Papers by Cheng Yan

CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)

Challenge:	Existing methods for style transfer are based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases.
Approach:	They propose a retrieval-based context-aware style representation that involves top-K relevant sentences in the target style in the transfer process.
Outcome:	The proposed method outperforms several strong baselines and is general and effective to the task of unsupervised style transfer.

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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao

From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis (2024.emnlp-main)

Challenge:	Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality.
Approach:	They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation.
Outcome:	The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks.

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Chuanqi Cheng, Jian Guan, Wei Wu, Rui Yan

Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)

Challenge:	Existing models consisting of multiple steps of visual and language processing are limited in the visual and visual processing community . a visual reasoner is a plug-and-play approach that can be used to improve VLMs' reasoning abilities.
Approach:	They propose a least-to-most visual reasoning paradigm that divides a question into sub-questions and invokes external tools for resolving sub-questions.
Outcome:	The proposed method can improve four VLMs on four VQA benchmarks.

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Yan Zeng, Wangchunshu Zhou, Ao Luo, Ziming Cheng, Xinsong Zhang

Decouple knowledge from paramters for plug-and-play language modeling (2023.findings-acl)

Challenge:	Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%.
Approach:	They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives.
Outcome:	The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets.

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Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, Rui Yan

Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning (2025.coling-main)

Challenge:	Pre-trained language models (PLMs) have made impressive results in a wide range of NLP tasks.
Approach:	They propose a pre-training model with editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’.
Outcome:	The proposed model decouples the knowledge storage from model parameters with an editable and scalable key-value memory and leverages knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’.

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Feng Zhao, Zhilu Zhang, Cheng Yan, Xianggan Liu

“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)

Challenge:	Existing subgraph-based models focus on predicting missing relations in knowledge graphs . a new meta-learning model extracts concepts from entities to construct commonsense subgraphs based on semantic information .
Approach:	They propose a commonsense subgraph meta-learning model that extracts concepts from entities to construct commonsensible subgraphs.
Outcome:	The proposed model outperforms existing models in inductive reasoning tasks and in few-shot scenarios.

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Chuanqi Cheng, Quan Tu, Wei Wu, Shuo Shang, Cunli Mao, Zhengtao Yu, Rui Yan

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create.
Approach:	They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles.
Outcome:	The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method.

Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)

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Guangtao Lyu, Qi Liu, Chenghao Xu, Jiexi Yan, Muli Yang, Xueting Li, Fen Fang, Cheng Deng

Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)

Challenge:	Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models .
Approach:	They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength.
Outcome:	The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks.

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Di Luo, Weiheng Liao, Shuqi Li, Xin Cheng, Rui Yan

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)

Challenge:	Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance.
Approach:	They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks.
Outcome:	The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets.

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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum

Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025.findings-acl)

Challenge:	Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window.
Approach:	They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window.
Outcome:	The proposed model scales to multi-million-token effective TTC without exceeding context limits.

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Fu Zhang, Yi Yan, Jingwei Cheng

Information Extraction & Text Mining

Challenge:	Existing document-level relation extraction models focus on individual entity pairs, limiting their ability to handle complex reasoning tasks.
Approach:	They propose a document-level relation extraction framework based on global relations and entity pair reasoning that captures fine-grained interactions between entity pairs.
Outcome:	The proposed framework outperforms existing models on widely-used datasets.

The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)

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Daniel Cheng, Kyle Yan, Phillip Keung, Noah A. Smith

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

Challenge:	Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models.
Approach:	They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups.
Outcome:	The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech.

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

Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models (2025.findings-acl)

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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Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan, Cheng Yang, Zheni Zeng, Zhiyuan Liu, Maosong Sun, Ge Yu

Priority on High-Quality: Selecting Instruction Data via Consistency Verification of Noise Injection (2025.emnlp-main)

Challenge:	Existing evaluation metrics cannot fairly evaluate the outputs of RAG models during training and evaluation.
Approach:	They propose a method which prompts LLMs to generate different judgments based on various combinations of judgment dimensions and utilizes the judge-consistency to evaluate these judgments.
Outcome:	The proposed method generates more accurate evaluations for RAG models across different RAG model and datasets.

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Hong Zhang, Feng Zhao, Ruilin Zhao, Cheng Yan, Kangzheng Liu

SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)

Challenge:	Existing methods for instruction selection rely on external models or rules, overlooking the intrinsic association between pre-trained model and instruction data.
Approach:	They propose a method that utilizes noise injection to identify the quality of instruction data without relying on external models.
Outcome:	The proposed method outperforms the model trained on the entire dataset and established baselines.

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Fu Zhang, Qi Miao, Jingwei Cheng, Hongsen Yu, Yi Yan, Xin Li, Yongxue Wu

Information Extraction & Text Mining

Challenge:	Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs.
Approach:	They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction.
Outcome:	The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models.

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)

Challenge:	Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models.
Approach:	They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines.

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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao

Natural Language Interfaces

Challenge:	Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning .
Approach:	They propose a synergistic dual-process framework that integrates reasoning and retrieval.
Outcome:	The proposed framework improves answer accuracy and coherence even in smaller-scale models.

Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction (2023.emnlp-main)

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Feng Zhao, Hongzhi Zou, Cheng Yan

Structured Data in NLP

Challenge:	Existing methods to generate knowledge graph-to-text (KG-to) text rely on pre-trained language models to bridge the gap between the different structures of the input KG and the target text.
Approach:	They propose a method that integrates graph structure-aware modules with pre-trained language models to capture the intricate topology information present in the KG.
Outcome:	The proposed model captures the topology information present in the knowledge graph and distinguishes similar input KGs through contrastive learning techniques.

LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection (2026.acl-long)

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Cheng Xu, Changhong Jin, Yingjie Niu, Nan Yan, Yuke Mei, Shuhao Guan, Liming Chen, Tahar Kechadi

System Prompt Hijacking via Permutation Triggers in LLM Supply Chains (2025.findings-acl)

Challenge:	Current evaluation frameworks are static and vulnerable to benchmark data contamination . current models are ineffective at assessing reasoning under temporal uncertainty .
Approach:	They propose a live-based benchmark that simulates the real-world "fog of war" they propose evaluating models on their ability to reason with evolving, incomplete information .
Outcome:	The proposed model outperforms proprietary state-of-the-art models in classification and evidence mode . it also provides a component to monitor BDC explicitly .

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Lu Yan, Siyuan Cheng, Xuan Chen, Kaiyuan Zhang, Guangyu Shen, Xiangyu Zhang

TestAgent: An Adaptive and Intelligent Expert for Human Assessment (2025.findings-acl)

Challenge:	Large language models (LLMs) are rapidly transforming the landscape of artificial intelligence due to the substantial resources required for training.
Approach:	They propose a post-deployment attack that bypasses system prompts to compromise models . they introduce Precise Activation Guarding and Unit Deviation Sampling to protect against attack .
Outcome:	The proposed attack bypasses system prompts, enabling unrestricted model outputs and safety violations.

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Junhao Yu, Yan Zhuang, Yuxuan Sun, Weibo Gao, Qi Liu, Mingyue Cheng, Zhenya Huang, Enhong Chen

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

Challenge:	Existing adaptive testing methods face several challenges due to mechanized nature of most algorithms and noisy response data.
Approach:	They propose to use large language models to enhance adaptive testing through interactive engagement to capture test-takers’ responses and anomalies.
Outcome:	The proposed agent achieves more accurate results with 20% fewer questions than state-of-the-art baselines and testers preferred it in speed, smoothness, and other dimensions.

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

Event Detection with Multi-Order Graph Convolution and Aggregated Attention (D19-1)

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

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Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, Xueqi Cheng

Syntactic Text Processing

Challenge:	Existing methods for event detection use first-order syntactic relations to identify trigger words.
Approach:	They propose a dependency tree-based method to model and aggregate multi-order syntactic representations in sentences.
Outcome:	The proposed method outperforms existing methods on a benchmark dataset . it uses a dependency tree based graph convolution network with aggregative attention .

Qsnail: A Questionnaire Dataset for Sequential Question Generation (2024.lrec-main)

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Yan Lei, Liang Pang, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng

Dialogue Summarization with Static-Dynamic Structure Fusion Graph (2023.acl-long)

Challenge:	Questionnaires are a professional research methodology used for qualitative and quantitative analysis of human opinions, preferences, and behaviors.
Approach:	They propose a questionnaire-based dataset that consists of 13,168 human-written questionnaires.
Outcome:	The proposed dataset contains 13,168 human-written questionnaires gathered from online platforms.

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Shen Gao, Xin Cheng, Mingzhe Li, Xiuying Chen, Jinpeng Li, Dongyan Zhao, Rui Yan

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

Challenge:	Dialogue summarization is a challenging task since it has dynamic interaction nature and inconsistent information flow among various speakers.
Approach:	They propose a Static-Dynamic graph-based Dialogue Summarization model which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion.
Outcome:	The proposed model can help people capture the highlights of a semi-structured and multi-participant dialogue without reviewing the complex dialogue context.

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

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)

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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Haidong Xin, Xinze Li, Zhenghao Liu, Yukun Yan, Shuo Wang, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

SSA: Semantic Contamination of LLM-Driven Fake News Detection (2025.emnlp-main)

Challenge:	Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows.
Approach:	They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% .
Outcome:	The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction.

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Cheng Xu, Nan Yan, Shuhao Guan, Yuke Mei, Tahar Kechadi

TripleFact: Defending Data Contamination in the Evaluation of LLM-driven Fake News Detection (2025.acl-long)

Challenge:	Evaluating 45 variants of nine LLMs, we find LIAR2 accuracy climbs monotonically with injected contamination, while the SSA Factor escalates in near-perfect lock-step.
Approach:	They propose a framework that detects BDC risks across semantic to label level via entity shift perturbation and an interpretable metric, the SSA Factor.
Outcome:	The proposed framework detects BDC risks across semantic to label level via entity shift perturbation and interpretable metric, the SSA Factor.

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Cheng Xu, Nan Yan

Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)

Challenge:	Existing evaluation paradigms for fake news detection are based on static datasets and closed-world assumptions that are inadvertently memorized during pre-training.
Approach:	They propose a framework to mitigate BDC risk while prioritizing real-world applicability by integrating three components to assess robustness against human-crafted misinformation.
Outcome:	The proposed framework mitigates BDC risk while prioritizing real-world applicability.

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)

Challenge:	Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal.
Approach:	They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations.
Outcome:	The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses.

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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan

AgentOCR: Reimagining Agent History via Optical Self-Compression (2026.acl-long)

Challenge:	Existing models for pre-training are not convenient for users to find and set them up.
Approach:	They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model .
Outcome:	The proposed models achieve new state-of-the-art on 10 benchmarks.

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Lang Feng, Fuchao Yang, Feng Chen, Xin Cheng, Haiyang Xu, Zhenglin Wan, Ming Yan, Bo An

Visual Data in NLP

Challenge:	Recent advances in large language models (LLMs) have enabled agentic systems trained with reinforcement learning over multi-turn interaction, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token and memory costs.
Approach:	They propose a framework that represents the accumulated observation-action history as a compact rendered image.
Outcome:	The proposed framework preserves over 95% of text-based agent performance while significantly reducing token consumption (>50%), yielding consistent token and memory efficiency.

CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)

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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen

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

Challenge:	Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected.
Approach:	They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans .
Outcome:	The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test .

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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 .

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

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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

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)

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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Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min Zhang

HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)

Challenge:	Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections.
Approach:	They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models.
Outcome:	The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies.

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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang

Natural Language Interfaces

Challenge:	Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research.
Approach:	They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables.
Outcome:	The proposed model shows that it is effective in QA and natural language generation over hierarchical tables.

ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation (2024.findings-emnlp)

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Yan Hu, Wenjie Li, Jiang Liu

Text Generation

Challenge:	Existing approaches to radiology report generation lack inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants.
Approach:	They propose a method which improves the inter-report consistency of radiology report generation by extracting lesions from input images and examining their characteristics.
Outcome:	The proposed system captures similarities in semantically equivalent lesions and can be used to generate reports for two semantically identical cases.

RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents (2026.findings-acl)

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Zijie Dai, Shiyuan Deng, Sheng Guan, Yizhou Tian, Xin Yao, Xiao Yan, James Cheng

Green & Sustainable NLP

Challenge:	Existing memory systems invoke LLMs to extract episodic and semantic memory, and this leads to substantial token consumption.
Approach:	They propose a method that stores incoming interactions in a subconscious memory layer and encodes them using lightweight embedding models for retrieval.
Outcome:	Experiments show that RecMem reduces the memory construction token cost of three SOTA memory systems by up to 87% while exceeding their accuracy.

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains (2025.acl-long)

Challenge:	Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization.
Approach:	They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question.
Outcome:	The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks.

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Juntian Zhang, Chuanqi Cheng, Yuhan Liu, Wei Liu, Jian Luan, Rui Yan

Correcting Language Model Bias for Text Classification in True Zero-Shot Learning (2024.lrec-main)

Challenge:	Existing vision-language models struggle to disentangle information scattered across complex visual inputs, leading to performance degradation.
Approach:	They propose a focus-centric visual chain paradigm that enhances VLMs’ perception, comprehension, and reasoning abilities in multi-image scenarios.
Outcome:	The proposed approach achieves average performance gains of 3.16% and 2.24% across two distinct model architectures, without compromising the general vision-language capabilities.

