scaling model capacity through top-k routing mechanisms.
Approach:	They propose a plug-and-play single-layer compensation framework that strategically inserts a lightweight trainable Hook module immediately preceding selected transformer blocks.
Outcome:	The proposed framework reduces the number of activated experts by more than 50% and achieves a 1.42 inference speed-up during the prefill stage.

Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR (2026.acl-long)

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Haobo Xu, Sirui Chen, Ruizhong Qiu, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, Hanghang Tong

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) has improved reasoning capabilities of Large Language Models (LLMs).
Approach:	They propose an online pruning method that prunes rollouts while steering correct ones to enhance learning signals.
Outcome:	The proposed method improves average accuracy by +2.30 to +2.99 across GRPO and DAPO on Qwen-3 and LLaMA-3.2 models.

Mitigating Safety Context Amnesia in Multimodal Reasoning Models via Intent-Guided Safety Reasoning (2026.acl-long)

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Xiyao Dong, Guangsheng Cheng, YiLong Chen, Xiaojin Zhang, Kun He

From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs (2025.acl-long)

Challenge:	Recent advances in Multimodal Large Reasoning Models (MLRMs) have enabled explicit chain-of-thought inference across vision and language, improving performance on complex cognitive tasks.
Approach:	They propose an inference-time defense that uses a percept decoupler to extract objective visual evidence into a structured intent output and a cognitive arbiter to enforce explicit safety constraints prior to generation.
Outcome:	The proposed defense improves defense success rates by over 62% compared to baselines while preserving task utility.

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Jialun Cao, Yaojie Lu, Meiziniu Li, Haoyang Ma, Haokun Li, Mengda He, Cheng Wen, Le Sun, Hongyu Zhang, Shengchao Qin, Shing-Chi Cheung, Cong Tian

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)

Challenge:	Recent studies in formal mathematical reasoning have shown an unstoppable growth trend.
Approach:	They constructed 18k high-quality instruction-response pairs across five mainstream formal specification languages and evaluated them against ten open-sourced LLMs.
Outcome:	The proposed model compared instruction-response pairs across five formal specification languages and found that the LLMs were good at writing proof segments when given either the code, or the detailed description of proof steps.

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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin

Programming Languages in NLP

Challenge:	Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model.
Approach:	They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model.
Outcome:	The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively.

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)

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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao

LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training (2024.emnlp-main)

Challenge:	Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech.
Approach:	They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness.
Outcome:	The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines .

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Tong Zhu, Xiaoye Qu, Daize Dong, Jiacheng Ruan, Jingqi Tong, Conghui He, Yu Cheng

Retrieval-Augmented Generation for Large Language Model based Few-shot Chinese Spell Checking (2025.coling-main)

Challenge:	Mixture-of-Experts (MoE) has gained increasing popularity as a framework for scaling up large language models.
Approach:	They investigate how to build Mixture-of-Experts (MoE) models from existing large language models . they use expert construction, Continual pre-training and data sampling strategies .
Outcome:	The proposed model outperforms existing models with similar parameters on a wide range of tasks.

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Ming Dong, Zhiwei Cheng, Changyin Luo, Tingting He

Low-Resource NLP

Challenge:	Existing LLM-based Chinese spelling check methods rely on fixed prompt samples . existing methods are limited by technical bottlenecks, complex recognition environments, and individual differences .
Approach:	They propose a framework called RagID to provide well-chosen prompt samples . they propose to use semantic-based similarity search and iterative discriminator mechanism .
Outcome:	The proposed framework can provide well-chosen prompt samples and reduce overcorrection issues in Chinese spelling check tasks.

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)

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Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan

Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs (2024.findings-emnlp)

Challenge:	Existing LLMs are difficult to achieve satisfactory results in table-related tasks.
Approach:	They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks.
Outcome:	The proposed model achieves state-of-the-art on a Logic2Text dataset.

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Wenhua Cheng, Weiwei Zhang, Haihao Shen, Yiyang Cai, Xin He, Lv Kaokao, Yi Liu

Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant memory and storage requirements.
Approach:	They propose a method that optimizes rounding values and weight clipping within 200 steps.
Outcome:	The proposed method achieves exceptional results across 2 to 4 bits while maintaining low tuning costs and avoiding additional inference overhead.

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Huadai Liu, Rongjie Huang, Jinzheng He, Gang Sun, Ran Shen, Xize Cheng, Zhou Zhao

Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model (2024.emnlp-main)

Challenge:	Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance.
Approach:	They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems.
Outcome:	The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline.

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Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang