Papers by Peng Chen

Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping (2026.findings-acl)

Challenge:	ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines .
Approach:	They propose a Macro-to-Micro progressive learning approach that improves UIE without external information.
Outcome:	ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone.

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Miao Peng, Weizhou Shen, Nuo Chen, Chenliang Li, Ming Yan, Jia Li

Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs’ short-context reasoning but falters in long-contemporal scenarios requiring precise grounding and multi-hop reasoning.
Approach:	They propose a framework that constructs high-difficulty, multi-hop long-context QA pairs with inherent reasoning chains to overcome this bottleneck.
Outcome:	The proposed framework outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters.

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Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong

AT²PO: Agentic Turn-based Policy Optimization via Tree Search (2026.acl-long)

Challenge:	Existing fashion recommendation systems struggle with the unique challenges of the fashion domain.
Approach:	They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts.
Outcome:	The proposed framework significantly improves fashion recommendation performance on Amazon fashion.

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Zefang Zong, Dingwei Chen, Yang Li, Qi Yi, Bo Zhou, Chengming Li, BO Qian, Peng Chen, Jie Jiang

Model Composition for Multimodal Large Language Models (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have catalyzed the development of autonomous agents capable of executing complex, multi-turn tasks.
Approach:	They propose a framework for agentic reinforcement learning that integrates turn-level tree search with tree search to address key challenges.
Outcome:	The proposed framework addresses key challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization.

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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset (2022.lrec-1)

Challenge:	Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities.
Approach:	They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters.
Outcome:	The proposed model retains the modal understanding capabilities of each original model.

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Tiezheng Yu, Rita Frieske, Peng Xu, Samuel Cahyawijaya, Cheuk Tung Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung

Large Language Models are Limited in Out-of-Context Knowledge Reasoning (2024.findings-emnlp)

Challenge:	In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages.
Approach:	They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model .
Outcome:	The proposed model improves on the biggest existing dataset, Common Voice zh-HK.

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Peng Hu, Changjiang Gao, Ruiqi Gao, Jiajun Chen, Shujian Huang

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning.
Approach:	They evaluated a dataset with seven representative OCKR tasks to assess their OCKr capabilities.
Outcome:	The model's OCKR abilities are limited regardless of whether the knowledge is trained in a separate or adjacent training setting.

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Zhiwen Ruan, Yichao Du, Jianjie Zheng, Longyue Wang, Yun Chen, Peng Li, Jinsong Su, Yang Liu, Guanhua Chen

Contextualized Perturbation for Textual Adversarial Attack (2021.naacl-main)

Challenge:	Existing adapter-based transfer methods treat instruction-tuned models as passive targets . direct fine-tuning can disrupt this delicate balance and lead to instability or performance degradation.
Approach:	They propose a framework that incorporates instruction-level guidance into task adaptation.
Outcome:	The proposed framework outperforms direct fine-tuning and representative transfer-based baselines while maintaining robust generalization and favorable test-time scaling behavior.

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Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, Bill Dolan

Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis (2025.acl-long)

Challenge:	Existing techniques for generating adversarial examples are driven by local heuristic rules that are agnostic to the context, resulting in unnatural and ungrammatical outputs.
Approach:	They propose a ContextuaLized AdversaRial Example generation model that generates fluent and grammatical outputs through a mask-then-infill procedure.
Outcome:	The proposed model outperforms baseline models in terms of attack success rate, textual similarity, fluency and grammaticality.

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Minghao Lv, Siyuan Chen, Haoan Jin, Minghao Yuan, Qianqian Ju, Yujia Peng, Kenny Q. Zhu, Mengyue Wu

Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training (2025.acl-long)

Challenge:	Existing studies have indicated that major life events can greatly impact individuals’ mental health, but shedding its light on social media data is challenging due to the complexity and ambiguity nature of life events.
Approach:	They propose to extract life events mentioned in posts on social media to uncover a social media event dataset which includes 12 major life event categories that are likely to occur in everyday life.
Outcome:	The proposed dataset includes 12 life event categories that are likely to occur in everyday life and is human-annotated under iterative procedure and boasts a high level of quality.

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Zheheng Luo, Xin Zhang, Xiao Liu, Haoling Li, Yeyun Gong, Qi Chen, Peng Cheng

ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models (2025.acl-long)

Challenge:	Existing methods to optimise pretraining performance have not addressed the complexities of domain-adaptive continual pretraining.
Approach:	They propose a framework that dynamically assesses learning velocity and adjusts data proportions accordingly, favouring slower learning domains while de-emphasising faster learning ones.
Outcome:	The proposed framework achieves performance gains in math and code reasoning tasks and command-line generation benchmarks.

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Ziyue Wang, Chi Chen, Fuwen Luo, Yurui Dong, Yuanchi Zhang, Yuzhuang Xu, Xiaolong Wang, Peng Li, Yang Liu

TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs (2026.acl-long)

Challenge:	Existing benchmarks for evaluating MLLMs have not addressed active perception . a novel benchmark is proposed to evaluate active perception in ML models .
Approach:	They propose a benchmark to evaluate active perception in Multimodal Large Language Models . they restrict the perceptual field of a model and require it to actively zoom or shift it .
Outcome:	The proposed benchmark focuses on a specialized form of Visual Question Answering (VQA) that eases and quantifies the evaluation yet challenging for existing MLLMs.

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Ziyi Wang, Chen Zhang, Wenjun Peng, Qi Wu, Xinyu Wang

VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)

Challenge:	Existing explainability methods for large language models have been limited in capturing interaction-dependent belief dynamics and multi-agent reasoning.
Approach:	They propose a tri-view explainability framework that instruments sequential decision making with aligned artifacts.
Outcome:	The proposed framework enables analysis of explanation faithfulness, belief dynamics, and evaluator reliability, revealing systematic mismatches between what agents say, what they believe, and what they do.

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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg

RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)

Challenge:	Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models.
Approach:	They propose a single-stage joint speech-text SFT approach for training SpeechLMs . their model combines text-only SFT data with three types of speech-related data .
Outcome:	The proposed model outperforms previous SpeechLMs on speech-based QA tasks while maintaining original speech-only capabilities.

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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma

Challenge:	Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation.
Approach:	They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation.
Outcome:	The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics.

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)

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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye

A Comprehensive Literary Chinese Reading Comprehension Dataset with an Evidence Curation Based Solution (2025.emnlp-main)

Challenge:	Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences.
Approach:	They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks .
Outcome:	The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency.

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Dongning Rao, Rongchu Zhou, Peng Chen, Zhihua Jiang

LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment (2026.findings-acl)

Challenge:	Low-resource language understanding is challenging for large language models (LLMs).
Approach:	They propose a CompRehensive lIterary Chinese readIng comprehenSion procedure with a large dataset for CRISIS.
Outcome:	The proposed procedure has the largest dataset and substantiates the effectiveness of the proposed procedure with a 7 percent hike in accuracy compared with the baseline.

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Shipeng Li, Zhiqin Yang, Shikun Li, Xiaobo Xia, Hengyu Liu, Xinghua Zhang, Gaode Chen, Dong Fang, Ying Tai, Zhe Peng

LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models (2024.naacl-long)

Challenge:	Reinforcement learning with verifiable rewards (RLVR) is a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck.
Approach:	They propose a gradient-alignment-based method which intelligently selects the learnable and representative training reasoning data for RLVR post-training.
Outcome:	Experiments on five reasoning benchmarks show that the proposed method significantly reduces training data requirements while improving performance.

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Chi Han, Qifan Wang, Hao Peng, Wenhan Xiong, Yu Chen, Heng Ji, Sinong Wang

G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)

Challenge:	Currently, large language models (LLMs) train on short text segments due to the computational overhead quadratic in the input lengths of their Transformer architectures.
Approach:	They propose a method that allows LLMs pre-trained with 2K or 4K-long segments to generalize to up to 200M length inputs while retaining perplexity.
Outcome:	The proposed method achieves 2.7 decoding speed up and 7.5 memory saving over the original model.

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Zhiwen Ruan, Yixia Li, Yefeng Liu, Yun Chen, Weihua Luo, Peng Li, Yang Liu, Guanhua Chen

Challenge:	Existing approaches to enhance output diversity but compromise quality of outputs.
Approach:	They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality.
Outcome:	The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality.

Grafting Pre-trained Models for Multimodal Headline Generation (2022.emnlp-industry)

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Lingfeng Qiao, Chen Wu, Ye Liu, Haoyuan Peng, Di Yin, Bo Ren

“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations? (2024.findings-emnlp)

Challenge:	Existing approaches to generate video headlines with pre-trained language models are labor intensive and impractical.
Approach:	They propose to graft the encoder from the pre-trained video-language model on the generative pre-trainer model and propose a consensus fusion mechanism for the integration of different components.
Outcome:	The proposed model achieves strong results on a brand-new dataset collected from real-world applications.

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Beiduo Chen, Xinpeng Wang, Siyao Peng, Robert Litschko, Anna Korhonen, Barbara Plank

Explainability & Interpretability in NLP

Challenge:	Human label variation arises when multiple human annotators provide different labels for valid reasons.
Approach:	They propose to use crowd workers to represent human judgment distributions or expert linguists to provide detailed explanations for their chosen labels.
Outcome:	The proposed model can approximate human judgment distributions using a small number of expert labels and explanations.

MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)

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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li

A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI (2025.findings-acl)

Challenge:	Existing datasets for event understanding have limited coverage due to complexity of tasks.
Approach:	They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation .
Outcome:	The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.

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Beiduo Chen, Siyao Peng, Anna Korhonen, Barbara Plank

Explainability & Interpretability in NLP

Challenge:	Recent research has shown that explanations provide valuable information for understanding human label variation (HLV) Large language models (LLMs) can approximate HJD from a few human-provided label-explanation pairs, but collecting explanations for every label is still time-consuming.
Approach:	They propose to use Large Language Models (LLMs) as annotators to generate model explanations for a few given human labels.
Outcome:	The proposed models can generate human-provided explanations from human labels, but they are still time-consuming.

Beyond Facts- Benchmarking Distributional Reading Comprehension in Large Language Models (2026.findings-acl)

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Pei-Fu Guo, Ya An Tsai, Chun-Chia Hsu, Kai-Xin Chen, Yun-Da Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Existing reading comprehension benchmarks focus on factual information, but many real-world tasks require distributional knowledge expressed across text.
Approach:	They propose a reading comprehension benchmark for LLMs to evaluate their ability to infer distributional knowledge from natural language.
Outcome:	Experiments with multiple LLMs show that the model outperforms baselines, but performance varies widely across distribution types and characteristics.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs (2026.acl-long)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu, Kai-Xin Chen, Ya An Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin

Multilinguality

Challenge:	Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training.
Approach:	They propose a pipeline to isolate and measure cross-lingual knowledge transfer by identifying self-contained, time-sensitive knowledge entities from real-world domains and generating factual questions.
Outcome:	The proposed pipeline analyzes multiple LLMs across five languages and shows that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions.

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu

Hybrid Hierarchical Retrieval for Open-Domain Question Answering (2023.findings-acl)

Challenge:	Sentence embeddings are typically learned to recognize the semantic relation between two text inputs.
Approach:	They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
Outcome:	The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences.

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Manoj Ghuhan Arivazhagan, Lan Liu, Peng Qi, Xinchi Chen, William Yang Wang, Zhiheng Huang

HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning (2021.findings-emnlp)

Challenge:	Recent work shows that dense hierarchical retrieval (DHR) can outperform dense passage retrieval.
Approach:	They propose a framework that applies sparse, dense and a combination of them to document and passage retrieval.
Outcome:	The proposed framework can outperform dense hierarchical retrieval (DHR) and sparse retrievers (BM25) on open-domain question answering (ODQA) datasets with an average improvement of 4.69% on recall@100 over DHR.

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Mingyu Derek Ma, Muhao Chen, Te-Lin Wu, Nanyun Peng

Representation Learning

Challenge:	Existing taxonomies have limited coverage due to expensive manual curation process.
Approach:	They propose an algorithm that expands existing taxonomies to preserve their structure in a more expressive hyperbolic embedding space and learns to represent concepts and their relations with a hyperbolical Graph Neural Network.
Outcome:	The proposed algorithm outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.

Difference in Task Performance on Sparse Speech Representations (2026.acl-long)

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Wenjie Peng, Chen Chen, Thomas Hain

Speech & Audio in NLP

Challenge:	Existing methods for learning speech representations that are useful for a variety of downstream tasks have been extensively investigated in different domains.
Approach:	They propose to train Autoencoders with varying sparsity levels using three SSL features and evaluate them on six tasks of SUPERB: speech enhancement, speaker identification, speech Emotion Recognition, phone recognition, automatic speech recognition and slot filling.
Outcome:	The proposed model can be used to learn speech representations that are useful for a variety of downstream tasks.

Improving Preference Alignment of LLM with Inference-Free Self-Refinement (2025.findings-emnlp)

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Fukun Ma, Kaibin Tian, Jieting Xue, Xiaoyi Wang, Ye Ma, Quan Chen, Peng Jiang, Lijie Wen

Challenge:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Approach:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Outcome:	Experiments show that incorporating IFSR into preference alignment yields performance improvement over 10%.

Incomplete In-context Learning (2026.acl-long)

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Wenqiang Wang, Wen Yujia, Yan Xiao, Zhifeng Chen, Yangshijie Zhang, Peng Chen, Mingbo Yang, Xiaochun Cao

Low-Resource NLP

Challenge:	Existing in-context learning assumes the retrieval dataset contains demonstrations for all output label spaces.
Approach:	They propose a framework with train-free and train-based variants to address IICL . they propose to integrate a dataset with labeled demonstrations for each output space .
Outcome:	The proposed framework outperforms existing methods under incomplete retrieval datasets and even outperformed ICL with complete labels.

UQ-Merge: Uncertainty Guided Multimodal Large Language Model Merging (2025.findings-acl)

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Huaizhi Qu, Xinyu Zhao, Jie Peng, Kwonjoon Lee, Behzad Dariush, Tianlong Chen

MUZO: Leveraging Multiple Queries and Momentum for Zeroth-Order Fine-Tuning of Large Language Models (2025.emnlp-main)

Challenge:	Existing models merging methods often lead to suboptimal performance due to harmful models . et al., 2018; 59: 59-64.
Approach:	They propose an uncertainty-guided MLLM merging algorithm that integrates models into a single MLML.
Outcome:	The proposed algorithm improves on held-in and held-out vision-language benchmarks.

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Yuezhang Peng, Yuxin Liu, Fei Wen, Xie Chen

Intrinsic Mutual Information as a Modulator for Preference Optimization (2026.findings-acl)

Challenge:	Existing methods for fine-tuning large language models incur memory overhead due to the need for activation storage for back-propagation (BP).
Approach:	They propose a method that estimates gradients through finite differences without activation storage for back-propagation.
Outcome:	The proposed method demonstrates superior performance in fine-tuning various LLMs.

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Peng Liao, Peijia Zheng, Lingbo Li, Shangsong Liang, Lin Chen

Challenge:	Existing methods for offline preference optimization involve additional hyperparameter tuning, resulting in substantial time overhead.
Approach:	They propose a lightweight framework for offline preference optimization that leverages hyperparameter modulation to decouple preference contributions.
Outcome:	The proposed framework achieves superior performance over existing methods while reducing training overhead by more than 15%.

Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning (2025.emnlp-main)

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Lang Cao, Yingtian Zou, Chao Peng, Renhong Chen, Wu Ning, Yitong Li

SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks (2026.acl-long)

Challenge:	Existing approaches to improve mathematical reasoning require extensive datasets for training or depend on few-shot methods that compromise computational accuracy.
Approach:	They propose a training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities.
Outcome:	The proposed framework outperforms Qwen2.5-72B-Math-Instruct on MMLU-STEM with a score of 90.9%, compared to 87.3%.

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Tianyi Wang, Yixia Li, Long Li, Yibiao Chen, Shaohan Huang, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

CELLO: Causal Evaluation of Large Vision-Language Models (2024.emnlp-main)

Challenge:	Proximal Policy Optimization (PPO) is central to aligning Large Language Models with verifiable rewards.
Approach:	They propose a scalable algorithm that harmonizes sample efficiency with stability of outcome-based updates.
Outcome:	The proposed algorithm outperforms standard PPO and matches the performance of computation-heavy group-based methods.

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Meiqi Chen, Bo Peng, Yan Zhang, Chaochao Lu

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization (2026.acl-long)

Challenge:	Recent advances in large vision-language models have improved causal reasoning abilities . however, current models struggle with tasks like causal reasoning .
Approach:	They propose a fine-grained and unified definition of causality involving interactions between humans and objects.
Outcome:	The proposed model surpasses traditional commonsense causality by including explicit causal graphs . it also shows that current LVLMs can benefit from a causally inspired prompting strategy .

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Changxin Ke, Rui Zhang, Jiaming Guo, Yuanbo Wen, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng, Di Huang, Zidong Du, Xing Hu, Qi Guo, Yunji Chen

Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures (2026.acl-long)

Challenge:	Existing approaches to program repair are based on correctness alone.
Approach:	They propose a framework that mitigates over-editing and improves repair accuracy by generating buggy programs and re-edits.
Outcome:	The proposed framework improves repair precision by 31.4% under fix1@1, a metric that considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing.

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Yi Hu, Jiaqi Gu, Ruxin Wang, Zijun Yao, Hao Peng, Xiaobao Wu, Jianhui Chen, Muhan Zhang, Liangming Pan

Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)

Challenge:	Recent research has shown that reinforcement learning can elicit intriguing emergent reasoning behaviors.
Approach:	They propose a comprehensive survey of the mechanistic understanding of large reasoning models . they organize findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors.
Outcome:	This paper synthesizes the mechanistic understanding of large reasoning models into three dimensions . authors outline a roadmap for future studies including improved interpretability and methodologies .

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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

Not All Modalities at Once: Dynamic Dropout and Bidirectional Fusion for Robust Multi-modal Knowledge Graph Completion (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone.
Approach:	They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs.
Outcome:	The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.

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Jiashun Peng, Fu Zhang, Hongzhi Chen, Jingwei Cheng, Yingsong Ning, Xiaoke Wang

AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)

Challenge:	Existing MKGC methods train with all modalities available, implicitly assuming consistent complementarity . however, this often induces modality dependence and modality competition under heterogeneous noise, which can hinder robust multi-modal fusion and limit overall performance.
Approach:	They propose a framework to infer missing links in multimodal knowledge graphs by leveraging structured triples together with auxiliary modalities such as text and images.
Outcome:	The proposed framework outperforms baselines and achieves new state-of-the-art results.

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Wenhao Huang, Zhouhong Gu, Chenghao Peng, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Liqian Wen, Zulong Chen

Challenge:	Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website.
Approach:	They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks.
Outcome:	The proposed framework can handle diverse web environments more efficiently.

Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)

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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han

Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text (2020.emnlp-main)

Challenge:	Existing methods to build named entity recognition systems with limited labeled data are lacking.
Approach:	They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited.
Outcome:	The proposed NER systems outperform existing methods on few-shot and training-free settings.

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Dongfang Li, Baotian Hu, Qingcai Chen, Weihua Peng, Anqi Wang

A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)

Challenge:	MRC has achieved significant progress on the open domain in recent years due to large-scale pre-trained language models.
Approach:	They propose a machine reading comprehension model which exploits structural medical knowledge and reference medical plain text to improve the exam's accuracy.
Outcome:	The proposed model outperforms existing models with a large margin and passes the exam with 61.8% accuracy rate on the test set.

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Xi Chen, Xinjiang Lu, Haoran Xin, Wenjun Peng, Haoyang Duan, Feihu Jiang, Jingbo Zhou, Hui Xiong

ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)

Challenge:	Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation.
Approach:	They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure.
Outcome:	The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets.

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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen

ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)

Challenge:	drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers .
Approach:	They propose a framework that automates method statement generation by using multi-agent collaboration.
Outcome:	The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity.

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Yuan Chen, Zhenyu Hu, Mengge Xue, Cao Te, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (2026.acl-long)

Challenge:	Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent.
Approach:	They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency.
Outcome:	The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video.

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Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov

PermuteFormer: Efficient Relative Position Encoding for Long Sequences (2021.emnlp-main)

Challenge:	Existing benchmarks emphasize final numerical answers while neglecting intermediate reasoning steps.
Approach:	They propose a symbolic benchmark for verifiable Chain-of-Thought evaluation in finance . FINCHAIN spans 58 topics across 12 financial domains and three difficulty levels .
Outcome:	The proposed benchmark aims to bridge symbolic reasoning and factual verification.

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Peng Chen

Contrastive Document Representation Learning with Graph Attention Networks (2021.findings-emnlp)

Challenge:	Existing Transformers that scale to long sequences are not compatible with relative position encoding.
Approach:	They propose a Performer-based model with relative position encoding that scales linearly on long sequences.
Outcome:	The proposed model outperforms performer on long sequences with no computational overhead and outperformed vanilla Transformer on most of the tasks.

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Peng Xu, Xinchi Chen, Xiaofei Ma, Zhiheng Huang, Bing Xiang

Knowledge Rumination for Pre-trained Language Models (2023.emnlp-main)

Challenge:	Existing methods for document representation learning are significantly affected by the scarcity of document-level data.
Approach:	They propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings.
Outcome:	Empirically, the proposed approach is effective in document classification and document retrieval tasks.

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Yunzhi Yao, Peng Wang, Shengyu Mao, Chuanqi Tan, Fei Huang, Huajun Chen, Ningyu Zhang

RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)

Challenge:	Existing studies have shown that pre-trained language models lack the capacity to handle knowledge-intensive tasks alone.
Approach:	They propose a new paradigm to help pre-trained language models utilize latent knowledge without retrieving it from external corpus.
Outcome:	The proposed paradigm can be applied to pre-trained language models without retrieving external knowledge from the corpus.

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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation.
Approach:	They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows.
Outcome:	The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases.

MAGI: Multi-Agent Guided Interview for Psychiatric Assessment (2025.findings-acl)

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Guanqun Bi, Zhuang Chen, Zhoufu Liu, Hongkai Wang, Xiyao Xiao, Yuqiang Xie, Wen Zhang, Yongkang Huang, Yuxuan Chen, Libiao Peng, Minlie Huang

Mitigating Language Bias of LMMs in Social Intelligence Understanding with Virtual Counterfactual Calibration (2024.emnlp-main)

Challenge:	Existing large language models (LLMs) do not align with psychiatric diagnostic protocols.
Approach:	They propose a framework that transforms the Mini International Neuropsychiatric Interview into automatic computational workflows through coordinated multi-agent collaboration.
Outcome:	The proposed framework transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration.

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Peng Chen, Xiao-Yu Guo, Yuan-Fang Li, Xiaowang Zhang, Zhiyong Feng

Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving (2024.acl-long)

Challenge:	Existing work on social intelligence using large multimodal models is under-explored due to the prevalence of text-based data in the pretraining stage.
Approach:	They propose a structure causal model to mitigate the negative language biases of large multimodal models by preserving beneficial priors.
Outcome:	The proposed model minimizes negative language bias while preserving beneficial priors while avoiding spurious correlations between LMMs' internal commonsense knowledge and the given context.

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Chenyang An, Zhibo Chen, Qihao Ye, Emily First, Letian Peng, Jiayun Zhang, Zihan Wang, Sorin Lerner, Jingbo Shang

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)

Challenge:	Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states.
Approach:	They propose to use a large language model that generates tactics to search through proof states.
Outcome:	The proposed model solves more unseen theorems with lower trial searches than the current model, which only learns from failed attempts.

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Gang Hu, Yating Chen, Haiyan Ding, Wang Gao, Huang Jiajia, Min Peng, Qianqian Xie, Kun Yue

Challenge:	Large Language Models excel in general domains but lack real-world practical capabilities.
Approach:	They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios.
Outcome:	The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios.

Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)

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Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

Challenge:	a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories .
Approach:	They propose a computational framework to analyze narratives through three discourse-level aspects.
Outcome:	The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding .

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu

RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

Challenge:	Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization.
Approach:	They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training.
Outcome:	The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization.

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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang

Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)

Challenge:	Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach:	They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome:	The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.

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Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu

Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)

Challenge:	Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles.
Approach:	They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments .
Outcome:	The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well .

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Zezhong Xu, Peng Ye, Hui Chen, Meng Zhao, Huajun Chen, Wen Zhang

RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning (2025.findings-acl)

Challenge:	Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed.
Approach:	They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer .
Outcome:	The proposed model takes context information into consideration, which helps select suitable rules for inference tasks.

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Yifan Niu, Miao Peng, Nuo Chen, Yatao Bian, Tingyang Xu, Jia Li

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

Challenge:	Existing knowledge editing methods struggle to reason about related conceptual knowledge effectively, despite a lack of model-level relational reasoning.
Approach:	They propose a benchmark to assess concept-level and instance-level relational reasoning abilities of edited models.
Outcome:	The proposed model obtains the best scores on the memory-based in-context editing baseline, MICE, suggesting a promising direction for model editing.

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis (2021.naacl-main)

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

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Xutan Peng, Guanyi Chen, Chenghua Lin, Mark Stevenson

CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners (2025.emnlp-main)

Challenge:	Knowledge Graph Embeddings (KGEs) have been explored in recent years due to their promise for a wide range of applications.
Approach:	They propose a KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches.
Outcome:	The proposed framework reduces the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches while producing competitive performance.

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Yunzhi Yao, Jizhan Fang, Jia-Chen Gu, Ningyu Zhang, Shumin Deng, Huajun Chen, Nanyun Peng

LLM-Enhanced Query Generation and Retrieval Preservation for Task-Oriented Dialogue (2025.findings-acl)

Challenge:	Existing knowledge editing methods fail to generalize updates to multi-hop reasoning tasks . Existing methods only edit single or a few model layers, inadequately integrate updated knowledge into reasoning pathways.
Approach:	They propose a circuit-aware method that enhances the effective integration of updated knowledge in large language models by leveraging curated data samples guided by their analysis.
Outcome:	The proposed method improves accuracy and accuracy of 20% on the MQuAKE dataset while requiring less memory.

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Jiale Chen, Xuelian Dong, Wenxiu Xie, Ru Peng, Kun Zeng, Tianyong Hao

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)

Challenge:	Existing knowledge retrieval methods for task-oriented dialogues are limited by data scarcity and lack of data to annotate.
Approach:	They propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue . they propose to select the most relevant knowledge from retrieved top-K records and incorporate them as prompts to guide a generator in response generation.
Outcome:	The proposed model outperforms state-of-the-art in three benchmarks on three standard benchmarks.

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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth

Question Answering

Challenge:	Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive.
Approach:	They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C.
Outcome:	The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness.

MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026.findings-acl)

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Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan, Xuanjing Huang

Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)

Challenge:	Existing research has focused on constraint categories, offering little guidance for improving instruction following abilities.
Approach:	They propose a multi-dimensional constraint framework that allows for instruction following . they construct 9,106 code-verifiable samples and evaluate 18 LLMs .
Outcome:	The proposed framework improves instruction following performance without compromising general performance.

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Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, Yang Liu

Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria (2025.acl-long)

Challenge:	Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination.
Approach:	They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references.
Outcome:	Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting.

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Yongqi Leng, Renren Jin, Yue Chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong

Challenge:	Existing evaluation methods are inadequate to evaluate large language models (LLMs).
Approach:	They propose a fine-grained generative LLM evaluator with instance-level customazable evaluation criteria that can be used to evaluate large language models.
Outcome:	The proposed model outperforms existing LLM evaluators and instruction-tuned LLMs on multiple benchmarks and sets new SOTA results.

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)

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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong

When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents? (2026.acl-long)

Challenge:	Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages.
Approach:	They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English.
Outcome:	The proposed model outperforms open-source and Tibetan-focused models on diverse tasks.

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Yibo Peng, James Song, Lei Li, Xinyu Yang, Mihai Christodorescu, Ravi Mangal, Corina S. Pasareanu, Haizhong Zheng, Beidi Chen

How does Misinformation Affect Large Language Model Behaviors and Preferences? (2025.acl-long)

Challenge:	Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses on functional correctness.
Approach:	They propose to attack functionally correct yet vulnerable (FCV) patches by combining multi-turn reasoning with tool invocation and environment interaction.
Outcome:	The proposed FCV-Attack achieves an attack success rate of 40.7% on GPT-5 Mini + OpenHands.

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Miao Peng, Nuo Chen, Jianheng Tang, Jia Li

Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense (2025.naacl-long)

Challenge:	Existing studies have explored the role of Large Language Models in combating misinformation, but there is still a lack of detailed analysis on the specific aspects and extent to which LLMs are influenced by misinformation.
Approach:	They propose to use a benchmark to evaluate LLMs' behavior and knowledge preference toward misinformation to identify their models.
Outcome:	The proposed approach is based on 10,346,712 pieces of misinformation and examines knowledge conflicts and stylistic variations.

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Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou

Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)

Challenge:	Existing methods to defend against jailbreak attacks exploit vulnerabilities to elicit unintended or harmful outputs.
Approach:	They propose a method to defend against jailbreak attacks by patching specific layers within large language models through self-augmented datasets.
Outcome:	The proposed approach reduces harmfulness and attack success rate of jailbreak attacks without compromising utility for benign queries compared to previous methods.

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Zezhong Xu, Wen Zhang, Peng Ye, Lei Liang, Huajun Chen

End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching (2022.emnlp-main)

Challenge:	Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities .
Approach:	They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query .
Outcome:	The proposed framework addresses embedding of emerging entities through contextual information aggregation.

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Chi Chen, Peng Li, Maosong Sun, Yang Liu

MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models (2026.findings-acl)

Challenge:	Existing unsupervised vision-and-language pre-training methods take pre-extracted region-based visual features from external object detectors, which limits flexibility and reduces computational efficiency.
Approach:	They propose an unsupervised vision-and-language pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features.
Outcome:	The proposed approach outperforms existing methods and obtains state-of-the-art results on four vision-and-language tasks.

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Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Kyle Lam, Bang Zheng, Lin Li, Jianing Qiu

Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)

Challenge:	Existing benchmarks for multimodal large language models do not capture real-world clinical complexity.
Approach:	They evaluate multilingual, multimodal multimodal models of clinical cases with up to 7 distinct visual clinical evidence types per case.
Outcome:	The proposed model outperforms human models on differential diagnosis (DDx) generation and final diagnosis (FDx) selection.

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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang

Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)

Challenge:	Recent advances in model editing for LLMs have created challenges and opportunities for the community.
Approach:	They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs.
Outcome:	The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs.

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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)

Challenge:	Empirical evidence shows that our proposed method improves performance across seven downstream tasks.
Approach:	They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data.
Outcome:	The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks.

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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang

Worldwide LiveVQA: Real-Time Visual Knowledge Seeking and Updating Across Languages (2026.findings-acl)

Challenge:	Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers.
Approach:	They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training.
Outcome:	The proposed model is open-source and transparent, with no data or data required to build it.

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Xuanao Huang, Xingjia Liu, Zetong Zhou, Yuyang Peng, Yao Wan, Dongping Chen

Visual Data in NLP

Challenge:	Existing resources for visual knowledge remain confined to English, creating a "Worldwide Knowledge Gap" eric liu: production and dissemination of knowledge exhibit a distinct trend toward decentralization and linguistic fragmentation.
Approach:	They propose a dataset for multilingual visual knowledge seeking and updating across ten major languages.
Outcome:	The proposed dataset is the first dynamic-updating dataset for multilingual visual knowledge seeking and updating across ten major languages.

HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction (2025.emnlp-industry)

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Ziheng Ni, Congcong Liu, Yuying Chen, Zhiwei Fang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao

MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)

Challenge:	Existing approaches fuse long-term behavioral profiles and short-term interactions, suffering from representational misalignment and noise in transient signals.
Approach:	They propose a framework that redefines interest fusion as a hierarchical denoising process through diffusion models.
Outcome:	The proposed framework redefines interest fusion as a hierarchical denoising process through diffusion models.

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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou

Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)

Challenge:	Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph.
Approach:	They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text.
Outcome:	The proposed model improves the quality of generated text and has robust superiority over other methods.

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Tongtong Liu, Zhaohui Wang, Meiyue Qin, Zenghui Lu, Xudong Chen, Yuekui Yang, Peng Shu

DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion (2020.emnlp-main)

Challenge:	Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction.
Approach:	They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time.
Outcome:	The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions.

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Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp

Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement (2025.findings-acl)

Challenge:	Existing embedding approaches for temporal knowledge graphs typically learn entity representations and their dynamic evolution in the Euclidean space.
Approach:	They propose a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds.
Outcome:	The proposed model improves on three real-world datasets showing that the embeddings on Riemannian manifolds can capture the evolution of temporal KGs.

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Peng Ding, Jun Kuang, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Jiajun Chen, Shujian Huang

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks but are vulnerable to meticulously crafted jailbreak attacks.
Approach:	They propose a training-free defense strategy to align LLMs’ strong safety discrimination performance with their relatively weaker safety generation ability.
Outcome:	The proposed strategy achieves an average 99% success rate against numerous complex and covert jailbreak methods while maintaining helpfulness on general benchmarks.

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)

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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen

LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)

Challenge:	Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals.
Approach:	They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training.
Outcome:	The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training .

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Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu

Challenge:	Large language model (LLM) routing assigns each query to the best suitable model from an ensemble.
Approach:	They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation .
Outcome:	The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing.

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)

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Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

Challenge:	Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models.
Approach:	They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification.
Outcome:	The proposed framework surpasses all baselines on 8 datasets and 5 LLMs.

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Task-oriented Dialogue System for Automatic Diagnosis (P18-2)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai

Natural Language Interfaces

Challenge:	Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another.
Approach:	They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports.
Outcome:	The proposed system can collect additional symptoms from conversation and improve disease identification accuracy.

Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)

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Xiuyi Chen, Feilong Chen, Fandong Meng, Peng Li, Jie Zhou

MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning (2026.acl-long)

Challenge:	Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues.
Approach:	They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision.
Outcome:	The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses.

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Tao Zhang, Ziqian Zeng, Hao Peng, Huiping Zhuang, Cen Chen

MoLA: MoE LoRA with Layer-wise Expert Allocation (2025.findings-naacl)

Challenge:	Existing low-bit quantization methods often exhibit severe performance degradation on complex reasoning tasks.
Approach:	They propose a plug-and-play method that uses a key channel's intrinsic quantization difficulty and relevance to the query to identify and preserve critical key channels that need higher precision.
Outcome:	Experiments on complex reasoning datasets show that the proposed method outperforms low-bit methods at a substantially reduced memory footprint.

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Chongyang Gao, Kezhen Chen, Jinmeng Rao, Ruibo Liu, Baochen Sun, Yawen Zhang, Daiyi Peng, Xiaoyuan Guo, Vs Subrahmanian

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)

Challenge:	Recent efforts to integrate low-rank adaptation (LoRA) with the Mixture-of-Experts (MoE) have achieved performance comparable to full-parameter fine-tuning by tuning much fewer parameters.
Approach:	They propose a parameter-efficient MoE method for low-rank adaptation with the Mixture-of-Experts (MoE) they use layers of LoRA experts to allocate more LoRA expert to middle layers .
Outcome:	The proposed method outperforms baseline models on six well-known NLP and commonsense QA benchmarks on LLAMA-2, Mistral, and Gemma.

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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie

Challenge:	FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions .
Approach:	They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures .
Outcome:	The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes.

ReGLA: Refining Gated Linear Attention (2025.naacl-long)

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Peng Lu, Ivan Kobyzev, Mehdi Rezagholizadeh, Boxing Chen, Philippe Langlais

LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval (2026.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) are known for their computational and storage requirements due to the quadratic computation complexity of softmax attention.
Approach:	They propose to reduce the quadratic computation complexity of softmax attention by using feature maps, normalization and the gating mechanism to improve performance.
Outcome:	The proposed model outperforms existing gated linear attention models in extensive tasks including training from scratch and post-linearization with continual pre-training.

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Hang Wang, Utkarsh Garg, Reza Davari, Huitian Jiao, Hao Cheng, Baolin Peng, Si-Qing Chen, Tao Ge

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Document editing requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency.
Approach:	a framework that constructs lightweight dependency graphs captures semantic relationships and structural hierarchies across document elements is proposed for agentic document editing . a scaLing agentic agentic framework is based on a dependency graph framework that captures dependencies and refactors function dependencies.
Outcome:	a new framework achieves 76 consistency versus 56 baseline while reducing token usage by 85 . the framework is based on a framework that captures semantic relationships and structural hierarchies across document elements . it can be used to improve document consistency, but it also reduces token costs and latency .

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts (2022.naacl-main)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Rujun Han, Hong Chen, Yufei Tian, Nanyun Peng

On Collaborating Small and Large Models For Few-shot Intent Detection (2025.findings-emnlp)

Challenge:	Existing systems that generate flashbacks are monotonic and lack explicit guidance on how to insert them.
Approach:	They propose to use event temporal orders to encode events as temporal prompts . they leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines .
Outcome:	The proposed method generates more interesting stories with flashbacks while maintaining textual diversity, fluency, and temporal coherence.

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Peng Chen, Bang Wang

Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations (2026.findings-acl)

Challenge:	Existing methods for few-shot intent detection face high inference cost and label interference.
Approach:	They propose a framework that integrates a small prediction model with a large language model for FSID.
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

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Xinyu Zhao, Kangqi Ni, Jie Peng, Ang Li, Tianlong Chen

Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)

Challenge:	Multimodal Large Language Models (MLLMs) have potential for cross-modal understanding . but extending MLLM to handle diverse modalities introduces two challenges .
Approach:	They propose a dual-stage compression mechanism to reduce the number of modality tokens per modality and condense it into a single, compact token sequence.
Outcome:	Experiments show that Flex-M3 outperforms its counterpart trained on only full-modality data.

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Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen

Natural Language Interfaces

Challenge:	Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles .
Approach:	They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning .
Outcome:	The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks.

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification (2021.acl-long)

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Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)

Challenge:	Existing methods for event causality identification (ECI) rely on annotated training data.
Approach:	They propose a method to augment training data for event causality identification by iteratively generating new examples and classifying event causalities in a dual learning framework.
Outcome:	The proposed method outperforms existing methods on EventStoryLine and Causal-TimeBank.

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Dengyun Peng, Yuhang Zhou, Qiguang Chen, JinHao Liu, Jingjing Chen, Libo Qin, Wanxiang Che

Hierarchical User Intent Inference with Knowledge Graph Grounding (2026.findings-eacl)

Challenge:	Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization.
Approach:	They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization.
Outcome:	The proposed methods integrate deep learning paradigms into text-based gradient optimization.

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Tzu-Cheng Peng, Chien Chin Chen, Yung-Chun Chang

Knowledge Representation

Challenge:	Existing large language models lack structured grounding and do not capture nuanced intent expression.
Approach:	They propose a Hierarchical Intent Inference framework that first predicts fine-grained aspect ratings and then generates natural language intent statements guided by contextual subgraphs retrieved from a domain-specific knowledge graph.
Outcome:	The proposed framework outperforms strong LLM and encoder-based baselines on a hotel review dataset.

CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)

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Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu

On the Impact of Cross-Domain Data on German Language Models (2023.findings-emnlp)

Challenge:	Multimodal large language models have demonstrated promising results in a variety of tasks that combine vision and language.
Approach:	They propose a benchmark to assess the ability of models to use contextual information in free-form text to enhance visual comprehension.
Outcome:	The proposed model fails to extract and utilize contextual information to improve understanding of images.

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Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu

FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness (2025.findings-acl)

Challenge:	Traditionally, large language models have been trained on general web crawls or domain-specific data.
Approach:	They present a German dataset and a dataset aimed at containing high-quality data to examine the importance of data diversity over quality.
Outcome:	The proposed model outperforms models trained on quality data on multiple downstream tasks.

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Yuanjian Xu, Jianing Hao, Kunsheng Tang, Jingnan Chen, Anxian Liu, Peng Liu, Guang Zhang

Multimodal Chemical Structure-Text Coreference in Intellectual Property via Rule-guided Reinforcement Learning (2026.findings-acl)

Challenge:	Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities.
Approach:	They propose a framework that empowers large language models to analyze ripple effects . they use financial theory-guided large-scale reinforcement learning to align LLMs with the market .
Outcome:	The proposed framework allows LLMs to analyze ripple effects through financial theory-guided large-scale reinforcement learning.

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Hanmeng Zhong, Wentao Wu, Linqing Chen, Peng Zhou

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing tools for identifying chemical structures and textual referents are inadequate for this multimodal task.
Approach:	They propose a RULE-guided multimodal Reinforcement learning framework for chemical structure-text coreference . RULER is a rule-driven reinforcement learning framework that uses rule-based reward functions to obtain the correct domain knowledge.
Outcome:	The proposed framework improves on the baseline framework and shows superior efficacy.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024.acl-long)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Huang Lei, Jiaming Guo, Guanhua He, Xishan Zhang, Rui Zhang, Shaohui Peng, Shaoli Liu, Tianshi Chen

Challenge:	Generating long-term texts using artificial intelligence has always been a challenge . however, the generated novels exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality.
Approach:	They propose a method for extracting excelsior and expanding from novel data to generate arbitrarily long novels using large language models.
Outcome:	The proposed method produces high-quality long-form novels with a high level of logical coherence and appeal despite the use of large language models.

QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm (2025.findings-acl)

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Qirui Zhou, Shaohui Peng, Weiqiang Xiong, Haixin Chen, Yuanbo Wen, Haochen Li, Ling Li, Qi Guo, Yongwei Zhao, Ke Gao, Ruizhi Chen, Yanjun Wu, Zhao Chen, Yunji Chen

Programming Languages in NLP

Challenge:	Existing LLMs cannot comprehend the complex data flow and computation process of the attention operator and utilize low-level primitive to exploit GPU performance.
Approach:	They propose an LLM-friendly Thinking Language (LLM-TL) that can decouple the generation of high-level optimization logic and low-level implementation on GPU and enhance LLMs’ understanding of attention operator.
Outcome:	The proposed method outshines existing LLMs on A100, RTX8000, and T4 GPUs, achieving a speed-up of up to 35.16.

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Wenqiang Wang, Peng Chen, Yan Xiao, Yangshijie Zhang, Xiaoyue Lu, Jianjie Huang, Xiaochun Cao

Self-supervised Cross-modal Pretraining for Speech Emotion Recognition and Sentiment Analysis (2022.findings-emnlp)

Challenge:	Standard in-context learning assumes identical output spaces between test and retrieval datasets . however, in practice, these datasets can be fully aligned, partially alignes, or fully disjoint in label space .
Approach:	They propose a framework for in-context learning under output-space mismatch . they identify demonstrations relevant to the test label space via a Bayesian probabilistic criterion .
Outcome:	The proposed framework achieves state-of-the-art results across three LLMs, three task types, and four datasets.

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Iek-Heng Chu, Ziyi Chen, Xinlu Yu, Mei Han, Jing Xiao, Peng Chang

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

Challenge:	Existing approaches to multimodal speech emotion recognition and sentiment analysis have not improved results due to their relatively simple fusion mechanisms and lack of proper cross-modal pretraining.
Approach:	They propose a deep-fused audio-text bi-modal transformer with carefully designed cross-modal fusion mechanism and stage-wise cross-mod pretraining scheme to facilitate cross-modulation.
Outcome:	The proposed method exceeds benchmarks on public IEMOCAP emotion and CMU-MOSEI sentiment datasets by a large margin.

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection (2026.acl-long)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Jinhan Liu, Yibo Yang, Ruiying Lu, Piotr Piękos, Yimeng Chen, Peng Wang, Dandan Guo

Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly (2024.naacl-long)

Challenge:	Existing likelihood-based methods for detecting pretraining data are limited in black-box, zero-shot settings.
Approach:	They propose a training-free and plug-and-play framework that reweights token-level scores to amplify distinct signals from early positions while suppressing noise from later ones.
Outcome:	The proposed framework amplifys signals from early positions while suppressing noise from later positions.

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Changjiang Gao, Hongda Hu, Peng Hu, Jiajun Chen, Jixing Li, Shujian Huang

EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)

Challenge:	Current large language models show imbalance abilities in different languages . authors propose two approaches to improve cross-lingual knowledge alignment .
Approach:	They propose a framework to assess cross-lingual knowledge alignment of large language models . they propose multilingual pretraining and multilingual instruction tuning to address this problem .
Outcome:	The proposed framework assesses the cross-lingual knowledge alignment of LLMs in performance, consistency and conductivity levels.

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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen

Finetuning Pretrained Transformers into RNNs (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
Approach:	They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs.
Outcome:	The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization.

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Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models (2025.emnlp-main)

Challenge:	Efficient transformers outperform recurrent neural networks in natural language generation, but this comes with significant computational cost and memory footprint during generation.
Approach:	They propose to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy.
Outcome:	The proposed transformers outperform recurrent neural networks in natural language generation but come with significant computational and memory footprint during generation.

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Peng Ding, Wen Sun, Dailin Li, Wei Zou, Jiaming Wang, Jiajun Chen, Shujian Huang

MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)

Challenge:	Large Language Models (LLMs) excel at various tasks but are vulnerable to jailbreak attacks that induce harmful content generation.
Approach:	They propose a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement.
Outcome:	The proposed framework improves model safety by iterative self-improvement without additional annotated data or external models during training phase.

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Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

Visual Data in NLP

Challenge:	Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks .
Approach:	They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps.
Outcome:	The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios.

CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition (2022.lrec-1)

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Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi, Peng Xu, Cheuk Tung Yiu, Rita Frieske, Holy Lovenia, Genta Winata, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung

EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs (2025.coling-main)

Challenge:	In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety.
Approach:	They propose a dataset for in-car command recognition in the cantonese language with both video and audio data.
Outcome:	The proposed model can achieve a considerable quality on the clean test set, but the speech recognition quality on noisy data is still inferior.

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Zixuan Dong, Baoyun Peng, Yufei Wang, Jia Fu, Xiaodong Wang, Xin Zhou, Yongxue Shan, Kangchen Zhu, Weiguo Chen

Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

Challenge:	Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLM or suffer from prohibitive computational costs due to tight coupling.
Approach:	They propose a framework that can strike a balance between performance and efficiency via an iterative paradigm.
Outcome:	The proposed framework can strike a balance between performance and efficiency via an iterative paradigm.

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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen

ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022.lrec-1)

Challenge:	Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach:	They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome:	The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.

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Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung

Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)

Challenge:	Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation.
Approach:	They propose to collect Mandarin Chinese-English code-switching corpus from read speech rather than spontaneous speech to address this phenomenon.
Outcome:	ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English.

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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)

Challenge:	Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages.
Approach:	They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems.
Outcome:	The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function.

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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao

Towards Robust Speech Representation Learning for Thousands of Languages (2024.emnlp-main)

Challenge:	Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence.
Approach:	They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory.
Outcome:	The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%.

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William Chen, Wangyou Zhang, Yifan Peng, Xinjian Li, Jinchuan Tian, Jiatong Shi, Xuankai Chang, Soumi Maiti, Karen Livescu, Shinji Watanabe

Decoupling Memories, Muting Neurons: Towards Practical Machine Unlearning for Large Language Models (2025.findings-acl)

Challenge:	XEUS is a cross-lingual encoder for universal speech that can be trained on 1 million hours of data across 4057 languages.
Approach:	They propose a Cross-lingual Encoder for Universal Speech that can be trained on 1 million hours of data across 4057 languages and a newly created corpus of 7400+ hours from 4057 .
Outcome:	The proposed model outperforms state-of-the-art models on several benchmarks and outperfies MMS 1B and w2v-BERT 2.0 v2 by 0.8% and 4.4% respectively.

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Lishuai Hou, Zixiong Wang, Gaoyang Liu, Chen Wang, Wei Liu, Kai Peng

When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026.findings-acl)

Challenge:	Existing methods for MU degrade model utility, especially when accessing the original training data.
Approach:	They propose a method that eliminates the influence of unlearned data by modulating the outputs of merely 1% of the neurons in the feed-forward network modules within the Transformer blocks.
Outcome:	The proposed method eliminates the influence of unlearned data from Large Language Models by modulating the outputs of 1% of the neurons in the feed-forward network modules within the Transformer blocks, minimizing disruption to the model’s performance.

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Zewen Long, Yu Peng, Fangming Dong, Congyi Li, Xingmao Guan, Shu Wu, Kai Chen

PwnGPT: Automatic Exploit Generation Based on Large Language Models (2025.acl-long)

Challenge:	Existing safety alignment methods rely on fixed or narrow transformation schemes to generalize . existing methods based on fixed and narrow transformations are often inadequate .
Approach:	They propose a framework for discovering and refining language game-based jailbreaks to probe alignment generalization.
Outcome:	The proposed framework allows controlled exploration of alignment behavior across closely related linguistic variants.

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Wanzong Peng, Lin Ye, Xuetao Du, Hongli Zhang, Dongyang Zhan, Yunting Zhang, Yicheng Guo, Chen Zhang

Language Agnostic Multilingual Information Retrieval with Contrastive Learning (2023.findings-acl)

Challenge:	Automated exploit generation (AEG) is the automatic discovery and exploitation of vulnerabilities against unknown targets.
Approach:	They propose an automatic exploit generation framework that automatically solves pwn challenges by using large language models.
Outcome:	The proposed framework improves the completion rate of exploits on the openAI o1-preview model and the GPT-4o model.

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Xiyang Hu, Xinchi Chen, Peng Qi, Deguang Kong, Kunlun Liu, William Yang Wang, Zhiheng Huang

Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach (2024.findings-acl)

Challenge:	Annotated training data is costly to obtain in many languages .
Approach:	They propose a semantic contrastive loss to align parallel sentences that share the same semantics in different languages and a language contrastive gain to leverage parallel sentence pairs to remove language-specific information from non-parallel corpora.
Outcome:	The proposed model improves retrieval performance while requiring less computational effort.

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Jingyuan Yang, Dapeng Chen, Yajing Sun, Rongjun Li, Zhiyong Feng, Wei Peng

Fully Hyperbolic Neural Networks (2022.acl-long)

Challenge:	Large Language Models generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt.
Approach:	They propose to refine a Large Language Model (LLM) with prompt-output pairs with equivalent semantics to achieve semantic consistency.
Outcome:	The proposed method improves the semantic consistency and task performance of LLMs.

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Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space.
Approach:	They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks.
Outcome:	The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models .

SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures (2026.findings-acl)

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Jiaming Wang, Zhe Tang, Zehao Jin, Hefei Chen, Yilin Jin, Peng Ding, Xiaoyu Li, Xuezhi Cao

Dense Procedure Captioning in Narrated Instructional Videos (P19-1)

Challenge:	Large language models (LLMs) are widely deployed as domain-specific agents, but evaluation of their capabilities in such contexts has not been fully explored.
Approach:	They propose a benchmark to evaluate LLMs' ability to follow instructions and make decisions in real-world scenarios.
Outcome:	The proposed benchmark is constructed from real-world business data and adapted into 23 complex SOP scenarios.

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Botian Shi, Lei Ji, Yaobo Liang, Nan Duan, Peng Chen, Zhendong Niu, Ming Zhou

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)

Challenge:	Existing models for video dense captioning learn video segments and generate captions without considering transcripts.
Approach:	They propose a model to generate procedure captions from narrated instructional videos . they extract procedures by a cross-modality module and generate captions by encoding video frames and transcripts within each extracted procedure.
Outcome:	The proposed model can extract procedures from narrated instructional videos and generate procedure captions by encoding video frames and transcripts.

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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang

MIND: Towards Immersive Psychological Healing with Multi-Agent Inner Dialogue (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge.
Approach:	They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass.
Outcome:	The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively.

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Yujia Chen, Changsong Li, Yiming Wang, Tianjie Ju, Qingqing Xiao, Nan Zhang, Zifan Kong, Peng Wang, Binyu Yan

Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

Challenge:	Mental health issues are worsening in today’s competitive society, such as depression and anxiety.
Approach:	They propose a multi-agent inner dialogue paradigm that provides more immersive psychological healing environments.
Outcome:	The proposed paradigm provides more immersive psychological healing environments.

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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo

MirrorQA: Benchmarking Multimodal LLMs on Mirror-Orientation Reasoning (2026.acl-long)

Challenge:	Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
Approach:	They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Outcome:	The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.

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Jingping Liu, Xingchen Peng, Yan Zhou, Ziyan Liu, Jie Zhai, Ronghao Chen, Huacan Wang, Xiaofeng Jia

Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning (2025.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) have achieved remarkable progress in recent years, yet their ability to perform left–right reasoning in mirror contexts remains underexplored.
Approach:	They propose a benchmark to evaluate MLLMs' ability to distinguish left from right from a subject-centered perspective.
Outcome:	The proposed benchmarks show that even the best performing models achieve only 65.40% accuracy, far below the 99.28% accuracy of humans.

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Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang, Sirui Song, Yuhao Zhou, Xinbo Zhang, Peng Sun, Hong Lu, Tao Gui, Qi Zhang, Xuanjing Huang

Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation (2021.findings-acl)

Challenge:	Existing work focuses on enabling models to generate natural language chain-of-thought rationales or leverage executable and verifiable code, such as Python.
Approach:	They propose a novel training pipeline that integrates sequential P-CoT and N-Co T generation and a subtask hybrid training strategy to facilitate natural language transferability.
Outcome:	The proposed training pipeline improves both N-CoT and P-Co T performance over the RL baseline.

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Feilong Chen, Fandong Meng, Xiuyi Chen, Peng Li, Jie Zhou

CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory (2025.acl-long)

Challenge:	Existing studies focus on implicit exploration of multimodal coreference but neglect the importance of locating the objects explicitly in the visual content, which is associated with textual entities.
Approach:	They propose a multimodal incremental transformer with visual grounding which aims to explicitly locate related objects in the image guided by textual entities.
Outcome:	The proposed model achieves comparable performance on the VisDial v0.9 and v1.0 datasets.

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Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

Challenge:	Existing ground VLN agents struggle in aerial VLLN due to the lack of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration.
Approach:	They propose a large language model-empowered aerial VLN agent that decomposes the long-horizon task into sub-goals with different semantic levels.
Outcome:	The proposed method achieves state-of-the-art performance with significant improvement in continuous city environments.

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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

Exploring Reasoning Reward Model for Agents (2026.findings-acl)

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Kaixuan Fan, Kaituo Feng, Manyuan Zhang, Tianshuo Peng, Zhixun Li, Yilei Jiang, Shuang Chen, Xiangyu Yue

RISK: A Framework for GUI Agents in E-commerce Risk Management (2026.acl-long)

Challenge:	Existing methods for agentic reinforcement learning rely on sparse outcome-based reward for training, leading to suboptimal results.
Approach:	They propose an agent-based reward model that produces structured feedback for agentic trajectories, including an explicit reasoning trace and a focused critique.
Outcome:	The proposed model produces structured feedback for agentic trajectories including an explicit reasoning trace, a focused critique, and an overall score that evaluates process performance.

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Renqi Chen, Zeyin Tao, Jianming Guo, Jingzhe Zhu, Yiheng Peng, Qingqing Sun, Tianyi Zhang, Shuai Chen

Challenge:	RISK is a framework designed to automate multi-step web interactions in e-commerce risk management.
Approach:	a new framework is designed to build and deploy GUI agents for e-commerce risk management . RISK-R1 provides a scalable, domain-specific solution for automating complex web interactions .
Outcome:	RISK provides a scalable, domain-specific solution for automating complex web interactions in e-commerce risk management.

CNEQ: Incorporating numbers into Knowledge Graph Reasoning (2024.findings-emnlp)

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Xianshu Peng, Wei Wei, Kaihe Xu, Dangyang Chen

Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)

Challenge:	Complex query answering (CQA) is a task that addresses semantics of numerical entities.
Approach:	They propose a model that includes a Number-Entity Predictor and an Entity Filter . they use three widely-used Knowledge Graphs to perform reasoning over knowledge graphs .
Outcome:	The proposed model can predict entities and numerical values better than existing models . it compares or filters out entities that meet certain constraints on three widely-used Knowledge Graphs .

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Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu

SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)

Challenge:	Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs.
Approach:	They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning.
Outcome:	The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query.

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Ming Chen, Wenyao Li, Chao Liang, Shi Gu, Peng Lin, De Ma, Huajin Tang, Qian Zheng, Gang Pan

A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)

Challenge:	Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks .
Approach:	They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results.
Outcome:	The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance.

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Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, Shujian Huang

X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System (2025.findings-acl)

Challenge:	Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency.
Approach:	They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts.
Outcome:	The proposed framework improves the attack success rate while reducing the time cost compared to baselines.

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Peng Wang, Ruihan Tao, Qiguang Chen, Mengkang Hu, Libo Qin

Multilinguality

Challenge:	X-WebAgentBench evaluates the planning and interaction performance of language agents across multiple languages.
Approach:	They propose a multilingual agent benchmark that evaluates the interaction performance of language agents across multiple languages.
Outcome:	The proposed benchmark evaluates the planning and interaction performance of language agents across multiple languages.

BalanceSFT: Improving LLM Function Calling with Balanced Training Signals and Data Hardness (2026.findings-acl)

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Bingguang Hao, Zengzhuang Xu, Maolin Wang, Yuntao Wen, Yicheng Chen, Cunyin Peng, Long Chen, Xiangyu Zhao, Jinjie Gu, Chenyi Zhuang, Ji Zhang

AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems (2026.acl-long)

Challenge:	Currently, Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, but its effectiveness is often compromised by two challenges: 1) lengthy Chain-of-Thought (CoT) reasoning tokens dominate training signals over concise function calls in the learning objective; 2) scarcity of hard training examples.
Approach:	They propose a framework that uses a self-adjusted signal balancing loss and a hard data re-sampling strategy to selectively generate new, high-quality complex data guided by model errors.
Outcome:	The proposed framework surpasses state-of-the-art models like GPT-5 in function calling performance.

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Yulang Chen, Haoxuan Peng, Jinyan Liu, Zichen Wen, Dongrui Liu, Linfeng Zhang

Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions (2023.findings-emnlp)

Challenge:	Automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption.
Approach:	They propose a plug-and-play compression framework for graph-structured multi-agent workflows . they estimate the importance score of each agent and remove redundant agents .
Outcome:	Experiments show that AgentSlimming reduces average token cost by 78.9% with negligible performance degradation.

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Ziyue Wang, Chi Chen, Peng Li, Yang Liu

Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking (2023.eacl-main)

Challenge:	Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge in natural language tasks.
Approach:	They propose a framework that enables LLMs to ask relevant questions to uncover more details in the image, along with filters for refining the generated information.
Outcome:	The proposed framework boosts the performance of baseline methods by 2.15% on OK-VQA and achieves consistent improvements across different LLMs.

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Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock

Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)

Challenge:	Existing scientific fact-checking datasets are limited due to expertise bottleneck . multi2Claim pipeline is a tool to convert multiple-choice questions into fact- checking data .
Approach:	They propose a pipeline for automatically converting multiple-choice questions into fact-checking data . they generate two large-scale datasets for scientific-fact-checker tasks . success at this task can help the reader understand scientific topics and promote science .
Outcome:	The proposed pipeline improves performance on two large-scale scientific fact-checking datasets.

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Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, Yuguang Chen, Weihua Peng

A Framework of Reflective Agents with Adaptive Collaboration for Attributed Summary Generation (2026.findings-acl)

Challenge:	Existing methods for identifying causal relations of events are limited . Existing approaches cannot handle well the problem, especially in the condition of lacking training data.
Approach:	They propose a Latent Structure Induction Network to integrate external structural knowledge into a causality reasoning task.
Outcome:	The proposed approach outperforms existing state-of-the-art methods on two widely used datasets.

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Yu Chen, Peng Chen, Ziwei Zheng, Bang Wang

SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings (2025.acl-long)

Challenge:	Existing prompt-based summarization approaches face limitations such as positional preference, poor citation quality and sensitivity to uninformative documents.
Approach:	They propose a framework of Reflective Agents with Adaptive Collaboration for attributed summarization that performs iterative summarizing via reflective agents’ collaboration.
Outcome:	The proposed framework outperforms baselines on the ALCE benchmark in factual correctness and citation quality.

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Weikai Lu, Hao Peng, Huiping Zhuang, Cen Chen, Ziqian Zeng

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations (2026.findings-acl)

Challenge:	Existing low-resource security alignment methods struggle with the security risks posed by additional modalities.
Approach:	They propose to use multimodal datasets to enhance safety alignment but it is costly to construct these datasets.
Outcome:	Experiments on image, video, and audio-based MLLMs show that the proposed method can synthesize a high-quality embedding on a single RTX3090 GPU within 24 seconds.

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Pingjun Hong, Beiduo Chen, Siyao Peng, Marie-Catherine de Marneffe, Benjamin Roth, Barbara Plank

Unsupervised Morphological Paradigm Completion (2020.acl-main)

Challenge:	Natural Language Inference (NLI) datasets often exhibit label variation.
Approach:	They extend LiTEx taxonomy to two NLI datasets and jointly analyze label variation and label variation.
Outcome:	The proposed model combines explanations as a lens to analyze variation in NLI annotations and examine individual differences in reasoning.

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Huiming Jin, Liwei Cai, Yihui Peng, Chen Xia, Arya McCarthy, Katharina Kann

NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)

Challenge:	a task of generating morphological paradigms is a challenging unsupervised task for natural language processing systems . acuidados y acciones del idioma es a problem in linguistic annotators.
Approach:	They propose a task of unsupervised morphological paradigm completion using raw text and a lemma list.
Outcome:	The proposed system outperforms trivial baselines on 14 typologically diverse languages with ease and higher accuracy than minimally supervised systems.

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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)

Challenge:	a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Approach:	They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Outcome:	The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains.

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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu

ToolGate: Contract-Grounded and Verified Tool Execution for LLMs (2026.findings-acl)

Challenge:	Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry.
Approach:	They propose to summarize advanced methods through a taxonomy that offers novel perspectives.
Outcome:	The proposed method delineates the challenges and future directions, thereby shedding light on future research.

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Yanming Liu, Xinyue Peng, Jiannan Cao, Xinyi Wang, Songhang Deng, Jintao Chen, Jianwei Yin, Xuhong Zhang

MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction (P19-1)

Challenge:	Existing frameworks for tool-augmented LLMs rely heavily on natural language reasoning to determine when tools can be invoked and whether their results should be trusted.
Approach:	They propose a forward execution framework that provides logical safety guarantees and verifiable state evolution for LLM tool calling.
Outcome:	The proposed framework improves the reliability and verifiability of tool-augmented LLM systems while maintaining competitive performance on multi-step reasoning tasks.

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Pengcheng Yang, Fuli Luo, Peng Chen, Tianyu Liu, Xu Sun

Machine Translation

Challenge:	Existing work shows that morphological variation is an intractable challenge for the unsupervised bilingual lexicon induction task.
Approach:	They propose a morphology-aware alignment model to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model.
Outcome:	The proposed model outperforms state-of-the-art unsupervised systems and achieves competitive performance compared to supervised methods.

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)

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Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye

CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

Challenge:	Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks.
Approach:	They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs.
Outcome:	The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks.

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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)

Challenge:	Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach:	They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome:	Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.

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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li

The Dominance of Text Space: Unveiling the Asymmetric Nature of Cross-Modal Alignment in Large Language Models (2026.acl-long)

Challenge:	Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) .
Approach:	They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries.
Outcome:	The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer.

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Linqing Chen, Hanmeng Zhong, Wentao Wu, Peng Zhou

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

Challenge:	Existing methods for cross-modal alignment assume a symmetric interaction between visual and textual modalities, implying that both spaces adapt to each other.
Approach:	They propose a method that regularizes the projector to maintain the geometric structure of the text embedding space via spectral filtering.
Outcome:	The proposed method preserves the LLM’s inherent linguistic capabilities and reduces object hallucination significantly better than standard fine-tuning methods.

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

GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)

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

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Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi

Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models (2024.emnlp-main)

Challenge:	Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems.
Approach:	They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning .
Outcome:	The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models.

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Zhengxuan Wu, Yuhao Zhang, Peng Qi, Yumo Xu, Rujun Han, Yian Zhang, Jifan Chen, Bonan Min, Zhiheng Huang

Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design (2025.naacl-long)

Challenge:	Modern language models fail to follow human instructions while being faithful . a trade-off exists between instruction following and faithfulness when training LMs .
Approach:	They propose a method that relies on Reject-sampling by Self-instruct with Continued Fine-tuning to train LMs to follow human instructions while being faithful.
Outcome:	The proposed method outperforms vanilla MTL with high-quality data, but with significantly smaller data.

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Mohan Zhang, Pingzhi Li, Jie Peng, Mufan Qiu, Tianlong Chen

Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)

Challenge:	Using Mixture-of-Experts, researchers have found that efficient MoE is difficult to achieve due to two key reasons: imbalanced expert activation and massive communication overhead.
Approach:	They propose a collaboration-constrained routing strategy that encourages more specialized expert groups and leverages expert specialization.
Outcome:	The proposed approach achieves an average performance improvement of 0.51% and 0.33% on LLaMA-MoE and Qwen-MaE respectively.

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Peng Wu, Shujian Huang, Rongxiang Weng, Zaixiang Zheng, Jianbing Zhang, Xiaohui Yan, Jiajun Chen

Automatic Marketing Theme and Commodity Construction System for E-commerce (2023.emnlp-industry)

Challenge:	Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data.
Approach:	They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding.
Outcome:	The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art.

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Zhiping Wang, Peng Lin, Hainan Zhang, Hongshen Chen, Tianhao Li, Zhuoye Ding, Sulong Xu, Jinghe Hu

A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis (2020.emnlp-main)

Challenge:	Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators.
Approach:	They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme.
Outcome:	The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods.

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Zehui Dai, Cheng Peng, Huajie Chen, Yadong Ding

Weakly Supervised Vision-and-Language Pre-training with Relative Representations (2023.acl-long)

Challenge:	Sentiment analysis is an increasingly popular natural language processing task in academia and industry.
Approach:	They propose to use category name encoding network to weaken catastrophic forgetting problem . they set both encoder and decoder shared among all categories to weaker the catastrophic forgetting problem a .
Outcome:	The proposed model achieves state-of-the-art on two (T)ACSA benchmark datasets.

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Chi Chen, Peng Li, Maosong Sun, Yang Liu

Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)

Challenge:	Weakly supervised vision-and-language pre-training (WVLP) uses only local descriptions of images as cross-modal anchors to construct weakly-aligned image-text pairs for pre- training.
Approach:	They propose to take a small number of aligned image-text pairs as anchors and represent each unaligned image and text by its similarities to these anchors.
Outcome:	The proposed model reduces the cost of pre-training while maintaining decent performance on downstream tasks.

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Jie Shi, Xi Cao, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects.
Approach:	They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap.
Outcome:	The proposed framework outperforms existing methods that generate SQL queries directly.

Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)

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Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, Minlie Huang

Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)

Challenge:	Existing approaches to cognitive restructuring (CR) are limited by entrenched cognitive distortions, emotional resistance, and individual differences.
Approach:	They propose a framework that structures CR as theory-grounded multi-stage multi-turn dialogue and a multi-channel loop mechanism to account for diverse individual distortions.
Outcome:	The proposed framework integrates supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions.

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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

GoG: Relation-aware Graph-over-Graph Network for Visual Dialog (2021.findings-acl)

Challenge:	Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution.
Approach:	They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research .
Outcome:	The proposed model can be used to analyze the evolution of parametric knowledge in LLMs.

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Feilong Chen, Xiuyi Chen, Fandong Meng, Peng Li, Jie Zhou

Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)

Challenge:	Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin.
Approach:	They propose a relation-aware graph-over-graph network (GoG) for visual dialog . their model outperforms the strong baseline in both generative and discriminative settings .
Outcome:	The proposed model outperforms baseline models in both generative and discriminative settings by a significant margin.

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Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, Jie Zhou

RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)

Challenge:	Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information .
Approach:	They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection.
Outcome:	The proposed model improves on two knowledge-grounded dialogue datasets.

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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)

Challenge:	Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries.
Approach:	They propose a framework for training query rewriting models that leverages a reranker framework.
Outcome:	The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models.

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Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

Representation Learning

Challenge:	Existing methods for event causality identification (ECI) rely on labeled data, but the scale of annotated datasets is limited.
Approach:	They propose a self-supervised framework to learn context-specific causal patterns from external causal statements and adopt a contrastive transfer strategy to incorporate the learned context- specific causal patterns into the target ECI model.
Outcome:	The proposed method significantly outperforms existing methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

How Does the Disclosure of AI Assistance Affect the Perceptions of Writing? (2024.emnlp-main)

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

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Zhuoyan Li, Chen Liang, Jing Peng, Ming Yin

Beyond the Panorama: Training-Free Hierarchical Perception-Reasoning for Fine-Grained Vision in MLLMs (2026.acl-long)

Challenge:	Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows.
Approach:	They conduct an experimental study to determine whether disclosure of AI assistance in the writing process would affect people's evaluation on the quality of the writing and ranking of different writings.
Outcome:	The disclosure of AI assistance decreases the average quality ratings for argumentative essays and creative stories, and increases the quality of the writings.

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Xiaoyang Yi, Jing Chen, Li Peng, Yuru Bao, Jian Zhang

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)

Challenge:	Existing multimodal large language models (MLLMs) face challenges in fine-grained visual tasks.
Approach:	They propose a training-free hierarchical perception-reasoning framework that enhances fine-grained visual understanding by simulating human perception mechanisms.
Outcome:	The proposed framework enhances fine-grained visual understanding by simulating human perception mechanisms.

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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui

Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embeddings (2025.acl-long)

Challenge:	Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows.
Approach:	They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system.
Outcome:	Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks.

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Haomiao Tang, Jinpeng Wang, Yuang Peng, GuangHao Meng, Ruisheng Luo, Bin Chen, Long Chen, Yaowei Wang, Shu-Tao Xia

Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically (2021.emnlp-main)

Challenge:	Composed Image Retrieval (CIR) combines text and reference images to search for images . metric learning methods that focus on point embeddings fail to capture uncertainty in input data .
Approach:	They propose a framework that captures uncertainty in images and queries by Gaussian distributions in latent space rather than fixed points.
Outcome:	Experiments show that the proposed framework quantifies quality and semantic uncertainties . it can handle polysemy and ambiguity in search intentions, authors say .

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Xin Liu, Qingcai Chen, Junying Chen, Wenxiu Zhou, Tingyu Liu, Xinlan Yang, Weihua Peng

Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity (2024.findings-emnlp)

Challenge:	Existing methods to integrate knowledge into text can confuse the representation and import unexpected noises.
Approach:	They propose to leverage capsule routing to associate knowledge with medical literature hierarchically . they extract two fragments from medical literature and encode them into fragment representations .
Outcome:	The proposed method can more accurately associate knowledge with medical literature than mainstream methods.

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Michael Metel, Peng Lu, Boxing Chen, Mehdi Rezagholizadeh, Ivan Kobyzev

Challenge:	Speculative decoding uses a small draft model to generate a single input token, instead of sequentially generating tokens until completion.
Approach:	They propose a method that generates varying draft models adapted to the input context using simple rules.
Outcome:	The proposed method is competitive with the current SOTA for self-speculative decoding while being a truly plug-and-play method.

Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)

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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang

Vulnerability of Large Language Models to Output Prefix Jailbreaks: Impact of Positions on Safety (2025.findings-naacl)

Challenge:	Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine.
Approach:	They propose a task to generate term names for GO and build a large-scale benchmark dataset.
Outcome:	The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation.

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Yiwei Wang, Muhao Chen, Nanyun Peng, Kai-Wei Chang

CiteEval: Principle-Driven Citation Evaluation for Source Attribution (2025.acl-long)

Challenge:	Previous research on jailbreak attacks has focused on optimizing the adversarial snippet content injected into input prompts to expose LLM security vulnerabilities.
Approach:	They propose to use a simple adversarial snippet at the beginning of output to expose LLM security vulnerabilities.
Outcome:	The proposed approach exposes LLM security vulnerabilities much faster than input suffix attacks or prompt-based output jailbreaks.

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Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang

Challenge:	Current evaluation frameworks rely on NLI to assess binary or ternary support from cited sources, which is suboptimal for citation evaluation.
Approach:	They propose a citation evaluation framework based on fine-grained citation ratings within a broad context and construct a multi-domain benchmark with high-quality human annotations.
Outcome:	The proposed framework provides a high-quality human annotation benchmark and a suite of model-based metrics that exhibit strong correlation with human judgments.

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)

Challenge:	Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets.
Approach:	They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring.
Outcome:	The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring .

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Song Wang, Zihan Chen, Peng Wang, Zhepei Wei, Zhen Tan, Yu Meng, Cong Shen, Jundong Li

Text Clustering

Challenge:	Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data.
Approach:	They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy.
Outcome:	The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks.

Adaptive Hinge Balance Loss for Document-Level Relation Extraction (2023.findings-emnlp)

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Jize Wang, Xinyi Le, Xiaodi Peng, Cailian Chen

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025.acl-long)

Challenge:	Document-Level Relation Extraction is a multi-label classification task . however, most entity pairs do not express any relations .
Approach:	They propose to use a distance between the classification threshold and predicted score to reduce the imbalance problem by balancing the easy negatives.
Outcome:	The proposed method is superior to other methods, the authors show . it reduces the threshold to an appropriate value, and increases quadratically with the number of entities.

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Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao, Kaipeng Zhang, Ping Luo

Character-centric Story Visualization via Visual Planning and Token Alignment (2022.emnlp-main)

Challenge:	Quantization-aware training (QAT) is a low-bit training solution that requires substantial training resources.
Approach:	They propose an algorithm that reduces memory consumption by low-bit representations with minimal accuracy loss.
Outcome:	EfficientQAT achieves 2-bit Llama-2-70B model on single GPU in 41 hours . compared to previous methods, it obtains model with less than 3 points accuracy degradation .

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Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, Nanyun Peng

Visual Data in NLP

Challenge:	Story visualization is a task that requires machines to understand long text inputs and produce a globally consistent image sequence that illustrates the contents of the story.
Approach:	They propose to augment VQ-VAE with a text-to-visual-token (transformer) architecture to enable multiple image generation based on a complete story.
Outcome:	The proposed method excels at preserving characters and produces higher quality image sequences compared with baselines.

LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference (2025.emnlp-main)

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Pingjun Hong, Beiduo Chen, Siyao Peng, Marie-Catherine de Marneffe, Barbara Plank

STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing evidence of human label variation in Natural Language Inference (NLI) however, within-label variation is an additional challenge.
Approach:	They propose a linguistically-informed taxonomy for categorizing free-text explanations in English that captures different reasoning strategies behind NLI explanations with a particular focus on within-label variation.
Outcome:	The proposed taxonomy can be used to classify explanations in English using a linguistically-informed taxonomies.

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Peng Yu, En Xu, Bin Chen, Haibiao Chen, Yinfei Xu

Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024.naacl-long)

Challenge:	Existing reasoning path retrieval methods lack a global structural perspective.
Approach:	They propose a framework that reframes multi-hop reasoning as a schema-guided graph search task.
Outcome:	The proposed framework improves accuracy and evidence completeness of multi-hop reasoning graph retrieval.

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Siyuan Chen, Meilin Wang, Minghao Lv, Zhiling Zhang, Juqianqian Juqianqian, Dejiyangla Dejiyangla, Yujia Peng, Kenny Zhu, Mengyue Wu

Challenge:	Existing studies focus on the semantic content of social media posts, overlooking the evolving nature of mental disorders and symptoms.
Approach:	They extract causality between psychiatric symptoms and life events from social media posts and extract temporal attributes to improve diagnosis and treatment planning.
Outcome:	The extracted causality features improve diagnostic and treatment planning and improve performance in tasks such as depression and diagnosis point detection.

RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)

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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)

Challenge:	Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
Approach:	They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone .
Outcome:	Experiments on four TVR datasets show that the proposed method performs better than other methods.

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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)

Challenge:	Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness .
Approach:	They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image.
Outcome:	The proposed model outperforms existing models on key downstream tasks.

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Xiaomeng Hu, Yiming Zhang, Ru Peng, Haozhe Zhang, Chenwei Wu, Gang Chen, Junbo Zhao

Scaling Collaborative Effort with Agents (2026.findings-acl)

Challenge:	Existing methods for hallucination detection have attracted more attention from the community.
Approach:	They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text.
Outcome:	The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets.

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Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, David Sontag

Challenge:	Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems.
Approach:	They propose a framework that captures how an agent’s utility grows with increasing user involvement.
Outcome:	The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding.

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)

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Jie Shi, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Chenhao Xie, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL.
Approach:	They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever .
Outcome:	The proposed method improves embedding-based retriever and reduces cost.

Glider: Global and Local Instruction-Driven Expert Router (2025.emnlp-main)

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Pingzhi Li, Prateek Yadav, Jaehong Yoon, Jie Peng, Yi-Lin Sung, Mohit Bansal, Tianlong Chen

CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade (2021.findings-emnlp)

Challenge:	Existing methods for routing-based expert models favor generalization over performance on held-in tasks.
Approach:	They propose a global and local instruction driven expert router that leverages recent LLMs' semantic reasoning capabilities to generate task-specific instructions from the input query.
Outcome:	The proposed method improves held-in performance while maintaining strong generalization on held-out tasks.

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Lei Li, Yankai Lin, Deli Chen, Shuhuai Ren, Peng Li, Jie Zhou, Xu Sun

EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices (2025.acl-industry)

Challenge:	Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks.
Approach:	They propose a framework which emits predictions in internal layers without passing through the entire model.
Outcome:	The proposed framework can achieve 15% improvement under 4x speed-up compared with existing methods on six classification tasks yielding more calibrated and accurate predictions.

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Jiyu Chen, Shuang Peng, Daxiong Luo, Fan Yang, Renshou Wu, Fangyuan Li, Xiaoxin Chen

BiasFilter: An Inference-Time Debiasing Framework for Large Language Models (2025.findings-emnlp)

Challenge:	Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks . alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructures.
Approach:	They propose a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module.
Outcome:	The proposed solution achieves comparable performance to baseline Transformer-based LLMs while optimizing memory consumption and time to first token.

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Xiaoqing Cheng, Ruizhe Chen, Hongying Zan, Yuxiang Jia, Min Peng