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Feng Zhao, Wan Xianlin, Cheng Yan, Chu Kiong Loo

Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models (2025.findings-emnlp)

Challenge:	Experimental results show that pre-trained language models outperform standard prompt learning in zero-shot settings.
Approach:	They propose a pipeline for annotating and filtering examples from unlabeled examples . they propose 'model bias validation' method that utilizes unlabed examples as validation set .
Outcome:	The proposed approach outperforms standard prompt learning on six text classification tasks.

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Cheng Yan, Feng Zhao, Ruilin Zhao, Hong Zhang

Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)

Challenge:	Current knowledge graph reasoning methods rely on extensive structural information to perform in these few-shot scenarios.
Approach:	They propose a task-aware language model that activates the language model’s in-context learning ability for structured KG tasks.
Outcome:	The proposed method achieves state-of-the-art in few-shot scenarios while reducing the inference time required by previous methods.

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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng

Challenge:	Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content.
Approach:	They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces .
Outcome:	The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining.

Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring (2026.acl-long)

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Dongxu Zhang, Yiding Sun, Cheng Tan, Wenbiao Yan, Ning Yang, Jihua Zhu, Haijun Zhang

Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space (2026.findings-acl)

Challenge:	Existing efforts to mitigate this via token compression fail due to its autoregressive nature . linguistically redundant tokens are erroneously pruned, leading to hallucinations .
Approach:	They propose a method that reformulates token pruning as a Visual-Anchored Information Bottleneck (VA-IB) optimization problem.
Outcome:	Experiments on Qwen2-VL and Llama-3.2 families show that the proposed model achieves a speedup with negligible accuracy loss.

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Xiang Zhang, Kun Wei, Xu Yang, Jiahua Li, Su Yan, Cheng Deng

Challenge:	Existing methods to remove undesirable data from Large Language Models suffer from cumulative catastrophic utility loss under continuous unlearning requests.
Approach:	They propose a method that leverages the rotational salience weight of RCU to quantify and control the unlearning degree in the continuous unlearning process.
Outcome:	The proposed method achieves SOTA performance without a retained dataset.

LLM Knows Body Language, Too: Translating Speech Voices into Human Gestures (2024.acl-long)

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Chenghao Xu, Guangtao Lyu, Jiexi Yan, Muli Yang, Cheng Deng

Biomedical Concept Normalization by Leveraging Hypernyms (2021.emnlp-main)

Challenge:	despite advances in the generation of realistic human gestures, the process often includes unintended, meaningless, or non-realistic gestures.
Approach:	They propose a framework that leverages large language models to generate human gestures . the primary stage employs a transformer-based auto-encoder network to encode human gesture into discrete symbols .
Outcome:	The proposed framework has demonstrated state-of-the-art performance on public TED and TED-Expressive datasets.

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Cheng Yan, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yafei Shi, Shengping Liu

Challenge:	Biomedical Concept Normalization (BCN) is widely used in biomedical text processing . despite numerous surface variants of biomedically-defined concepts, it remains challenging and unsolved.
Approach:	They propose a framework that uses hypernyms and synonyms to facilitate BCN . they use list-wise training to make use of both hypernies and synonym entities .
Outcome:	The proposed framework outperforms the state-of-the-art model on the NCBI dataset.

NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (D19-3)

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Ming Gong, Linjun Shou, Wutao Lin, Zhijie Sang, Quanjia Yan, Ze Yang, Feixiang Cheng, Daxin Jiang

Challenge:	Deep Neural Networks (DNN) have been widely employed in industry to address various natural language processing tasks.
Approach:	They propose an NLP toolkit that encapsulates neural network modules as building blocks to construct various DNN models with complex architecture.
Outcome:	The proposed toolkit can build, train, and test various DNN models with complex architecture.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)

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Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng

DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment (2023.findings-emnlp)

Challenge:	FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds .
Approach:	They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows.
Outcome:	The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments.

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

Natural Language Interfaces

Challenge:	Existing studies on dialogue quality assessment are uncapable of providing an end-to-end and human-epistemic assessment dataset . open-domain dialogue assessment is complicated and costly, but it can be done by recruiting human evaluators.
Approach:	They propose a large-scale dialogue quality assessment dataset for automatically assessing open-domain dialogue quality.
Outcome:	The proposed dataset is openly accessible at https://github.com/yukunZhao/Dialogue_quality_evaluation.

AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)

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Yuhang Wu, Wenmeng Yu, Yean Cheng, Yan Wang, Xiaohan Zhang, Jiazheng Xu, Ming Ding, Yuxiao Dong

SCALE: Synergized Collaboration of Asymmetric Language Translation Engines (2024.findings-acl)

Challenge:	Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions.
Approach:	They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts.
Outcome:	The proposed benchmark covers 13 tasks across three categories and includes both single-turn and multi-turn dialogue scenarios.

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Xin Cheng, Xun Wang, Tao Ge, Si-Qing Chen, Furu Wei, Dongyan Zhao, Rui Yan

Multilinguality

Challenge:	In this paper, we introduce SCALE, a collaborative framework that connects a compact Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine.
Approach:	They propose a collaborative framework that connects a Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine.
Outcome:	The proposed framework outperforms both LLMs and supervised models in high-resource or challenging low-resourced settings.

Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing (2025.acl-long)

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Zhilin Wang, Yafu Li, Jianhao Yan, Yu Cheng, Yue Zhang

DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)

Challenge:	Dynamical systems theory provides a framework for understanding iterative processes and evolution over time.
Approach:	They propose to apply this perspective to large language models which iteratively map input text to output text and re-express meaning with linguistic variation.
Outcome:	The proposed model reveals that paraphrases re-express meaning with linguistic variation limiting linguistic diversity .

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Cheng Xu, Nan Yan, Shuhao Guan, Changhong Jin, Yuke Mei, Yibing Guo, Tahar Kechadi

Challenge:	Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment.
Approach:	They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system.
Outcome:	The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels.

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

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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

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

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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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.

Unified Active Retrieval for Retrieval Augmented Generation (2024.findings-emnlp)

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Qinyuan Cheng, Xiaonan Li, Shimin Li, Qin Zhu, Zhangyue Yin, Yunfan Shao, Linyang Li, Tianxiang Sun, Hang Yan, Xipeng Qiu

Information Retrieval

Challenge:	Existing active retrieval methods struggle with handling various types of instructions.
Approach:	They propose a unified active retrieval framework for retrieval-augmented generation . they propose to combine four orthogonal criteria into plug-and-play classification tasks .
Outcome:	The proposed framework outperforms existing methods on four representative types of user instructions on four types of instructions.

Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs (2025.emnlp-main)

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Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, Jieping Ye

Revisiting Self-training for Few-shot Learning of Language Model (2021.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) demonstrate excellent abilities for understanding visual information, but the hallucination remains a challenging problem.
Approach:	They propose a training-free approach to enhance vision attention sinks to facilitate convergence of the image token attention sink within shallow layers.
Outcome:	The proposed approach improves the convergence of the image token attention sink within shallow layers and strengthens the layer’s focus on the image itself.

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Yiming Chen, Yan Zhang, Chen Zhang, Grandee Lee, Ran Cheng, Haizhou Li

LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)

Challenge:	Unlabeled data are useful for few-shot learning of language models.
Approach:	They propose a prompt-based few-shot learner that uses unlabeled data to fine-tune language models.
Outcome:	The proposed approach outperforms state-of-the-art models on six sentence classification and six sentence-pair classification benchmarking tasks.

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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li

HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training.
Approach:	They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks.

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Jie Ouyang, Tingyue Pan, Mingyue Cheng, Ruiran Yan, Yucong Luo, Jiaying Lin, Qi Liu

Information Retrieval

Challenge:	Current approaches to addressing knowledge outdating in LLMs struggle with retrieval and generation aspects when handling outdated information.
Approach:	They propose a benchmark to evaluate the impact of outdated information on RAG . they use token-level diff algorithms and LLM pipelines to create a large-scale QA dataset .
Outcome:	The proposed benchmark analyzes the impact of outdated information on RAG performance.

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)

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Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si

Structured Data in NLP

Challenge:	Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description.
Approach:	They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information.
Outcome:	The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information.

DNASpeech: A Contextualized and Situated Text-to-Speech Dataset with Dialogues, Narratives and Actions (2025.acl-long)

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Chuanqi Cheng, Hongda Sun, Bo Du, Shuo Shang, Xinrong Hu, Rui Yan

Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (2026.acl-long)

Challenge:	Existing TTS datasets lack situated descriptive prompts aligned with speech data.
Approach:	They propose a contextualized and situated text-to-speech task to promote more accurate and customized speech generation using DNA prompts.
Outcome:	The proposed task promotes more accurate and customized speech generation using DNA prompts.

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Chenghao Xu, Jiexi Yan, Guangtao Lyu, Qi Liu, Muli Yang, Cheng Deng

Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)

Challenge:	Existing methods for locating and editing static knowledge are costly and risk catastrophic forgetting or error.
Approach:	They propose a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update.
Outcome:	Experiments on standard benchmarks show that FiDAL improves editing effectiveness and knowledge preservation across multiple editing methods.

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Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have shown outstanding breakthroughs in code generation.
Approach:	They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training.
Outcome:	The proposed task improves distribution case-to-code induction and various coding generation tasks.

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun