Papers by Huang Chen

Enhancing Hierarchical Text Classification through Knowledge Graph Integration (2023.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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Ye Liu, Kai Zhang, Zhenya Huang, Kehang Wang, Yanghai Zhang, Qi Liu, Enhong Chen

TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)

Challenge:	Existing approaches to hierarchical text classification are limited by lack of domain knowledge, which leads to mistakes in a variety of situations.
Approach:	They propose a Knowledge-enabled Hierarchical Text Classification model which integrates knowledge graphs into HTC to address the knowledge limitations of traditional methods.
Outcome:	The proposed model integrates knowledge graphs into the hierarchical text classification process, addressing the knowledge limitations of traditional methods.

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Yue Chen, Dingnan Jin, Chen Huang, Jia Liu, Wenqiang Lei

Agentic Knowledgeable Self-awareness (2025.acl-long)

Challenge:	Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling.
Approach:	They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information.
Outcome:	The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context.

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Shuofei Qiao, Zhisong Qiu, Baochang Ren, Xiaobin Wang, Xiangyuan Ru, Ningyu Zhang, Xiang Chen, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks.
Approach:	They propose a data-centric approach that applies agents with knowledgeable self-awareness like humans to a heuristic situation judgement criterion to mark special tokens on their self-explored trajectories for collecting training data.
Outcome:	The proposed paradigm outperforms baseline models on various tasks with minimal external knowledge.

Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations (2024.findings-emnlp)

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Peixin Qin, Chen Huang, Yang Deng, Wenqiang Lei, Tat-Seng Chua

Towards Explainable Computerized Adaptive Testing with Large Language Model (2024.findings-emnlp)

Challenge:	Existing CRSs can be highly persuasive, but they can be deceptive and can damage the long-term trust between users and the CRS.
Approach:	They propose a method to enhance the credibility of CRS’s explanations by using a set of credibility-aware persuasive strategies and a post-hoc self-reflection process.
Outcome:	The proposed method enhances the credibility of CRS’s explanations and refines them via post-hoc self-reflection.

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Cheng Cheng, GuanHao Zhao, Zhenya Huang, Yan Zhuang, Zhaoyuan Pan, Qi Liu, Xin Li, Enhong Chen

CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds (2025.naacl-long)

Challenge:	Existing methods focus on minimizing the number of questions required to assess ability, lacking clear and reliable explanations for the question selection process.
Approach:	They propose to use large language models to enhance computer adaptive testing (CAT) by providing human-like interpretability and explanations.
Outcome:	The proposed agent-based CAT performs comparably or superior to traditional CAT methods in accuracy and significantly improves student trust and satisfaction.

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Lei Wang, Jianxun Lian, Yi Huang, Yanqi Dai, Haoxuan Li, Xu Chen, Xing Xie, Ji-Rong Wen

Sample Design Engineering: An Empirical Study on Designing Better Fine-Tuning Samples for Information Extraction with LLMs (2024.emnlp-industry)

Challenge:	Evaluating role-playing capabilities in large language models is challenging due to complex dynamics involved in role-playering.
Approach:	They propose a simulation sandbox that generates situational fine-grained character behavior trajectories to enhance LLM performance.
Outcome:	The proposed model generates situational fine-grained character behavior trajectories to enhance performance.

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Biyang Guo, He Wang, Wenyilin Xiao, Hong Chen, ZhuXin Lee, Songqiao Han, Hailiang Huang

Towards Multi-label Unknown Intent Detection (2022.coling-1)

Challenge:	Prompt Engineering (PE) is renowned for improving IE performance through prompt modifications, but the realm of sample design for downstream fine-tuning remains unexplored.
Approach:	They propose a methodical approach to enhancing LLMs’ post-tuning performance by refining input, output, and reasoning designs.
Outcome:	The proposed approach outperforms heuristic design strategies on three complex IE tasks with four additional LLMs.

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Yawen Ouyang, Zhen Wu, Xinyu Dai, Shujian Huang, Jiajun Chen

Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang

Challenge:	Existing methods for multi-class unknown intent detection assume that each utterance has only one intent, which is not true because utterrances often contain multiple intents.
Approach:	They propose a task to detect whether an utterance contains the unknown intent by recognizing whether all intents contained in the utterant are known.
Outcome:	The proposed method significantly reduces the FPR95 on the MultiWOZ 2.3 dataset by 12.25% compared to the best baseline.

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Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages.
Approach:	They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods.
Outcome:	The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs.

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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)

Challenge:	Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data.
Approach:	They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse.
Outcome:	The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures.

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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

A Deep Learning-Based System for PharmaCoNER (D19-57)

Challenge:	Unlike English letters, Chinese characters have rich and specific meanings.
Approach:	They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships.
Outcome:	The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser.

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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

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

Challenge:	Efficient access to mentions of clinical entities is very important for using clinical text.
Approach:	They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 .
Outcome:	The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2.

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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

Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages (2024.acl-long)

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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Shih-Cheng Huang, Pin-Zu Li, Yu-chi Hsu, Kuang-Ming Chen, Yu Tung Lin, Shih-Kai Hsiao, Richard Tsai, Hung-yi Lee

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)

Challenge:	Despite the rapid development of large language models, the language capabilities of most open-source LLMs are primarily focused on English due to data constraints.
Approach:	They propose a chat vector to equip pre-trained language models with instruction following and human value alignment via simple model arithmetic.
Outcome:	The proposed method can be extended to include various languages, base models, and chat vectors.

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Fuqiang Niu, Zini Chen, Zhiyu Xie, Hu Huang, Qing Liao, Qianlong Wang, Genan Dai, Bowen Zhang

Convolutional Interaction Network for Natural Language Inference (D18-1)

Challenge:	Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision.
Approach:	They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals.
Outcome:	The proposed framework outperforms baselines in terms of quality and reliability.

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Jingjing Gong, Xipeng Qiu, Xinchi Chen, Dong Liang, Xuanjing Huang

Entity-Aware Dual Co-Attention Network for Fake News Detection (2023.findings-eacl)

Challenge:	Attention-based neural models have achieved great success in natural language inference (NLI).
Approach:	They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI.
Outcome:	The proposed model can capture complex interactions on three large datasets.

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Sin-han Yang, Chung-chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)

Challenge:	Existing models for fake news detection are limited in their ability to detect it from different aspects.
Approach:	They propose a Dual Co-Attention Network (Dual-CAN) for fake news detection that takes news content, social media replies, and external knowledge into consideration.
Outcome:	The proposed model outperforms existing models in two benchmark datasets.

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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang

When Evolution Strategy Meets Language Models Tuning (2025.coling-main)

Challenge:	Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target.
Approach:	They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success.
Outcome:	The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design.

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Bo Huang, Yuxin Jiang, Mingyang Chen, Yi Wang, Hongyang Chen, Wei Wang

PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud (2024.acl-demos)

Challenge:	Autoregressive language models with pretraining often display limited capability in effectively following instructions.
Approach:	They propose an on-policy approach to optimize models by harnessing the principle of biological evolution, namely survival of the fittest.
Outcome:	The proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task.

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Chengyu Wang, Zhongjie Duan, Bingyan Liu, Xinyi Zou, Cen Chen, Kui Jia, Jun Huang

A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding (2026.findings-acl)

Challenge:	Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships.
Approach:	They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images.
Outcome:	The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions.

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Zhe Yang, Yi Huang, Yaqin Chen, Mengfei Guo, Xiaoting Wu, Junlan Feng

FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)

Challenge:	a novel multi-task learning framework for domain-specific natural language understanding tasks addresses these limitations by combing multiple tasks into a single framework.
Approach:	They propose a multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks.
Outcome:	The proposed framework surpasses conventional multi-task learning approaches in performance.

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Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang, Cuiping Li, Hong Chen, Juanzi Li

Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis (2023.findings-emnlp)

Challenge:	Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression.
Approach:	They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression.
Outcome:	The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline.

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Jianqiao Lu, Wenyong Huang, Nianzu Zheng, Xingshan Zeng, Yu Yeung, Xiao Chen

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)

Challenge:	Latent Synthesis is an efficient textual data utilization framework for end-to-end speech processing models . labeled speech data are scarcer and more expensive for collection compared to textual ones .
Approach:	They propose a textual data utilization framework for E2E speech processing models . they train a latent synthesizer to convert textual information into an intermediate latent representation .
Outcome:	The proposed framework improves on low-resource speech recognition and spoken language understanding tasks.

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Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Huang Chen, Dongxia Wang, Jie Zhang

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume.
Approach:	They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data.
Outcome:	The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs.

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

Symbol tuning improves in-context learning in language models (2023.emnlp-main)

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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le

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

Challenge:	Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner.
Approach:	They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols.
Outcome:	The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model.

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

Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting (2022.naacl-main)

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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Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)

Challenge:	Existing knowledge-grounded dialogue generation models only produce pedantic responses, which lacks emotion and attraction compared with the responses with polite style, positive and negative sentiments.
Approach:	They propose a method which generates responses via combing disentangled style templates and content templates.
Outcome:	The proposed method improves on evaluation metrics compared with state-of-the-art methods.

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Shiting Huang, Zhen Fang, Zehui Chen, Siyu Yuan, Junjie Ye, Yu Zeng, Lin Chen, Qi Mao, Feng Zhao

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters (2024.acl-long)

Challenge:	a number of tools are used to perform complex tasks, but the tool utilization process can cause errors.
Approach:	They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks.
Outcome:	The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios.

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Yinghui Li, Zishan Xu, Shaoshen Chen, Haojing Huang, Yangning Li, Shirong Ma, Yong Jiang, Zhongli Li, Qingyu Zhou, Hai-Tao Zheng, Ying Shen

Unified Semantic Typing with Meaningful Label Inference (2022.naacl-main)

Challenge:	Existing studies focus on misspelled characters, ignoring faked characters which are more common and difficult to correct.
Approach:	They propose to use Chinese character checking to identify and correct wrong characters in texts by human annotation.
Outcome:	The proposed dataset is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.

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James Y. Huang, Bangzheng Li, Jiashu Xu, Muhao Chen

LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation (2022.tacl-1)

Challenge:	Semantic typing aims at classifying tokens into semantic categories such as relations, entity types, and event types.
Approach:	They propose a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space.
Outcome:	The proposed framework achieves strong performance across three semantic typing tasks.

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Jian Guan, Zhuoer Feng, Yamei Chen, Ruilin He, Xiaoxi Mao, Changjie Fan, Minlie Huang

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)

Challenge:	Existing benchmarks for natural language processing focus on understanding or generating short texts . lack of standardized benchmarks makes it difficult to assess and compare models .
Approach:	They propose a story-centric benchmark for Chinese long text modeling that aggregates two understanding tasks and two generation tasks.
Outcome:	The proposed model outperforms similar-sized models on understanding and generation tasks.

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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022.acl-long)

Challenge:	Existing models of robustness evaluation are incomprehensive, impractical, and invalid .
Approach:	They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks.
Outcome:	The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol.

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Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy Liu, Cheng-I Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee

DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models (2024.findings-acl)

Challenge:	Existing evaluation methods for transfer learning are limited in speech research . authors show that pre-trained models transfer well across multiple tasks .
Approach:	They propose a benchmark to evaluate pre-trained models by increasing task diversity and difficulty over SUPERB.
Outcome:	The proposed benchmark increases task diversity and difficulty over SUPERB-SG.

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Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang

TrInk: Ink Generation with Transformer Network (2025.emnlp-main)

Challenge:	Large language models (LLMs) have shown impressive results, but still suffer from hallucination, i.e., the generation of false information.
Approach:	They propose a task of sequential model editing that aims to rectify mistakes continuously.
Outcome:	The proposed method significantly outperforms baselines in single-turn and sequential editing.

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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu

LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples (2026.findings-acl)

Challenge:	Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples.
Approach:	They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies.
Outcome:	The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models.

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Yezi Liu, Hanning Chen, Wenjun Huang, Yang Ni, Mohsen Imani

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

Challenge:	Large Language Models encode vast factual knowledge, yet their inability to selectively forget specific information hinders privacy protection, bias mitigation, and post-deployment correction.
Approach:	They propose a LoRA-based negative-only unlearning framework that updates only low-rank adapters while freezing the backbone.
Outcome:	The proposed framework reduces computational cost by about an order of magnitude compared to full fine-tuning and memory-editing methods.

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, Liang Huang

ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)

Challenge:	PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks.
Approach:	They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks.
Outcome:	The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods.

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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)

Challenge:	Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers.
Approach:	They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach.
Outcome:	The proposed method outperforms manual methods and outperfies baselines on Taobao in China.

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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang

Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)

Challenge:	Existing systems that use a left-to-right completion paradigm are inefficient and expensive.
Approach:	They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation .
Outcome:	The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems .

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Weipeng Huang, Xingyi Cheng, Kunlong Chen, Taifeng Wang, Wei Chu

Don’t Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation (2025.acl-long)

Challenge:	Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds.
Approach:	They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge .
Outcome:	The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency.

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Yingchaojie Feng, Yiqun Sun, Yandong Sun, Minfeng Zhu, Qiang Huang, Anthony Kum Hoe Tung, Wei Chen

Formality is Favored: Unraveling the Learning Preferences of Large Language Models on Data with Conflicting Knowledge (2024.emnlp-main)

Challenge:	Existing methods for text embedding require re-encoding the entire corpus for each instruction.
Approach:	They propose a framework that generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text.
Outcome:	The proposed framework improves instruction-following text embedding quality over state-of-the-art methods while speeding up processing on large datasets.

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Jiahuan Li, Yiqing Cao, Shujian Huang, Jiajun Chen

Omni-Chart-600K: A Comprehensive Dataset of Chart Types for Chart Understanding (2025.findings-naacl)

Challenge:	Large language models have shown excellent performance on knowledge-intensive tasks, but pretraining data tends to contain misleading and conflicting information.
Approach:	They systematically analyze LLMs’ learning preferences for data with conflicting knowledge.
Outcome:	The proposed model outperforms human-level models on knowledge-intensive tasks by analyzing pretraining data.

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Shulei Wang, Shuai Yang, Wang Lin, Zirun Guo, Sihang Cai, Hai Huang, Ye Wang, Jingyuan Chen, Tao Jin

From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)

Challenge:	Existing chart-related training methods lack capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types.
Approach:	They propose a two-stage training strategy and method for jointly training a vision encoder tailored for multi-type charts to address the deficiencies in chart types and limited scope of chart tasks in existing datasets.
Outcome:	The proposed dataset includes 21 diverse chart types and tasks, including data retrieval and mathematical reasoning.

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Xuanchang Zhang, Wei Xiong, Lichang Chen, Tianyi Zhou, Heng Huang, Tong Zhang

Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)

Challenge:	Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints .
Approach:	They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model .
Outcome:	The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena.

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Xiusheng Huang, Yubo Chen, Shun Wu, Jun Zhao, Yuantao Xie, Weijian Sun

Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention (N18-1)

Challenge:	Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples.
Approach:	They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term .
Outcome:	The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets.

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Huadong Chen, Shujian Huang, David Chiang, Xinyu Dai, Jiajun Chen

Code-Switching Can be Better Aligners: Advancing Cross-Lingual SLU through Representation-Level and Prediction-Level Alignment (2024.acl-short)

Challenge:	Neural machine translation models learn to map from source language sentences to target language sentences via continuous-space intermediate representations.
Approach:	They propose an encoder with character attention which augments the (sub)word-level representation with character-level information and a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively.
Outcome:	The proposed model outperforms the standard word-based model, subword-based models, and strong character-based ones on translation tasks.

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Zhihong Zhu, Xuxin Cheng, Zhanpeng Chen, Xianwei Zhuang, Zhiqi Huang, Yuexian Zou

Mixture of Ordered Scoring Experts for Cross-prompt Essay Trait Scoring (2025.acl-long)

Challenge:	Existing code-switching-based cross-lingual spoken language understanding frameworks are limited to low-resource languages.
Approach:	They propose a cross-lingual spoken language understanding framework that leverages both code-switched and original sentences to achieve multi-level alignment.
Outcome:	The proposed framework can achieve multi-level alignment on two benchmarks across ten languages.

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Po-Kai Chen, Bo-Wei Tsai, Shao Kuan Wei, Chien-Yao Wang, Jia-Ching Wang, Yi-Ting Huang

Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models (2026.acl-long)

Challenge:	Existing approaches to automate essay scoring overlook critical information, authors say . evaluators often limit their performance to unseen topics, resulting in incomplete assessment perspectives.
Approach:	They propose a framework that integrates information from prompts and essays into an AES framework.
Outcome:	The proposed framework achieves state-of-the-art in cross-prompt scoring and multi-trait scoring on the ASAP++ dataset.

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Shaoning Sun, Mingzhu Cai, Huang He, Bingjin Chen, Siqi Bao, Yujiu Yang, Hua Wu, Haifeng Wang

MED-COREASONER: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning (2026.acl-long)

Challenge:	RL-friendly models exhibit intra-class compactness and inter-class separation in probability assignments . under identical training, Qwen models achieve substantial gains, while others like Llama yield limited improvements.
Approach:	They propose a method to quantify distributional clarity in probability space . they show distributional clearness is a trainable property underlying RL-Friendliness .
Outcome:	The proposed model families achieve substantial gains under identical training, while others like Llama yield limited improvements.

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Fan Gao, Sherry T. Tong, Jiwoong Sohn, Jiahao Huang, Junfeng Jiang, Ding Xia, Piyalitt Ittichaiwong, Kanyakorn Veerakanjana, Hyunjae Kim, Qingyu Chen, Edison Marrese-Taylor, Kazuma Kobayashi, Akiko Aizawa, Irene Li

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

Challenge:	Existing models that use English and local languages have a multilingual gap . a language-informed co-reasoning framework can be used to improve multilingual reasoning .
Approach:	They propose a language-informed co-reasoning framework that elicits parallel English and local-language reasoning and abstracts them into structured concepts.
Outcome:	Experiments show that Med-CoReasoner improves multilingual reasoning performance by 5% . the framework produces clinically sound and culturally grounded reasoning traces .

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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

Code Representation Pre-training with Complements from Program Executions (2024.emnlp-industry)

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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Jiabo Huang, Jianyu Zhao, Yuyang Rong, Yiwen Guo, Yifeng He, Hao Chen

SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)

Challenge:	Existing languages have syntactic representations of code to improve code intelligence, but they are difficult to learn from code.
Approach:	They propose to embed dynamic information of programs revealed by their test cases into feature representations of code as complements.
Outcome:	The proposed method yields 6%/19% mAP improvements over its masked language modeling counterparts.

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Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Gao Xing, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang

Relation-Aware Question Answering for Heterogeneous Knowledge Graphs (2023.findings-emnlp)

Challenge:	Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence.
Approach:	They propose a benchmark to evaluate the sociality of role-playing agents using LLMs.
Outcome:	The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances.

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Haowei Du, Quzhe Huang, Chen Li, Chen Zhang, Yang Li, Dongyan Zhao

Red Teaming Language Models for Processing Contradictory Dialogues (2024.emnlp-main)

Challenge:	Existing retrieval-based approaches to solve multihop Knowledge Base Question Answering (KBQA) fail to utilize information from head-tail entities and the semantic connection between relations to enhance the information capturing of relations in KGs.
Approach:	They propose to use a dual relation graph to find the answer entity in a knowledge graph . they use primal entity graph reasoning, dual relation grafitment and interaction .
Outcome:	The proposed approach achieves significant performance gain over the prior state-of-the-art on two public datasets, WebQSP and CWQ.

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Xiaofei Wen, Bangzheng Li, Tenghao Huang, Muhao Chen

AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)

Challenge:	a recent study shows that language models are prone to self-contradiction during dialogues.
Approach:	They propose a red teaming framework that detects and attempts to explain dialogues, then modifies existing contradictory content using the explanation.
Outcome:	The proposed task improves the ability to detect contradictory dialogues and provides valid explanations.

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Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, Jun Huang

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information.
Approach:	They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods .
Outcome:	The proposed method improves stock trend prediction and financial question answering tasks.

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Dongwon Jung, Qin Liu, Tenghao Huang, Ben Zhou, Muhao Chen

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)

Challenge:	Retrieval-augmented generation (RAG) improves large language models by incorporating non-parametric knowledge through evidence retrieved from external sources.
Approach:	They propose a training-free evidence compression technique that makes retrieved evidence more familiar to the target model while seamlessly integrating parametric knowledge from the model.
Outcome:	The proposed technique outperforms the most recent evidence compression baselines across open-domain QA datasets while achieving high compression rates.

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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang

ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment (2026.eacl-demo)

Challenge:	Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios.
Approach:	They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers .
Outcome:	The proposed model outperforms baselines and class transfer models in low-resource scenarios.

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Li-Yang Chang, Chih-Ming Chen, Hen-Hsen Huang, Ming-Feng Tsai, An-Zi Yen, Chuan-Ju Wang

Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)

Challenge:	Existing methods for ESG compliance assessment rely on fact-based retrieval methods.
Approach:	They propose a multi-modal information extraction pipeline to extract, structure, and evaluate sustainability reports.
Outcome:	The proposed system extracts, structures, and evaluates ESG-related content from text, tables, figures, and infographics.

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Mingze Zhong, Meng Fang, Zijing Shi, Yuxuan Huang, Shunfeng Zheng, Yali Du, Ling Chen, Jun Wang

The Side Effects of Being Smart: Safety Risks in MLLMs’ Multi-Image Reasoning (2026.acl-long)

Challenge:	Existing theories of Spiral of Silence do not apply to large language models .
Approach:	They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals .
Outcome:	The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance .

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Renmiao Chen, Yida Lu, Shiyao Cui, Xuan Ouyang, Victor Shea-Jay Huang, Shumin Zhang, Chengwei Pan, Han Qiu, Minlie Huang

HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)

Challenge:	Recent advances in multimodal reasoning may pose new safety risks . evaluators neglect reasoningbased safety, where harm emerges only through MLLMs .
Approach:	They introduce a benchmark for multi-image reasoning safety that includes 2,676 instances . they find that models with more advanced multi- image reasoning are more vulnerable .
Outcome:	The proposed benchmark consists of 2,676 instances covering 9 multi-image relations . the results show that models with more advanced multi- image reasoning are more vulnerable .

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Qianyu He, Yikai Zhang, Jiaqing Liang, Yuncheng Huang, Yanghua Xiao, Yunwen Chen

Probing Multimodal Large Language Models for Global and Local Semantic Representations (2024.lrec-main)

Challenge:	Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation.
Approach:	They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion .
Outcome:	The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics.

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Mingxu Tao, Quzhe Huang, Kun Xu, Liwei Chen, Yansong Feng, Dongyan Zhao

Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)

Challenge:	Existing studies have focused on the ability of MLLMs to generate single tokens one by one, while lacking studies about how their representation vectors can encode global multimodal information.
Approach:	They propose to use image-caption corpus to train Multimodal Large Language Models (MLLMs) . they find that the topmost layers encode more global semantic information .
Outcome:	The proposed models can encode more global semantic information, rather than the topmost layers, and perform better on visual-language entailment tasks.

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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

ASTRO: Automatic Strategy Optimization For Non-Cooperative Dialogues (2025.findings-acl)

Challenge:	Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts.
Approach:	They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features.
Outcome:	The proposed method achieves state-of-the-art on three benchmark datasets.

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Yikuan Hu, Chen Huang, Wenqiang Lei

Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning (2022.findings-emnlp)

Challenge:	Existing methods for optimizing dialogues require substantial human effort for strategy optimization.
Approach:	They propose a fully automated solution that leverages large language models’ self-envolving capabilities to optimize dialogue strategies.
Outcome:	The proposed solution significantly improves on baseline models across non-cooperative dialogue tasks, highlighting the potential for autonomously developing such agents without human intervention.

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Hongqiu Wu, Ruixue Ding, Hai Zhao, Boli Chen, Pengjun Xie, Fei Huang, Min Zhang

Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)

Challenge:	Empirical studies show that learning multiple training objectives in a single model makes the learned language representation barely converge to the desired optimum.
Approach:	They propose a meta-learning-based adaptive sampler which learns latent sampling pattern on arbitrary pre-training objectives.
Outcome:	Empirical studies show that learning multiple objectives in a single model makes it difficult to achieve the desired optimum.

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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang

Challenge:	Recent studies have focused on a single pass of lyrics generation with little human intervention.
Approach:	They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes.
Outcome:	The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly.

Financial Opinion Mining (2021.emnlp-tutorials)

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Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset (2023.findings-acl)

Challenge:	This tutorial will provide an overview of financial opinion mining and provide research directions.
Approach:	This tutorial will introduce financial opinion mining and examine possible research directions.
Outcome:	This tutorial aims to provide an overview of financial opinion mining and figure out research directions.

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Hanchong Zhang, Jieyu Li, Lu Chen, Ruisheng Cao, Yunyan Zhang, Yu Huang, Yefeng Zheng, Kai Yu

Chinese Discourse Parsing: Model and Evaluation (2020.lrec-1)

Challenge:	a cross-domain text-to-SQL task aims to parse user questions into SQL on complete unseen databases . a single-domain task evaluates the performance on identical databases based on the same domain .
Approach:	They propose a cross-domain text-to-SQL task that parses user questions into SQL on unseen databases.
Outcome:	The proposed system can parse user questions into SQL on complete unseen databases.

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Lin Chuan-An, Shyh-Shiun Hung, Hen-Hsen Huang, Hsin-Hsi Chen

ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification (2022.coling-1)

Challenge:	Chinese discourse parsing has not yet a consistent evaluation metric . micro vs. macro F1 scores, binary v. multiway ground truth, and left-heavy v . right-heaviness binarization are important for Chinese discourses .
Approach:	They propose a neural network model that unifies a pre-trained transformer and a CKY-like algorithm and compare it with previous models with different evaluation scenarios.
Outcome:	The proposed model outperforms the previous models with different evaluation scenarios.

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Yen-Hao Huang, Yi-Hsin Chen, Yi-Shin Chen

Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning (2024.emnlp-main)

Challenge:	Graph neural networks (GNNs) are used to learn document representation from graph structures.
Approach:	They propose a unified model with a joint training mechanism to learn from document embeddings and contextual word interactions simultaneously.
Outcome:	The proposed model outperforms pure inductive GNNs and BERT-style models . the proposed model also has a joint training mechanism to learn from document embeddings and contextual word interactions simultaneously.

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Lu Chen, Rui Zheng, Binghai Wang, Senjie Jin, Caishuang Huang, Junjie Ye, Zhihao Zhang, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective (2026.findings-acl)

Challenge:	Current methods rely on ranking losses to teach reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions.
Approach:	They propose a method that incorporates contrastive learning into the reward modeling process to enhance generalization and stabilize the reinforcement learning training process.
Outcome:	The proposed method enhances generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences.

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Meifang Chen, Zhe Yang, Huang Nianchen, Yizhan Huang, Yichen LI, Zihan Li, Michael R. Lyu

Bridging the Memorization-Utilization Gap: Near-Lossless Context Compression via Reinforcement Learning (2026.acl-long)

Challenge:	Code Large Language Models (CLLMs) are reshaping how software is built, maintained, and evolved.
Approach:	They propose to use BPE tokenization to inadvertently leak code secrets . they propose to mitigate the gibberish bias by using a newer tokenizer .
Outcome:	The proposed model is based on a novel method that can be used to detect and mitigate gibberish bias in CLLMs.

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Yujan Ting, Xu Tang, Terrence Chen, Weijing Huang

Temporal Scaling Law for Large Language Models (2025.emnlp-main)

Challenge:	Recent advances in context compression have failed to effectively utilize compressed representations for downstream tasks.
Approach:	They propose a holistic training paradigm that uses outcome-based RL to enable implicit expansion.
Outcome:	The proposed model outperforms previous models on NIAH, LongBench and multi-hop reasoning.

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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding

Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)

Challenge:	Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size.
Approach:	They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law .
Outcome:	The proposed model predicts the test loss of LLMs as the training steps scale up.

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Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, Hai Jin

An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction (2023.emnlp-main)

Challenge:	Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation.
Approach:	They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them.
Outcome:	The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches.

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Guanhua Huang, Runxin Xu, Ying Zeng, Jiaze Chen, Zhouwang Yang, Weinan E

Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models (2024.findings-acl)

Challenge:	Existing methods to generate event roles require a given generation order . parallel methods suffer from inadequate training and manifest zero accuracies on some event roles.
Approach:	They propose an iteratively parallel generation method with the Pre-Filling strategy to generate event roles in parallel to avoid order selection.
Outcome:	The proposed method outperforms other entity-enhanced models and achieves state-of-the-art performance on two public datasets.

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Sheng-Lun Wei, Cheng-Kuang Wu, Hen-Hsen Huang, Hsin-Hsi Chen

Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent (2020.emnlp-main)

Challenge:	Using zero-shot or few-shot prompting, Large Language Models have been widely adopted in downstream applications.
Approach:	They propose to quantify the impact of option order and token usage on LLMs and propose mitigation strategies to enhance model performance.
Outcome:	The proposed mitigation strategies improve model performance and reduce the impact of token and order sensitivity on LLMs.

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Yun-Hsuan Jen, Chieh-Yang Huang, MeiHua Chen, Ting-Hao Huang, Lun-Wei Ku

Challenge:	Prior work uses hand-crafted scores to recommend sentences but has difficulty adopting such scores to all the near-synonyms as near-near-sonyms differ in various ways.
Approach:	They propose an inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent's performance.
Outcome:	The proposed agent achieves the best performance in fill-in-the-blank and good example sentence selection tasks.

Unlocking the Potential of Model Merging for Low-Resource Languages (2024.findings-emnlp)

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Mingxu Tao, Chen Zhang, Quzhe Huang, Tianyao Ma, Songfang Huang, Dongyan Zhao, Yansong Feng

OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection (2024.findings-acl)

Challenge:	Adapting large language models (LLMs) to new languages requires continual pre-training followed by supervised fine-tuning.
Approach:	They propose a model merging solution that integrates LLMs with distinct capabilities into a single model without additional training.
Outcome:	The proposed model merging outperforms CT-then-SFT in low-resource languages with scarce data.

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Chenyang Huang, Abbas Ghaddar, Ivan Kobyzev, Mehdi Rezagholizadeh, Osmar Zaiane, Boxing Chen

STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents (2024.findings-acl)

Challenge:	Existing methods for detecting hallucinations and omissions in Machine Translation systems focus on analyzing the model’s internal states or relying on external tools.
Approach:	They propose an Optimal Transport-based word aligner specifically designed to enhance the detection of hallucinations and omissions in Machine Translation systems.
Outcome:	The proposed method is competitive with state-of-the-art methods across 18 language pairs on the HalOmi benchmark and shows promising features.

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Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua

wav2vec-S: Adapting Pre-trained Speech Models for Streaming (2024.findings-acl)

Challenge:	Existing methods for addressing ambiguities in conversational search systems are one-size-fits-all and struggle to achieve effective domain transferability.
Approach:	They propose a method to provide search engines with strategies regarding when to ask clarification questions in a post-hoc manner.
Outcome:	The proposed method improves search performance 10% on four unseen domains.

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Biao Fu, Kai Fan, Minpeng Liao, Yidong Chen, Xiaodong Shi, Zhongqiang Huang

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)

Challenge:	Pre-trained speech models have advanced speech-related tasks, including speech recognition and translation.
Approach:	They propose a pre-trained speech model that incorporates modifications to ensure consistent speech representations during training and inference phases for streaming speech inputs.
Outcome:	The proposed model outperforms baseline models on speech recognition and translation tasks and achieves a superior balance between quality and latency.

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Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Yin Zhang

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

Challenge:	Existing memory networks do not perform well when leveraging heterogeneous information from different sources.
Approach:	They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model.
Outcome:	The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets.

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

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)

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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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang

HiSVD: Principled Low-Rank Approximation of LLMs via Hierarchical Modeling of Information Capacity and Spectral Structure (2026.acl-long)

Challenge:	Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability.
Approach:	They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs .
Outcome:	The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks.

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Zhuo Chen, Minghao Li, Xiaoqian Ma, Siqi Fan, Xiusheng Huang, Zhang Liujie, Weihang Chen

MultiCAT: Multimodal Communication Annotations for Teams (2025.findings-naacl)

Challenge:	Existing methods for generating layer importance ignore the fine-grained influence of spectral distribution shape.
Approach:	They propose a hierarchical rank allocation framework with two stages to address this gap . they propose SVD-based lowrank approximation that exploits spectral heterogeneity .
Outcome:	Experiments show that HiSVD outperforms state-of-the-art methods on LLMs .

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Adarsh Pyarelal, John M Culnan, Ayesha Qamar, Meghavarshini Krishnaswamy, Yuwei Wang, Cheonkam Jeong, Chen Chen, Md Messal Monem Miah, Shahriar Hormozi, Jonathan Tong, Ruihong Huang

Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

Challenge:	Recent flagship models from OpenAI and Google are only capable of 1-on-1 interactions with humans, limiting the potential for integration into human-machine teams of the future.
Approach:	They propose a dataset that allows team members to make multiple types of predictions on the same dataset.
Outcome:	The proposed dataset builds upon data from teams working collaboratively to save victims in a simulated search and rescue mission.

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Yaoke Wang, Yun Zhu, XintongBao XintongBao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang

CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)

Challenge:	Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
Approach:	They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback.
Outcome:	The proposed method is based on an industrial e-commerce benchmark and public datasets.

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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang

LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)

Challenge:	Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored.
Approach:	They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces.
Outcome:	The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces .

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Ning Li, Zheng Zhang, Zhenya Huang, Rui Li, Yi Zhan, Yinbo Luo, Qi Liu, Enhong Chen

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation (2023.findings-acl)

Challenge:	Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning.
Approach:	They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion .
Outcome:	The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action.

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Wenhao Zhu, Shujian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen

MESAQA: A Dataset for Multi-Span Contextual and Evidence-Grounded Question Answering (2025.coling-main)

Challenge:	kNN-MT builds an external datastore, which saves all target language token occurrences in the parallel corpus.
Approach:	They propose a new paradigm for domain adaptation by building an external datastore which usually saves all target language token occurrences in the parallel corpus.
Outcome:	The proposed model can be easily pruned according to local correctness, and it is more explainable.

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Jui-I Wang, Hen-Hsen Huang, Hsin-Hsi Chen

Question Answering

Challenge:	Existing question answering systems focus on extracting answers from single spans, but real-world scenarios require synthesizing information from multiple spans.
Approach:	They propose a dataset that leverages the MASH-QA dataset and large language models (LLMs) to ensure that each Q/A pair requires considering all selected spans.
Outcome:	The proposed method enables the model to answer multiple Q/A pairs in a single span, while ensuring that all selected spans are considered.

Explicit Semantic Decomposition for Definition Generation (2020.acl-main)

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Jiahuan Li, Yu Bao, Shujian Huang, Xinyu Dai, Jiajun Chen

Challenge:	Existing definition generation methods rely on decoding to extract semantic components of words.
Approach:	They propose a method which explicitly decomposes meaning of words into semantic components and models them with discrete latent variables for definition generation.
Outcome:	The proposed method outperforms existing methods on WordNet and Oxford benchmarks.

ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent (2025.acl-long)

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Shangjian Yin, Peijie Huang, JiaTian Chen, Haojing Huang, Yuhong Xu

Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance, but their application to spoken language understanding remains challenging.
Approach:	They propose an Entity-level Language Model framework which reformulates slot-filling as an entity recognition task and introduces a new concept, Chain of Intent, to enable step-by-step multi-intent recognition.
Outcome:	The proposed framework outperforms strong baselines such as Uni-MIS and achieves gains of 3.7% and 3.1% on MixATIS and MixSNIPS.

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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Fanchao Qi, Longtao Huang, Zhiyuan Liu, Maosong Sun

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)

Challenge:	Textual adversarial samples are often misrepresented in research on security, evaluation, explainability, and data augmentation.
Approach:	They propose to use adversarial samples to evaluate their methods on security tasks to demonstrate the real-world concerns rather than developing impractical methods.
Outcome:	The proposed method has higher practical value than the current benchmark.

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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang

LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand.
Approach:	They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages.
Outcome:	Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages.

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Yining Huang, Bin Li, Keke Tang, Meilian Chen

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

Challenge:	Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning.
Approach:	They propose a dual-system LoRA framework that partitions data and parameters by System 1 or System 2 demands and adopts a two-stage fine-tuning strategy to enhance knowledge and intuition.
Outcome:	The proposed framework partitions data and parameters by System 1 or System 2 demands, using fewer yet more focused parameters for each task.

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

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.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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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang

MEEL: Multi-Modal Event Evolution Learning (2024.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs).
Approach:	They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.
Outcome:	The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.

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Zhengwei Tao, Zhi Jin, Junqiang Huang, Xiancai Chen, Xiaoying Bai, Yifan Zhang, Chongyang Tao

Learning to Rank Visual Stories From Human Ranking Data (2022.acl-long)

Challenge:	Existing models fail to grasp the principles governing event evolution in various scenarios.
Approach:	They propose a multi-modal event evolution learning approach to grasp event evolution . they propose an instruction encapsulation process that transforms evolving graphs into instruction-tuning data .
Outcome:	The proposed model grasps the event evolution mechanism yielding advanced MMER ability.

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Chi-Yang Hsu, Yun-Wei Chu, Vincent Chen, Kuan-Chieh Lo, Chacha Chen, Ting-Hao Huang, Lun-Wei Ku

NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)

Challenge:	Existing studies on visual storytelling (VIST) use automated evaluation metrics for text generation.
Approach:	They develop a Vrank metric that repurposes human evaluation results for automatic evaluation.
Outcome:	The proposed model is more accurate than existing metrics and is generalizable to textual stories.

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Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li, Chen Li, Fei Huang, Min Zhang

Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)

Challenge:	Recent studies on Chinese grammatical error correction focus on learning essays.
Approach:	They propose a Chinese grammatical error correction dataset that annotates multiple references for 12,500 sentences from three native domains.
Outcome:	The proposed dataset can be used to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.

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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi

A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)

Challenge:	Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance.
Approach:	They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation.
Outcome:	The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities.

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Jie Zhou, Qi Zhang, Qin Chen, Qi Zhang, Liang He, Xuanjing Huang

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)

Challenge:	Event argument extraction (EAE) aims to extract arguments with given roles from texts.
Approach:	They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets.
Outcome:	The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE.

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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Challenge:	Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis.
Approach:	They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis.
Outcome:	SciAssess evaluates 11 LLMs on multiple tasks across scientific fields.

Integrating Data Validation with Large Language Models for Regulation-Guided Tabular Anomaly Detection (2026.acl-long)

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Haoliang Huang, Zihuang Cai, Zhuo Tang, Yifan Liu, Chen Tian, Kenli Li, Changjian Chen

AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)

Challenge:	Existing tabular anomaly detection methods focus on detecting anomalies based on data distribution without considering regulatory compliance.
Approach:	They propose a task that leverages regulations to detect anomalies in tabular data . they also develop three new datasets to address this task .
Outcome:	The proposed method outperforms baselines on three new datasets.

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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks.
Approach:	They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process.
Outcome:	The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS.

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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

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)

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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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)

Challenge:	a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling .
Approach:	They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution.
Outcome:	The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data.

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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration (2025.acl-long)

Challenge:	i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data.
Approach:	They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data.
Outcome:	i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks.

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Yunkun Wang, Yue Zhang, Zhen Qin, Chen Zhi, Binhua Li, Fei Huang, Yongbin Li, Shuiguang Deng

Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)

Challenge:	Large language models face intrinsic limitations in coding with unseen APIs in training corpora.
Approach:	They propose a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by planning a complex problem into several API invocation subtasks and experimenting with correct API usage at intermediate steps.
Outcome:	The proposed framework significantly improves performance for models lacking prior API knowledge, achieving 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10.

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Yu Bao, Hao Zhou, Shujian Huang, Lei Li, Lili Mou, Olga Vechtomova, Xin-yu Dai, Jiajun Chen

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

Challenge:	Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space.
Approach:	They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence.
Outcome:	The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models.

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

Orthogonal Subspace Learning for Language Model Continual Learning (2023.findings-emnlp)

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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Xiao Wang, Tianze Chen, Qiming Ge, Han Xia, Rong Bao, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)

Challenge:	Existing methods for continual learning in language models suffer catastrophic forgetting when learning sequential tasks.
Approach:	They propose an orthogonal low-rank adaptation approach for continual learning in language models that uses orthogons to learn sequentially.
Outcome:	The proposed approach outperforms state-of-the-art methods on continual learning benchmarks and preserves generalization ability of LLMs on unseen tasks.

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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang

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

Challenge:	Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks.
Approach:	They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes.
Outcome:	The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset.

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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.

Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR (2021.findings-acl)

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Junkun Chen, Mingbo Ma, Renjie Zheng, Liang Huang

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

Challenge:	Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency.
Approach:	They propose a new paradigm for simultaneous speech-to-text translation using two separate decoders . they use multitask learning to jointly learn these two tasks with a shared encoder .
Outcome:	The proposed method achieves substantially better translation quality at similar levels of latency.

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

Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)

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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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables.
Approach:	They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments.
Outcome:	The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods.

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Siyin Wang, Jie Zhou, Qin Chen, Qi Zhang, Tao Gui, Xuanjing Huang

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)

Challenge:	Existing approaches to domain adaptation fail to generalize well on unknown test data.
Approach:	They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift.
Outcome:	The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift.

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Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

Challenge:	Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models.
Approach:	They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context.
Outcome:	The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation.

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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach:	They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome:	The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.

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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang

Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)

Challenge:	Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs.
Approach:	They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them.
Outcome:	The proposed architecture achieves comparable performance with GShard with 2B parameters and computation.

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Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun

Reduce Human Labor On Evaluating Conversational Information Retrieval System: A Human-Machine Collaboration Approach (2023.emnlp-main)

Challenge:	Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus.
Approach:	They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture.
Outcome:	The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.

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Chen Huang, Peixin Qin, Wenqiang Lei, Jiancheng Lv

Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)

Challenge:	Evaluating conversational information retrieval systems requires a significant amount of human labor for annotation.
Approach:	They propose to use human annotation to calibrate evaluation results to eliminate evaluation biases.
Outcome:	The proposed method consumes less than 1% of human labor and achieves a consistency rate of 95%-99% with human evaluation results.

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Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation (D19-1)

Challenge:	Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention.
Approach:	They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task.
Outcome:	The proposed framework outperforms state-of-the-art on two public datasets.

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Huiyun Yang, Shujian Huang, Xin-Yu Dai, Jiajun Chen

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

Challenge:	Existing domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual sample samples.
Approach:	They propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from a source domain model.
Outcome:	The proposed model outperforms baselines and state-of-the-art models on three sequence labeling tasks.

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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

Through the Valley: Path to Effective Long CoT Training for Small Language Models (2025.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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Renjie Luo, Jiaxi Li, Chen Huang, Wei Lu

Issues and Perspectives from 10,000 Annotated Financial Social Media Data (2020.lrec-1)

Challenge:	Long chain-of-thought (CoT) supervision is effective for large language models . but small models trained on limited long CoT data experience performance degradation .
Approach:	They identify a phenomenon called Long CoT Degradation in small language models . long CoT data can be used to generate long chain-of-thought (CoT) responses .
Outcome:	The results show that models trained on 8k long CoT examples lose up to 75% of their original performance before fine-tuning.

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Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf (2026.acl-demo)

Challenge:	In the NLP community, many researchers have begun to use machine learning on financial and economic data.
Approach:	They present a dataset with 10,000 financial tweets annotated by experts from the front desk and the middle desk in a bank’s treasury.
Outcome:	The annotated financial tweets of a bank's front desk and middle desk are compared against a general sentiment dictionary and a domain-specific dictionary.

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Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, Xuanqiang Angelo Huang, Vilém Zouhar, Dominik Glandorf, Isabel Dahlgren, Rishit Dagli, Yuen Chen, Felix Leeb, Van Q. Truong, Punya Syon Pandey, Yves Bicker, Suvajit Majumder, Wenyuan Jiang, Zeju Qiu, Sankalan Pal Chowdhury, Mrinmaya Sachan, Bernhard Schölkopf, Mona T. Diab, Zhijing Jin

Challenge:	Emerging AI-powered writing assistants focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting.
Approach:	They propose a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors.
Outcome:	The proposed system outperforms a baseline with the skill library and provides actionable suggestions while leaving the actual writing to human authors.

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)

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Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, Tao Gui

LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation (2025.findings-acl)

Challenge:	coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored .
Approach:	They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding.
Outcome:	The proposed benchmark aims to accelerate the development of more scaffold-aware agents.

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Yen-Shan Chen, Jing Jin, Peng-Ting Kuo, Chao-Wei Huang, Yun-Nung Chen

DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)

Challenge:	Recent studies have shown that large language models (LLMs) exhibit significant biases in evaluation tasks, especially in preferentially rating and favoring self-generated content.
Approach:	They propose to simulate two critical phases of retrieval-augmented generation (RAG) frameworks where keyword extraction and factual accuracy take precedence over stylistic elements.
Outcome:	The proposed model emulates two critical phases of the retrieval-augmented generation framework.

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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models (2025.emnlp-main)

Challenge:	Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
Approach:	They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase.
Outcome:	The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets.

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Yi Feng, Jiaqi Wang, Wenxuan Zhang, Zhuang Chen, Shen Yutong, Xiyao Xiao, Minlie Huang, Liping Jing, Jian Yu

Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Existing approaches to mental health support lack realism and capture therapeutic progression over time.
Approach:	They propose a framework that simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate responses through retrieval-augmentation.
Outcome:	The proposed framework outperforms standard methods in quality and depth on 260 simulated clients and 230 human participants.

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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun

AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)

Challenge:	Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence.
Approach:	They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary.
Outcome:	Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training.

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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei

Linguistics & Cognitive NLP

Challenge:	Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
Approach:	They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts.
Outcome:	The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning.

EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)

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Hao Zhou, Minlie Huang, Yong Liu, Wei Chen, Xiaoyan Zhu

Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)

Challenge:	Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation.
Approach:	They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph.
Outcome:	The proposed model generates more informative, coherent, and natural responses than baseline models.

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Zewei Sun, Shujian Huang, Xinyu Dai, Jiajun Chen

A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models (2024.emnlp-main)

Challenge:	Recent studies show that the attention heads in Transformer are not equal.
Approach:	They propose a masking method to mask attention heads in Transformer . they empirically validate the inequality and propose 'head mask' method to avoid bottleneck .
Outcome:	The proposed masking method improves translation performance on multiple languages . it can be used to remove a small subset of heads without affecting performance .

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Zhihao Wang, Shiyu Liu, Jianheng Huang, Wang Zheng, YiXuan Liao, Xiaoxin Chen, Junfeng Yao, Jinsong Su

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

Challenge:	Version updates are an indispensable requirement for Large Language Models . a large learning rate in the first stage and a complete learning decay process are crucial for version updates of LLMs.
Approach:	They propose a learning rate path switching training paradigm for version updates of Large Language Models.
Outcome:	The proposed paradigm reduces training cost to 58% when training four versions of LLMs compared to PTFS and CPT .

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang

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

Challenge:	Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process.
Approach:	They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors.
Outcome:	The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs.

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

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

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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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

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Lei Chen, Zhongyu Wei, Jing Li, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors.
Approach:	They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity.
Outcome:	Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods.

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

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

AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.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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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Zhen Ye, Guang Chen, Zhiyong Huang, Jing Ma

Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation (2026.findings-acl)

Challenge:	Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy.
Approach:	They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a .
Outcome:	The proposed framework systematically reveals the performance of different target mLLMs.

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Jen-tse Huang, Chang Chen, Shiyang Lai, Wenxuan Wang, Michelle R Kaufman, Mark Dredze

Argument-Based Sentiment Analysis on Forward-Looking Statements (2024.findings-acl)

Challenge:	Short-video platforms have become major channels for misinformation, but their robustness against misinformation entangled with cognitive biases remains under-explored.
Approach:	They propose a framework for evaluation of short-video platforms that use visual cues and social cue.
Outcome:	The proposed framework evaluates MLLMs across five modality settings.

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Chin-Yi Lin, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)

Challenge:	Existing models for argument mining are limited in interpreting future-oriented arguments.
Approach:	They propose a categorization of argument units into claims, premises, and scenarios coupled with a unique sentiment analysis framework.
Outcome:	The proposed framework outperforms existing models in most tasks and is more efficient than existing methods.

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Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, Zuyu Zhao

From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG (2026.findings-acl)

Challenge:	Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient.
Approach:	They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs .
Outcome:	The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs .

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Guanhua Chen, Chuyue Huang, Yutong Yao, Shudong Liu, Xueqing Song, Lidia S. Chao, Derek F. Wong

code-transformed: The Influence of Large Language Models on Code (2026.findings-eacl)

Challenge:	Existing multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities, making failures unverifiable.
Approach:	They propose a multimodal benchmark that features real-world landmarks with annotations across multiple viewpoints and a framework that treats visual elements as first-class retrieval units through three stages: element-level detection and classification, multi-granularity cross-modal alignment for evidence retrieval, and attribution-constrained generation.
Outcome:	The proposed framework achieves up to 29.2% improvement over six strong baselines for this task.

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Yuliang Xu, Siming Huang, Mingmeng Geng, Yao Wan, Xuanhua Shi, Dongping Chen

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	Using Large Language Models, code generation capabilities have transformed programming practices.
Approach:	They analyze 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025 . they identify measurable trends in the evolution of coding style that align with LLM-generated code .
Outcome:	The proposed study examines 20,000 GitHub repositories linked to arXiv papers . it finds that LLMs influence code style, and that they can be observed in real-world code .

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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Barry Yao, Sijia Wang, Yu Chen, Qifan Wang, Minqian Liu, Zhiyang Xu, Licheng Yu, Lifu Huang

SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning (2026.findings-acl)

Challenge:	Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking.
Approach:	They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base.
Outcome:	The proposed approach integrates attributes into disambiguation.

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Yongfeng Huang, Ruiying Chen, James Cheng

A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

Challenge:	Standard RAG frameworks treat retrieval as a static, single-round auxiliary step . compressed workflow makes it difficult to form reliable evidence chains .
Approach:	They propose a framework that decouples tasks and allows for dynamic multi-round exploration . they propose retrieval-augmented generation (RAG) to mitigate hallucinations and knowledge obsolescence .
Outcome:	The proposed framework improves the strongest baseline by +6.46 accuracy points on average across five benchmarks and five LLM backbones.

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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu

Adaptive Nearest Neighbor Machine Translation (2021.acl-short)

Challenge:	a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach:	They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome:	The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks.

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Xin Zheng, Zhirui Zhang, Junliang Guo, Shujian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations (D19-1)

Challenge:	kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Approach:	They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Outcome:	The proposed method outperforms the existing model on four benchmark datasets and is open-source.

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Zuyi Bao, Rui Huang, Chen Li, Kenny Zhu

Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination (2025.findings-acl)

Challenge:	Existing approaches to cross-lingual sequence labeling require bilingual resources and require linguistic knowledge.
Approach:	They propose a multilingual language model with deep semantic Alignment to generate language-independent representations for cross-lingual sequence labeling.
Outcome:	The proposed model achieves state-of-the-art NER and POS performance across European languages and on distant language pairs such as English and Chinese.

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Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh, Boxing Chen, Sarath Chandar

Towards Zero-shot Learning for End-to-end Cross-modal Translation Models (2023.findings-emnlp)

Challenge:	Large language models (LLMs) have a tendency to hallucinate false or misleading information, limiting their reliability.
Approach:	They examine how architecture-based inductive biases affect the propensity to hallucinate . they find that the models are more reliable and more reliable than traditional models .
Outcome:	The proposed models can be used to train and train large language models that are factual or able to explain themselves through their knowledge.

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Jichen Yang, Kai Fan, Minpeng Liao, Boxing Chen, Zhongqiang Huang

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

Challenge:	End-to-end zero-shot speech translation model is based on a zero-shot approach, but it is less competitive because of the limited amount of data available for multiple modalities.
Approach:	They propose an end-to-end zero-shot speech translation model that connects two pre-trained uni-modality modules via word rotator’s distance.
Outcome:	The proposed model performs better than or as well as those of the CTC-based models and can be trained in an end-to-end style to avoid error propagation.

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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan

EmotionLines: An Emotion Corpus of Multi-Party Conversations (L18-1)

Challenge:	Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach:	They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome:	The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.

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Chao-Chun Hsu, Sheng-Yeh Chen, Chuan-Chun Kuo, Ting-Hao Huang, Lun-Wei Ku

DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)

Challenge:	Emotion is a critical characteristic to distinguish people from machines.
Approach:	They propose a dataset with emotions labeling on all utterances in each dialogue . they use Friends TV scripts and Facebook messenger dialogues to collect the data .
Outcome:	The proposed dataset is the first with emotions labeling on all utterances in each dialogue based on their textual content.

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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang

Challenge:	Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation.
Approach:	They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward.
Outcome:	The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.

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

Hierarchical Context-aware Network for Dense Video Event Captioning (2021.acl-long)

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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Lei Ji, Xianglin Guo, Haoyang Huang, Xilin Chen

Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021.emnlp-main)

Challenge:	Existing technologies for dense video event captioning generate fine-grained captions for all events in a long untrimmed video.
Approach:	They propose a hierarchical context-aware network for dense video event captioning to capture context from various aspects.
Outcome:	The proposed model outperforms the existing model on youcook2 and activitynet . it generates coherent captions for events in a long untrimmed video .

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Ting-Wei Hsu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)

Challenge:	Existing methods for data augmentation address data deficiencies and semantic consistency, but they ignore the second issue.
Approach:	They propose a semantics-preserving data augmentation approach that preserves the semantics of a textual sequence.
Outcome:	The proposed method achieves better performance on publicly available datasets and stock price/risk movement prediction scenarios.

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Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua, Jimmy Huang

Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking (2024.eacl-long)

Challenge:	Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years .
Approach:	They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges.
Outcome:	The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks .

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Yong Cao, Ruixue Ding, Boli Chen, Xianzhi Li, Min Chen, Daniel Hershcovich, Pengjun Xie, Fei Huang

Dual-oriented Disentangled Network with Counterfactual Intervention for Multimodal Intent Detection (2024.emnlp-main)

Challenge:	Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates.
Approach:	They propose a framework to integrate Chinese geographic semantics into re-ranking pipelines.
Outcome:	The proposed framework improves on two Chinese benchmark datasets.

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Zhanpeng Chen, Zhihong Zhu, Xianwei Zhuang, Zhiqi Huang, Yuexian Zou

ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)

Challenge:	Existing methods for multimodal intent detection have two limitations: (i) close entanglement of multimodal semantics with modal structures; (ii) insufficient learning of causal effects of semantic and modality-specific information on the final predictions.
Approach:	They propose a Dual-oriented Disentangled Network with Counterfactual Intervention model that decouples semantics-oriented and modality-oriented representations and a Counterfective Intervention Module that applies causal inference to understand causal effects by injecting confounders.
Outcome:	The proposed model overcomes key limitations in existing systems by effectively disentangling and utilizing modality-specific and multimodal semantic information.

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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

Challenge:	retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures .
Approach:	They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents.
Outcome:	The proposed method reduces offline indexing costs and accelerates retrieval.

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

GhostBERT: Generate More Features with Cheap Operations for BERT (2021.acl-long)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Zhiqi Huang, Lu Hou, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)

Challenge:	Existing studies show that some parameters in pre-trained language models can be pruned away without severe accuracy degradation.
Approach:	They propose a method which generates more features with very cheap operations from the remaining features and can be applied to unpruned BERT models to enhance their performance.
Outcome:	Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of the proposed method.

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Qin Liu, Wenxuan Zhou, Nan Xu, James Y. Huang, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning (2025.findings-emnlp)

Challenge:	Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios.
Approach:	They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task .
Outcome:	Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses .

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Yue Xin, Chen Shen, Shaotian Yan, Xiaosong Yuan, Yaoming Wang, Xiaofeng Zhang, Chenxi Huang, Jieping Ye

RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)

Challenge:	Chain-of-Thought prompting improves the math reasoning capability of large language models.
Approach:	They propose a method for attribution of component-level contributions in CoT reasoning using Shapley value and a stratified sampling algorithm that significantly reduces computational complexity.
Outcome:	The proposed method reduces computational complexity and provides robust correlations with model performance.

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Xukai Liu, Kai Zhang, Ye Liu, Enhong Chen, Zhenya Huang, Linan Yue, Jiaxian Yan

Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue (2025.findings-naacl)

Challenge:	Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations.
Approach:	They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG.
Outcome:	Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods.

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Jieming Cao, Chen Huang, Yanan Zhang, Ruibo Deng, Jincheng Zhang, Wenqiang Lei

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

Challenge:	Recent efforts to improve depression-diagnosis-oriented dialogue systems ignore the Stigma preventing users from open conversations about their struggles.
Approach:	They propose a method to promote a sense of unobtrusiveness within the dialogue system and assessing depression disorder by probing symptoms.
Outcome:	The proposed method improves on baselines including unobtrusiveness evaluation of dialogue content and diagnostic accuracy.

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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.

Multi-VQG: Generating Engaging Questions for Multiple Images (2022.emnlp-main)

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Min-Hsuan Yeh, Vincent Chen, Ting-Hao Huang, Lun-Wei Ku

NICE: Neural Image Commenting with Empathy (2021.findings-emnlp)

Challenge:	Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event.
Approach:	They propose to generate engaging questions from multiple images using a visual question generation dataset and establish a series of baselines.
Outcome:	The proposed model builds stories behind the image sequence to allow for creativity and experience sharing and hence draw attention to downstream applications.

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Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao

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

Challenge:	Emotion and empathy are examples of human qualities lacking in many human-machine interactions.
Approach:	They propose to generate images with human-generated comments with enhanced emotion and empathy while minimizing inappropriate or offensive outputs.
Outcome:	The proposed model generates more human-like and engaging image comments on two images with human-generated comments and human annotations while minimizing inappropriate or offensive outputs.

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

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

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

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)

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Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan, Xiaojun Quan, Hehong Chen, Ji Zhang, Fei Huang

Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)

Challenge:	Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems.
Approach:	They propose a multi-LLM approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
Outcome:	The proposed model outperforms existing models by demonstrating its effectiveness and advantages in tool learning.

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Xinyi Mou, Zhongyu Wei, Lei Chen, Shangyi Ning, Yancheng He, Changjian Jiang, Xuanjing Huang

Leveraging Conflicts in Social Media Posts: Unintended Offense Dataset (2024.emnlp-main)

Challenge:	Existing studies rely on roll call data to estimate political preference of legislators.
Approach:	They propose to integrate voting behavior and public statements on Twitter to jointly model legislators.
Outcome:	The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements .

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Che Wei Tsai, Yen-Hao Huang, Tsu-Keng Liao, Didier Estrada, Retnani Latifah, Yi-Shin Chen

Text-Attributed Graph Learning with Coupled Augmentations (2025.coling-main)

Challenge:	a new study examines the impact of conflict on multi-person communication datasets on offensive language . conflict datasets often neglect contextual information and focus on intended offenses . authors propose a conflict-based data collection method to analyze inter-conflict cues in multi-user communications .
Approach:	They propose a conflict-based data collection method to utilize inter-conflict cues in multi-person communications.
Outcome:	The proposed method improves the accuracy of detecting offensive language and enriches our understanding of conflict dynamics in digital communication.

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Chuang Zhou, Jiahe Du, Huachi Zhou, Hao Chen, Feiran Huang, Xiao Huang

Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding (2026.acl-long)

Challenge:	Existing models focus on either the text attribute or the graph structure, neglecting the other aspect.
Approach:	They propose a model that combines the strengths of both text-learning and graph-learning models in parallel.
Outcome:	The proposed model outperforms existing models on diverse datasets.

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Dexuan Xu, Yuan Jiayin, Jianing Wang, Yanyuan Chen, Hanpin Wang, Yu Huang

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

Challenge:	Existing benchmarks for multimodal large language models are limited to multiview diagnostics.
Approach:	They propose a benchmark specifically designed for medical multi-image understanding that evaluates MLLMs across four dimensions.
Outcome:	The proposed model performs better in multi-image contexts than open-source models . the model perform better when processing increased visual loads than closed-source ones .

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

Memp: Exploring Agent Procedural Memory (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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Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Challenge:	Large Language Models (LLMs) based agents suffer from brittle procedural memory that is manually engineered or entangled in static parameters.
Approach:	They propose a procedural-memory repository that distills past agent trajectories into fine-grained, step-by-step instructions and higher-level, script-like abstractions.
Outcome:	The proposed repository can be used to improve agents' performance on travelplanner and Alfworld.

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He

Dual Supervised Learning for Natural Language Understanding and Generation (P19-1)

Challenge:	Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps.
Approach:	They propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
Outcome:	The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT.

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Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen

Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)

Challenge:	Natural language understanding and natural language generation are important research topics in the NLP and dialogue fields.
Approach:	They propose a dual-supervised learning framework for natural language understanding and generation on top of dual supervised learning.
Outcome:	The proposed framework boosts the performance of both tasks simultaneously in the benchmark experiments.

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Zeliang Tong, Wei Wei, Xiaoye Qu, Rikui Huang, Zhixin Chen, Xingyu Yan

KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words.
Approach:	They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words.
Outcome:	The proposed framework achieves state-of-the-art performance under low-resource conditions.

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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang

Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question .
Approach:	They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance .
Outcome:	The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered .

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Mingkuan Zhao, Wentao Hu, Tianchen Huang, Yuheng Min, Suquan Chen, Yide Gao, Yanbo Zhai, Shuangyong Song, Xuelong Li

Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations (2026.findings-eacl)

Challenge:	Hallucinations in Large Language Models persist in critical domains where generated content diverges from contextual facts or logical constraints.
Approach:	They propose to generate hallucinations as orthogonal noise relative to the semantic manifold of the residual stream.
Outcome:	The proposed method achieves superior contextual faithfulness compared to state-of-the-art methods.

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Sheng-Lun Wei, Yu-Ling Liao, Yen-Hua Chang, Hen-Hsen Huang, Hsin-Hsi Chen

MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)

Challenge:	Recent advances extend language understanding beyond text to speech, enabling unified reasoning across modalities.
Approach:	They construct and release a speech-augmented benchmark based on Global MMLU Lite and a data set spanning English, Chinese, and Korean.
Outcome:	The proposed model is robust to demographic factors but sensitive to language and option order, suggesting that speech can amplify structural biases.

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Shuaijie She, Wei Zou, Shujian Huang, Wenhao Zhu, Xiang Liu, Xiang Geng, Jiajun Chen

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)

Challenge:	Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data .
Approach:	They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language.
Outcome:	The proposed framework improves multilingual reasoning across languages on three benchmarks.

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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu

Modeling Historical Relevant and Local Frequency Context for Representation-Based Temporal Knowledge Graph Forecasting (2024.findings-emnlp)

Challenge:	Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory.
Approach:	a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities .
Outcome:	Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory .

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Shengzhe Zhang, Wei Wei, Rikui Huang, Wenfeng Xie, Dangyang Chen

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

Challenge:	Existing representation-based approaches neglect candidate-specific temporal context, resulting in serious information loss or homogeneous prediction.
Approach:	They propose a temporal representation learning model that incorporates temporal contexts of candidates and models temporal contextual information from historiCal Relevant context and locAl Frequency contexT.
Outcome:	The proposed model can leverage temporal contextual information to achieve differential predictions on six benchmark datasets.

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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

Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning (2026.findings-acl)

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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Xiaoyun Zhang, Xiaojian Yuan, Di Huang, Wang You, Chen Hu, Jingqing Ruan, Kejiang Chen, Xing Hu

ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation (2024.acl-long)

Challenge:	Reasoning ability is a defining capability of Large Language Models (LLMs), but RLVR training suffers from policy entropy collapse, hindering exploration and limiting reasoning performance.
Approach:	They propose a framework that dynamically balances exploration and exploitation via three components: difficulty-aware coefficient allocation, initial-anchored target entropy, and dynamic global coefficient adjustment.
Outcome:	The proposed framework outperforms baselines on multiple mathematical reasoning benchmarks.

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Chen Huang, Yiping Jin, Ilija Ilievski, Wenqiang Lei, Jiancheng Lv

Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence (2023.emnlp-main)

Challenge:	Empirical studies demonstrate that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods.
Approach:	They propose an analogical reasoning-based approach that enhances automatic annotation accuracy in the interactive data annotation setting and reduces the need for human corrections.
Outcome:	Empirical studies show that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods.

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Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen, Yuexian Zou

Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)

Challenge:	Existing models rely on annotated training data, limiting their scalability to low-resource languages.
Approach:	They propose a method termed SoGo for zero-shot cross-lingual SLU that uses keywords as substitution options to extract keywords and a token-level alignment strategy to ensure grammatical coherence.
Outcome:	The proposed method improves zero-shot cross-lingual SLU across nine languages on MultiATIS++.

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Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu

OntoED: Low-resource Event Detection with Ontology Embedding (2021.acl-long)

Challenge:	Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario.
Approach:	They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus.
Outcome:	The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge.

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Shumin Deng, Ningyu Zhang, Luoqiu Li, Chen Hui, Tou Huaixiao, Mosha Chen, Fei Huang, Huajun Chen

E2EDev: Benchmarking Large Language Models in End-to-End Software Development Task (2026.acl-long)

Challenge:	Existing methods to ED rely on training instances and ignore correlation of event types.
Approach:	They propose a process of event ontology population linking event instances to pre-defined event types in event ontoology and ontological embedding to address these problems.
Outcome:	The proposed framework can be applied to new unseen event types by establishing linkages to existing ones.

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Jingyao Liu, Chen Huang, Zhizhao Guan, Wenqiang Lei, Yang Deng

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)

Challenge:	Existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols.
Approach:	They propose a benchmark to assess whether generated software meets user needs . they use a fine-grained set of user requirements and a fully automated testing pipeline .
Outcome:	E2EDev is a benchmark to assess whether generated software meets user needs through mimicking real user interactions.

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Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu

D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)

Challenge:	Existing slot filling models memorize inherent patterns of entities and contexts from training data.
Approach:	They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution .
Outcome:	The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts.

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Zhe Yang, Yi Huang, Yaqin Chen, Chunyang Gao, Jingyu Yao, Junlan Feng

AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation (2026.acl-long)

Challenge:	Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval.
Approach:	They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing.
Outcome:	The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing.

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Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen, Dejing Dou

Transfer of Frames from English FrameNet to Construct Chinese FrameNet: A Bilingual Corpus-Based Approach (L18-1)

Challenge:	Existing models rely on a single segmentation token whose hidden state implicitly encodes both semantic reasoning and spatial localization . Existing methods rely only on SEG>, which encodes semantic reasoning, limiting the model's ability to explicitly disentangle what to segment from where to segment.
Approach:	They propose a method which reformulates reasoning segmentation as a structured conditional generation process over image tokens conditioned on language grounded query banks.
Outcome:	The proposed model bridges token-level predictions and pixel-level supervision by decoupling spatial grounding from semantic reasoning through structured language grounded query banks.

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Tsung-Han Yang, Hen-Hsen Huang, An-Zi Yen, Hsin-Hsi Chen

M2-TabFact: Multi-Document Multi-Modal Fact Verification with Visual and Textual Representations of Tabular Data (2025.findings-acl)

Challenge:	Current publicly available Chinese FrameNet has a relatively low coverage of frames and lexical units compared with other languages.
Approach:	They propose an automatic way to construct Chinese FrameNet using a sentence-aligned English-Chinese bilingual corpus.
Outcome:	The proposed resource can provide frame recommendations acceptable by annotators.

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Mingyang Zhou, Lingyu Zhang, Sophia Horng, Maximillian Chen, Kung-Hsiang Huang, Shih-Fu Chang

Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems (2026.acl-long)

Challenge:	Existing fact-checking systems that can reason over structured data are inefficient compared to humans.
Approach:	They propose a multi-modal table-based fact verification task that requires reasoning over visual and textual representations of structured data.
Outcome:	The proposed model can reason over visual and textual representations of structured data.

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Xingchen Zou, Yuhao Yang, Zheng Chen, Xixuan Hao, Yiqi Chen, Chao Huang, Yuxuan Liang

BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation (2022.acl-long)

Challenge:	Rapid urbanization and surging vehicle ownership intensify congestion . rapid urbanization drives crash rates, slow emergency response, and burden transit-poor communities .
Approach:	They introduce a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC) they use reinforcement learning and network communication to convert LLM into a traffic-control model that operates like a human traffic agent.
Outcome:	The proposed model outperforms baselines and training-intensive RL controllers on a simulated traffic environment and reduces queue lengths by more than 5%.

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Yanling Xiao, Lemao Liu, Guoping Huang, Qu Cui, Shujian Huang, Shuming Shi, Jiajun Chen

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

Challenge:	Existing IMT systems relying on lexical constrained decoding (LCD) are limited in translation efficiency and quality due to LCD.
Approach:	They propose a novel interactive neural machine translation system that uses lexical constraints to decode missing words in a manually revised translation.
Outcome:	The proposed system performs significantly better and faster than state-of-the-art IMT on three translation tasks.

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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.

Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)

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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning (2026.acl-long)

Challenge:	Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding .
Approach:	They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning.
Outcome:	The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS.

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Zerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei, Linfeng Gao, Xiao Huang, Zhihong Zhang, Jinsong Su

One Pair Suffices: Unlocking Universal Zero-Shot Translation via Cross-Architecture Alignment (2026.acl-long)

Challenge:	Graph-based Retrieval-Augmented Generation (GraphRAG) is a new approach to document retrieval, but it is not suitable for legal reasoning.
Approach:	They propose a framework for reliable legal reasoning that structures knowledge as relational graphs and uses a multi-agent system to verify validity.
Outcome:	The proposed framework outperforms existing GraphRAG models in accurate and trustworthy legal analysis.

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Hao Zong, Cong Hu Yuan, Chao Bei, Wentao Chen, Huan Liu, Kaiyu Huang, Degen Huang

Predicting Clinical Trial Results by Implicit Evidence Integration (2020.emnlp-main)

Challenge:	Current paradigms for empowering Large Language Models with multilingual capabilities rely heavily on massive instruction tuning.
Approach:	They propose a hybrid cross-alignment approach that fuses a frozen NLLB encoder with a Qwen decoder via a closed-loop dual-adapter architecture.
Outcome:	The proposed model outperforms towerPlus-9B and Aya-101 on language-agnostic projection protocols.

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Qiao Jin, Chuanqi Tan, Mosha Chen, Xiaozhong Liu, Songfang Huang

Structure-Unified M-Tree Coding Solver for Math Word Problem (2022.emnlp-main)

Challenge:	Clinical trials are expensive and time-consuming, and inappropriately designed studies can be devastating in a pandemic.
Approach:	They propose a model that takes a PICO-formatted clinical trial proposal and predicts the outcome from it.
Outcome:	The proposed model outperforms baseline models on a benchmark dataset with 10.7% relative gain over BioBERT.

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Bin Wang, Jiangzhou Ju, Yang Fan, Xinyu Dai, Shujian Huang, Jiajun Chen

Numerical Reasoning

Challenge:	Existing models that take into account the binary tree structure of mathematical expressions have achieved better performance, but the output space is non-deterministic.
Approach:	They propose a Structure-Unified M-Tree Coding Solver which applies a tree with any M branches to unify the output structures.
Outcome:	The proposed model outperforms several state-of-the-art models under similar experimental conditions and performs much better under low-resource conditions.

PRAM: An End-to-end Prototype-based Representation Alignment Model for Zero-resource Cross-lingual Named Entity Recognition (2023.findings-acl)

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Yucheng Huang, Wenqiang Liu, Xianli Zhang, Jun Lang, Tieliang Gong, Chen Li

SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)

Challenge:	Existing methods to address the named entity recognition problem are limited and lack explicit optimization specific to the task.
Approach:	They propose a prototype-based representation alignment model for a cross-lingual named entity recognition task using labeled source language data.
Outcome:	The proposed model outperforms existing state-of-the-art methods in some challenging scenarios.

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Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, Leqi Lei, Yi Feng, Zexuan Xiong, Miao Yan, Xunzhi Wang, Yaru Cao, Jianing Yin, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang

Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)

Challenge:	Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans.
Approach:	They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts.
Outcome:	The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts.

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Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang

MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China (2024.acl-long)

Challenge:	Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks.
Approach:	They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations.
Outcome:	The proposed pipeline can be used to study tool use under three scenarios.

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Chen Zhang, Mingxu Tao, Quzhe Huang, Jiuheng Lin, Zhibin Chen, Yansong Feng

Challenge:	MC2 is the largest open-source corpus of minority languages in china . MC2, however, includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian .
Approach:	They propose a multilingual corpus of minority languages in China that includes four underrepresented languages . they prioritize accuracy while enhancing diversity by using a quality-centric approach .
Outcome:	The proposed model prioritizes accuracy while enhancing diversity, the authors say . MC2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian .

JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents (2026.acl-long)

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Ada Chen, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang, Shuai Wang

Challenge:	Recent advances in computer-using agents have created new safety and security risks . despite the impressive capabilities of CUAs, there are still significant security risks.
Approach:	They propose a systematization of knowledge on the safety and security threats of Computer-Using Agents.
Outcome:	The proposed framework provides a framework for assessing the safety and security risks of computer-using agents.

OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025.emnlp-main)

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Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in machine writing such as open domain long-form generation.
Approach:	They propose a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection to improve the knowledge density of generated articles.
Outcome:	The proposed framework improves the knowledge density of generated articles without compromising metrics such as coherence and depth.

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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo

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

Challenge:	Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.
Approach:	They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark.
Outcome:	The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness.

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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

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

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

Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)

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

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Tianle Gu, Zongqi Wang, Kexin Huang, Yuanqi Yao, Xiangliang Zhang, Yujiu Yang, Xiuying Chen

MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response (2024.findings-naacl)

Challenge:	Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods .
Approach:	They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low.
Outcome:	The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods.

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Zihao Deng, Yinghao Ma, Yudong Liu, Rongchen Guo, Ge Zhang, Wenhu Chen, Wenhao Huang, Emmanouil Benetos

Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)

Challenge:	Large Language Models have shown immense potential in multimodal applications, but convergence between textual and musical domains remains unexplored.
Approach:	They propose a system that aligns music representations with a frozen LLM . they train the system on an extensive music caption dataset and fine-tune it with instructional data .
Outcome:	The proposed system bridges the gap between music audio and textual contexts by combining music captions with a frozen model . it performs well in generating music caption and composing music-related Q&A pairs . the proposed system is available for free download at http://www.musilingo.com/ .

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Chen Xu, Bojie Hu, Yufan Jiang, Kai Feng, Zeyang Wang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu

Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)

Challenge:	Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard.
Approach:	They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system.
Outcome:	The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.

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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li

Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)

Challenge:	Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization .
Approach:	They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume.
Outcome:	Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency.

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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation (2025.findings-emnlp)

Challenge:	Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications.
Approach:	They propose a framework that re-uses existing parameter-efficient methods with a unified classifier.
Outcome:	The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier.

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Wei-Hsiang Lin, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen

Challenge:	LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models’ generation and judgment abilities remain inconsistent.
Approach:	They propose a self-reference-guided evaluation strategy that leverages a model’s own answers as references to strengthen the correlation between generation and judgment abilities.
Outcome:	The proposed approach strengthens the correlation between model generation and judgment abilities and provides a reliable proxy for model selection in evaluation tasks.

Continual Learning for Natural Language Generation in Task-oriented Dialog Systems (2020.findings-emnlp)

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Fei Mi, Liangwei Chen, Mengjie Zhao, Minlie Huang, Boi Faltings

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

Challenge:	Existing neural approaches for natural language generation are typically developed offline for specific domains.
Approach:	They propose a method to expand NLG knowledge incrementally to new domains . major challenge is catastrophic forgetting, meaning a model forgets the knowledge it has learned before .
Outcome:	The proposed method outperforms other methods by effectively mitigating catastrophic forgetting issue.

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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021.naacl-main)

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

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Yufan Huang, Yanzhe Zhang, Jiaao Chen, Xuezhi Wang, Diyi Yang

Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection (2024.findings-acl)

Challenge:	Existing continual learning methods focus on preserving knowledge from previous tasks . Continual learning is a useful tool for learning over time, but it is not always possible to generalize to new tasks.
Approach:	They propose a disentanglement-based regularization method for continual learning on text classification that disentangles text hidden spaces into generic representations and regularizes them differently to constrain knowledge required to generalize.
Outcome:	The proposed method disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task.

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Ruibo Chen, Yihan Wu, Lichang Chen, Guodong Liu, Qi He, Tianyi Xiong, Chenxi Liu, Junfeng Guo, Heng Huang

Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)

Challenge:	Existing data selection methods for instruction-following large language models rely on unreliable scores or use downstream tasks for selection.
Approach:	They propose a method that utilizes the VLM itself as a filter to select high-quality instruction-tuning data.
Outcome:	The proposed method can reach better results compared to full data settings with merely about 15% samples and can achieve superior performance against competitive baselines.

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Henglin Huang, Chen Tang, Tyler Loakman, Frank Guerin, Chenghua Lin

SEEN: Structured Event Enhancement Network for Explainable Need Detection of Information Recall Assistance (2022.emnlp-main)

Challenge:	Current neural models for Chinese story generation struggle to generate high-quality long text narratives due to ambiguity in syntactically parsing the Chinese language.
Approach:	They propose a framework that enhances the feature capturing mechanism by informing the generation model of dependencies between words and additionally augmenting the semantic representation learning through synonym denoising training.
Outcome:	The proposed framework outperforms the state-of-the-art Chinese generation models on all evaluation metrics, showing that it enhances dependency and semantic representation learning.

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You-En Lin, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen

ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)

Challenge:	Existing work on information recall focuses on reactively retrieving life events . but, proactively detecting the need for information recall services is rarely discussed .
Approach:	They propose a human-annotated life experience retelling dataset to detect the right time to trigger an information recall service.
Outcome:	The proposed system detects life event inconsistency, additional information in life events, and forgotten events.

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Wentong Chen, Yankai Lin, ZhenHao Zhou, HongYun Huang, YanTao Jia, Zhao Cao, Ji-Rong Wen

Reasoning with Language Model Prompting: A Survey (2023.acl-long)

Challenge:	Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability.
Approach:	They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark.
Outcome:	The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy.

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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)

Challenge:	Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications.
Approach:	They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners.
Outcome:	The proposed approaches have not been systematically reviewed and analyzed.

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Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, Zhi Yu

PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment (2024.emnlp-main)

Challenge:	Song translation requires both translation of lyrics and alignment of music notes . human translators of songs need to have a mastery of cultural traditions and the poetic usage of both source and target languages .
Approach:	They propose a model that can model lyric translation and lyrics-melody alignment . they use an encoder-decoder framework that can translate lyrics and determine number of aligned notes .
Outcome:	The proposed framework can translate lyrics and determine the number of aligned notes at each decoding step.

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Jiahuan Li, Shujian Huang, Aarron Ching, Xinyu Dai, Jiajun Chen

Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)

Challenge:	Large language models exhibit reasonable multilingual abilities, despite predominantly English-centric pretraining.
Approach:	They propose a framework that establishes multilingual alignment prior to language model pretraining and preserves this alignment using a code-switching strategy during pretraining.
Outcome:	Experiments in a synthetic English to English-Clone setting show that PreAlign outperforms standard multilingual joint training in language modeling, zero-shot cross-lingual transfer, and cross-linguistic knowledge application.

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Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua

Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment (2022.findings-naacl)

Challenge:	Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users.
Approach:	They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives.
Outcome:	The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives.

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Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver

A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)

Challenge:	Existing models suffer performance degradation when evaluated on Spider-CG, even though every sub-sentence is seen during training.
Approach:	They propose a clause-level compositional example generation method to generate compositional biases from SQL clauses.
Outcome:	The proposed method improves generalization performance even on a training dataset.

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Wei Zou, Shujian Huang, Jun Xie, Xinyu Dai, Jiajun Chen

S^4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview (2026.acl-long)

Challenge:	Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems.
Approach:	They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric.
Outcome:	The proposed paradigm produces stable attacks with meaning-preserving adversarial examples.

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Guanqun Bi, Zhoufu Liu, Zhuang Chen, Dazhen Wan, Xiyao Xiao, Minlie Huang

MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios (2025.findings-acl)

Challenge:	Existing methods for psychiatric interviewing degenerate into rigid interrogation or aimless chitchat due to a lack of strategic planning.
Approach:	They propose a framework for psychiatric interviewing grounded in Speech Act Theory that integrates a large-scale dataset with fine-grained psychic speech act annotations.
Outcome:	The proposed framework outperforms baselines in psychiatric interviewing.

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JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin

A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)

Challenge:	MLDebugging is a benchmark designed to assess debugging challenges within multi-library Python code.
Approach:	They propose to introduce a benchmark to assess debugging challenges within multi-library Python code using 126 Python libraries.
Outcome:	The proposed benchmark covers 126 Python libraries and a wide range of multi-library code issues.

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Zhongwu Chen, Long Bai, Zixuan Li, Zhen Huang, Xiaolong Jin, Yong Dou

Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation (2024.findings-acl)

Challenge:	Conventional knowledge Graph Reasoning models learn the embeddings of KG components over the structure of a KG.
Approach:	They propose a pipeline to integrate knowledge from LLMs into KGs without fine-tuning . they propose knowledge alignment, KG reasoning and entity reranking to enhance conventional models .
Outcome:	The proposed pipeline can enhance the performance of conventional KGR models in incomplete and general situations.

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Xu Huang, Zhirui Zhang, Xiang Geng, Yichao Du, Jiajun Chen, Shujian Huang

COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)

Challenge:	Recent studies have shown that Large Language Models (LLMs) can be used as translation evaluators.
Approach:	They propose to use both coarse-grained and fine-grounded prompts to discern the utility of source versus reference data in machine translation evaluation tasks.
Outcome:	The proposed model can be used to evaluate translations in multiple languages.

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Yucheng Huang, Kai He, Yige Wang, Xianli Zhang, Tieliang Gong, Rui Mao, Chen Li

CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems (2020.findings-emnlp)

Challenge:	Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity.
Approach:	They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class.
Outcome:	The proposed approach outperforms state-of-the-art models with a significant margin in most cases.

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Yiran Chen, Pengfei Liu, Ming Zhong, Zi-Yi Dou, Danqing Wang, Xipeng Qiu, Xuanjing Huang

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)

Challenge:	Existing evaluation methods for text summarization systems are limited to in-domain setting, where supervised pre-trained models are evaluated on the same dataset.
Approach:	They propose to use a cross-dataset evaluation approach to evaluate different summarization systems in a multi-domain setting.
Outcome:	The proposed model can be used to evaluate text summarization systems on different datasets.

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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen

RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning (2025.findings-acl)

Challenge:	Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities.
Approach:	They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance.
Outcome:	The proposed method achieves a superior balance between downstream learning and general capability retention.

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Junhao Hu, Wenrui Huang, Weidong Wang, Zhenwen Li, Tiancheng Hu, Zhixia Liu, Xusheng Chen, Tao Xie, Yizhou Shan

VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated strong capabilities across various domains, but their large-scale deployment faces a major obstacle: the high computational cost of long-sequence inference.
Approach:	They propose an algorithm that retains key-value vectors until they are no longer needed to solve reasoning tasks.
Outcome:	The proposed algorithm achieves high accuracy with O(L) time but O(N) memory complexities.

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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei

Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)

Challenge:	Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision.
Approach:	They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone.
Outcome:	The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data.

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Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen

Cross-Lingual Transfer

Challenge:	Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints.
Approach:	They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media.
Outcome:	The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method.

DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking (2025.acl-long)

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Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin, Yaojie Lu, Fei Huang, Xianpei Han, Yongbin Li, Le Sun

Challenge:	Existing studies in retrieval-augmented generation (RAG) do not sufficiently address the design of complex engineering solutions.
Approach:	They propose a retrieval-augmented generation system that leverages tree-based exploration and bi-point thinking mechanism to generate reliable solutions.
Outcome:	Experiments show that the proposed system achieves state-of-the-art (SOTA) performance on the SolutionBench, highlighting its potential to enhance the automation and reliability of complex engineering solution design in real-world applications.

Dynamic Oracle for Neural Machine Translation in Decoding Phase (L18-1)

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Zi-Yi Dou, Hao Zhou, Shu-Jian Huang, Xin-Yu Dai, Jia-Jun Chen

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)

Challenge:	Existing methods to improve NMT performance but there is a discrepancy between training and inference when decoding.
Approach:	They propose to use Scheduled Sampling to reduce the discrepancy between training and inference in NMT when decoding to mitigate the discrépancy.
Outcome:	The proposed methods improve translation quality over standard NMT system.

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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen

KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)

Challenge:	Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models.
Approach:	They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model.
Outcome:	The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD.

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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen

Chain-of-Memory: Lightweight Memory Construction with Dynamic Evolution for LLM Agents (2026.acl-long)

Challenge:	Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings.
Approach:	They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method.
Outcome:	The proposed framework can be used in few-shot settings and reduces data requirements.

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Xiucheng Xu, Bingbing Xu, Tian Xueyun, Zihe Huang, Rongxin Chen, Li Yunfan, Huawei Shen

Dynamic Past and Future for Neural Machine Translation (D19-1)

Challenge:	Existing paradigms for large language model (LLM) agents use memory construction and retrieval-augmented generation.
Approach:	They propose a framework that advocates for a paradigm shift toward lightweight construction paired with sophisticated utilization.
Outcome:	Experiments show that CoM outperforms baselines with accuracy gains of 7.5%–10.4% while reducing computational overhead to approximately 2.7% of token consumption and 6.0% of latency compared to complex memory architectures.

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Zaixiang Zheng, Shujian Huang, Zhaopeng Tu, Xin-Yu Dai, Jiajun Chen

EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)

Challenge:	Neural machine translation models can benefit from modeling translated and untranslated source contents as recurrent states, but this less interpretable recurrence hinders their power to model dynamic updating of and contents during decoding.
Approach:	They propose to model the dynamic updating of and contents during decoding by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment.
Outcome:	The proposed method achieves significant improvements over both Rnmt and Transformer by producing more adequate translations.

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Xinyi Mou, Chen Qian, Wei Liu, Ling Yan, Yao Hu, Xuanjing Huang, Zhongyu Wei

Pre-training Language Model as a Multi-perspective Course Learner (2023.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics.
Approach:	They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%.
Outcome:	The proposed model reduces token consumption by over 20% while preserving human language.

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Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang

TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)

Challenge:	Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks.
Approach:	They propose a multi-perspective course learning method to fetch many degrees and visual angles for sample-efficient pre-training and to fully leverage the relationship between generator and discriminator.
Outcome:	The proposed method improves ELECTRA's performance on GLUE and SQuAD 2.0 benchmarks and overshadows recent advanced ELECL-style models under the same settings.

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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma

Joint Enhancement of Relational Reasoning for Long-Context LLMs (2025.findings-emnlp)

Challenge:	Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases.
Approach:	They propose a test case generation benchmark that uses large language models to generate unit test cases.
Outcome:	The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths.

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Zhirui Chen, Wei Shen, Jiashui Huang, Ling Shao

DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence? (2024.findings-emnlp)

Challenge:	JERR is a graph-based reasoning framework for large language models . it enables LLMs to handle extended contexts with improved reliability and transparency .
Approach:	They propose a graph-based reasoning framework that integrates synopsis extraction, graph construction, and relational reasoning.
Outcome:	The proposed framework outperforms baselines on ROUGE and F1 metrics and achieves the highest scores on the LLM-Rater evaluation.

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Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)

Challenge:	Existing LLMs' abilities to detect evidence in long contexts are far inferior to humans.
Approach:	They propose a benchmark to assess LLMs' abilities in evidence and multi-step commonsense reasoning within a long context.
Outcome:	The proposed method improves the performance of LLMs in evidence detection and commonsense reasoning.

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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu

Rethinking Document-level Neural Machine Translation (2022.findings-acl)

Challenge:	Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains.
Approach:	They propose a training framework that operationalizes this principle through coarse-to-fine budgeting.
Outcome:	Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines.

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Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, Lei Li

Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)

Challenge:	Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence .
Approach:	They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly .
Outcome:	The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages.

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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang

CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling (2024.emnlp-main)

Challenge:	evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT).
Approach:	They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations .
Outcome:	The proposed model outperforms strong baselines in both supervised and unsupervised settings.

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Yu Bai, Xiyuan Zou, Heyan Huang, Sanxing Chen, Marc-Antoine Rondeau, Yang Gao, Jackie Cheung

BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)

Challenge:	Recent studies have found that information relevant to the next token prediction task accumulates in the hidden representations of just a few tokens.
Approach:	They propose a method that integrates attention preferences useful for a downstream task into the eviction process of hidden states.
Outcome:	The proposed method performs better on comprehension and retrieval tasks while preserving language modeling perplexity.

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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li

Modeling Diagnostic Label Correlation for Automatic ICD Coding (2021.naacl-main)

Challenge:	Existing information extraction (IE) tasks rely on in-context learning with large language models.
Approach:	They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates.
Outcome:	The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1.

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Shang-Chi Tsai, Chao-Wei Huang, Yun-Nung Chen

Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning (2026.acl-long)

Challenge:	Existing work built a binary prediction for each label independently, ignoring the dependencies between labels.
Approach:	They propose a framework to capture the label correlation and train a reranking estimator to rescore the probability of each label set candidate generated by a base predictor.
Outcome:	The proposed framework improves on the best-performing predictors on MIMIC datasets.

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Qisheng Su, Shiting Huang, Zhen Fang, Ziyan Chen, Zehui Chen, Feng Zhao

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)

Challenge:	Tool-Integrated Reasoning (TIR) is a tool that can be used to solve complex tasks.
Approach:	They propose a hardware-aware TIR-efficiency metric that unifies internal reasoning and external tool-use costs while explicitly accounting for non-reusable KV-Cache and long-tool-response scenarios.
Outcome:	The proposed metric explains wall-clock latency significantly better than token-count metric in a simulated high-concurrency industrial setting.

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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang

Self-Augmented Preference Alignment for Sycophancy Reduction in LLMs (2025.emnlp-main)

Challenge:	prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems.
Approach:	They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference.
Outcome:	The proposed model maintains safety while reducing over-refusal.

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Chien Hung Chen, Hen-Hsen Huang, Hsin-Hsi Chen

A Chinese Writing Correction System for Learning Chinese as a Foreign Language (C18-2)

Challenge:	Sycophantic behavior in models can erode user trust by creating a perception of dishonesty or bias.
Approach:	They propose to assess the user’s expected answer rather than ignore it and introduce self-augmented preference alignment to reduce sycophancy.
Outcome:	The proposed methods significantly reduce sycophancy across tasks and improve models' assessment ability.

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Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen

Poisoning Retrieval Corpora by Injecting Adversarial Passages (2023.emnlp-main)

Challenge:	a new writing correction system for Chinese learners is available for learning as a second language . a classification approach to English GEC does not require exact recognition of error types . however, there is no general model that handles all types of Chinese writing errors.
Approach:	They propose a Chinese writing correction system that takes a wrong input sentence and generates correction suggestions.
Outcome:	The proposed system generates correction suggestions for Chinese sentences with English translations, helping users understand correct usages of certain grammar patterns.

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Zexuan Zhong, Ziqing Huang, Alexander Wettig, Danqi Chen

EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)

Challenge:	Dense retrievers have outperformed traditional lexical methods in a range of information retrieval tasks, but to what extent can they be safely deployed in real-world applications?
Approach:	They propose a method where a malicious user injects a small number of adversarial passages into a retrieval corpus to maximize similarity with a set of training queries.
Outcome:	The proposed attack fools retrieval systems into returning top results for queries not seen by the attacker.

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Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang

Challenge:	Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules.
Approach:	They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration.
Outcome:	The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%.

GenSense: A Generalized Sense Retrofitting Model (C18-1)

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Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Yow-Ting Shiue, Hsin-Hsi Chen

PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check (2021.acl-long)

Challenge:	Existing word embedding models use only one vector to represent a word, which is problematic in some natural language processing tasks that require sense level representation.
Approach:	They propose a generalized sense embedding learning framework that integrates with the semantic relations between the senses, the relation strength and the semantic strength.
Outcome:	The proposed model outperforms previous models in three types of experiments: semantic relatedness, contextual word similarity and semantic difference.

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Li Huang, Junjie Li, Weiwei Jiang, Zhiyu Zhang, Minchuan Chen, Shaojun Wang, Jing Xiao

Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping (2024.findings-emnlp)

Challenge:	False gram and phonological errors make Chinese spelling check difficult . a novel end-to-end trainable model outperforms existing methods .
Approach:	They propose a trainable Chinese spelling check model that integrates phonological and visual information into a pre-trained language model.
Outcome:	The proposed model outperforms existing state-of-the-art models on three benchmarks.

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Wenhao Zhu, Sizhe Liu, Shujian Huang, Shuaijie She, Chris Wendler, Jiajun Chen

EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention (2022.findings-emnlp)

Challenge:	Decoding by contrasting layers (DoLa) is designed to improve the generation quality of large language models (LLMs) however, this approach does not work well on non-English tasks.
Approach:	They propose a contrastive decoding algorithm that uses amateur logits to contrast with the output of an expert model's early exit logits.
Outcome:	The proposed method outperforms baselines and significantly improves chain-of-thought reasoning accuracy across 11 languages.

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Chen Tang, Chenghua Lin, Henglin Huang, Frank Guerin, Zhihao Zhang

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)

Challenge:	Existing methods for story generation still suffer from problems of relevance and coherence.
Approach:	They propose a novel neural generation model which maps contextual and event features to event sequences with a cross-attention mechanism and exploits logical relatedness between events.
Outcome:	The proposed model outperforms state-of-the-art models on automatic and human evaluations and shows that it can leverage contextual and event features.

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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

Towards Unified Spoken Language Understanding Decoding via Label-aware Compact Linguistics Representations (2023.findings-acl)

Challenge:	Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains .
Approach:	They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone.
Outcome:	The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone.

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Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen, Yuexian Zou

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

Challenge:	Existing methods for intent detection and slot filling decoders could result in misaligned predictions for both tasks.
Approach:	They propose a method that leverages label embeddings to jointly guide the decoding process.
Outcome:	The proposed method outperforms existing methods on two single- and multi-intent SLU benchmarks and can be incorporated into existing models.

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

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)

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.

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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu

Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts (2025.acl-long)

Challenge:	Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps.
Approach:	They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling.
Outcome:	The proposed method outperforms open- and closed-source models on most metrics.

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Youcheng Huang, Chen Huang, Duanyu Feng, Wenqiang Lei, Jiancheng Lv

DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping (2024.naacl-long)

Challenge:	Prior work has shown that a single LLM’s concept representations can be captured as steering vectors (SVs) prior work has demonstrated that SVs extracted from smaller LLMs can effectively control the behavior of larger LLM.
Approach:	They propose a linear transformation method to bridge LLM concept representations using simple linear transformations to enable efficient cross-model transfer and behavioral control via SVs.
Outcome:	The proposed method bridges concept representations across different LLMs and enables efficient cross-model transfer and behavioral control via SVs.

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Yongrui Chen, Haiyun Jiang, Xinting Huang, Shuming Shi, Guilin Qi

CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)

Challenge:	Existing methods to collect high-quality instruction-response pairs suffer from unaffordable labor costs or severe hallucinations in the self-generation of LLMs.
Approach:	They propose a method that trains LLMs to generate instruction-response pairs based on human-written documents rather than relying solely on self-generation without context.
Outcome:	The proposed method outperforms existing typical methods on multiple benchmarks and shows that it is 100% scalable.

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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

Challenge:	Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown.
Approach:	They propose a benchmark for evaluating large language models using a well-organized taxonomy.
Outcome:	The proposed model is based on a well-organized taxonomy and compares it with other models.

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao

Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning (2024.emnlp-main)

Challenge:	Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio.
Approach:	They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities .
Outcome:	The proposed model improves in simple and complex scenarios with AI feedback learning.

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Qizhou Chen, Taolin Zhang, Xiaofeng He, Dongyang Li, Chengyu Wang, Longtao Huang, Hui Xue’

Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models (2024.acl-long)

Challenge:	Existing methods to correct outdated or erroneous knowledge in large language models (LLMs) are slow and cumbersome, resulting in catastrophic knowledge forgetting and degradation of model performance.
Approach:	They propose a RetriEval-augmented ContInuous Prompt lEarning method that converts knowledge statements into short and informative continuous prompts, prefixed to the LLM’s input query embedding.
Outcome:	The proposed method improves the performance of large language models (LLMs) while maintaining the overall performance of the model.

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Changjiang Gao, Jixing Li, Jiajun Chen, Shujian Huang

Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)

Challenge:	Existing computational metric to quantify extent of meaning composition is lacking . despite extensive neurolinguistic research, understanding how meaning is constructed is difficult .
Approach:	They propose a computational metric to quantify the degree of meaning composition . they use key-value memory interpretation of transformer feed-forward network blocks to study meaning composition.
Outcome:	Experimental results show that the metric correlates with brain clusters associated with word frequency, structural processing, and general sensitivity to words, suggesting multifaceted nature of meaning composition during human sentence comprehension.

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Liyang He, Chenglong Liu, Rui Li, Zhenya Huang, Shulan Ruan, Jun Zhou, Enhong Chen

VisualEDU: A Benchmark for Assessing Coding and Visual Comprehension through Educational Problem-Solving Video Generation (2025.findings-emnlp)

Challenge:	Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability.
Approach:	They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information.
Outcome:	The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis.

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Hao Chen, Tianyu Shi, Pengran Huang, Zeyuan Li, Jiahui Pan, Qianglong Chen, Lewei He

PAL: Persona-Augmented Emotional Support Conversation Generation (2023.findings-acl)

Challenge:	VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . advanced proprietary models show promise, but struggle with increasing task complexity .
Approach:	VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to enhance output quality.
Outcome:	VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to improve output quality.

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Jiale Cheng, Sahand Sabour, Hao Sun, Zhuang Chen, Minlie Huang

CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)

Challenge:	Recent work has demonstrated the effectiveness of dialogue models in providing emotional support due to the lack of human resources for mental health support.
Approach:	They propose a framework for dynamically inferring and modeling seekers’ persona from the conversation history and a model that leverages persona information to provide personalized emotional support.
Outcome:	The proposed model outperforms baseline models on the studied benchmark.

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Libo Qin, Qiguang Chen, Tianbao Xie, Qian Liu, Shijue Huang, Wanxiang Che, Zhou Yu

Specialization through Collaboration: Understanding Expert Interaction in Mixture-of-Expert Large Language Models (2026.eacl-long)

Challenge:	Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm .
Approach:	They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks.
Outcome:	The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement)

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Yuanbo Tang, Naifan Zhang, Yan Tang, Meixuan Chen, Shuhan Huang, Tingyu Cao, Yang Li

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)

Challenge:	Mixture-of-Experts (MoE) based large language models are popular for multitasking . however, whether each expert can specialize to a task remains unclear .
Approach:	They propose to use a dictionary learning approach to analyze expert collaboration mechanisms in MoE LLMs.
Outcome:	The proposed model outperforms existing methods by 2.5% while enabling 50% expert reduction.

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Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu

AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)

Challenge:	Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents.
Approach:	They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process.
Outcome:	The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets .

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Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei

A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)

Challenge:	We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Approach:	They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Outcome:	The proposed system generates public responses considering demographic distributions.

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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu

FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)

Challenge:	Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context.
Approach:	They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model.
Outcome:	The proposed model captures structural information of arguments and is able to identify arguments automatically.

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Yutao Hou, Yihan Jiang, Yuhan Xie, Jian Yang, Liwen Zhang, Hailiang Huang, Guanhua Chen, Yun Chen

Diffusion Language Model Inference with Monte Carlo Tree Search (2026.findings-eacl)

Challenge:	Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks.
Approach:	They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance.
Outcome:	The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches.

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Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng, Sangmin Woo, Balasubramaniam Srinivasan, Zhichao Xu, Kang Zhou, Shuai Wang, Haibo Ding, Lin Lee Cheong

EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence (2026.acl-long)

Challenge:	Existing methods for inference use heuristics to determine which positions to unmask and which tokens to commit . MEDAL is an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference.
Approach:	They propose a framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference.
Outcome:	The proposed framework achieves 22.0% improvement over existing inference strategies across multiple benchmarks.

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Chaoyin She, Ruifang Lu, Lida Chen, Wei Wang, Qinghua Huang

Exploiting Noisy Data in Distant Supervision Relation Classification (N19-1)

Challenge:	Ultrasound is the preferred early cancer screening modality due to non-ionizing radiation, cost-effectiveness, and real-time imaging.
Approach:	They propose to use ultrasound-tailored vision-language models with a mixture-of-experts architecture to train ultrasound-specific knowledge across seven anatomical systems.
Outcome:	The proposed model outperforms Qwen2-VL by 7.58 BLEU-1 and 3.45 ROUGE-1 points in report generation.

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Kaijia Yang, Liang He, Xin-yu Dai, Shujian Huang, Jiajun Chen

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers (2026.acl-demo)

Challenge:	Existing approaches to relation classification are noisy and time-consuming . RCEND uses noisy data to split noisy data into correctly and incorrectly labeled data .
Approach:	They propose a framework to enhance relation classification by exploiting noisy data . they use an instance discriminator with reinforcement learning to split noisy data into correctly and incorrectly labeled data based on the noisy data.
Outcome:	The proposed method outperforms the state-of-the-art models on relation classification . the proposed method is based on a semi-supervised learning method .

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Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)

Challenge:	Existing post-training pipelines that generate QA pairs require costly expert annotation and synthetic data that drops evidence structure.
Approach:	They propose a system that converts raw biomedical papers into evidence-enriched training sets and a domain-specialized VLM.
Outcome:	Ryze synthesizes QA pairs with complete supporting evidence, reduces layout and OCR errors . the system outperforms the base model on LAB-Bench and surpasses GPT-5.2 by +3.8%.

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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs.
Approach:	They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes.
Outcome:	The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning.

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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang

From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)

Challenge:	Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections.
Approach:	They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models.
Outcome:	The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages.

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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu

Challenge:	Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios .
Approach:	They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios.
Outcome:	The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm.

Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings (2021.emnlp-main)

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Junkun Chen, Renjie Zheng, Atsuhito Kita, Mingbo Ma, Liang Huang

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)

Challenge:	Existing systems for simultaneous translation are still trained on full-sentence bitexts due to the abundance of unnecessary long-distance reorderings.
Approach:	They propose to rewrite target side of existing full-sentence corpora into simultaneous-style translation by adding generated pseudo-references to the target side.
Outcome:	Experiments on ZhEn and JaEn simultaneous translation show that the proposed method improves on existing full-sentence corpora.

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Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li

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

Challenge:	Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction.
Approach:	They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning.
Outcome:	The proposed agent performs well in both dialogue element modeling and out-of-domain tasks.

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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.

Toward Automated Robustness Evaluation of Mathematical Reasoning (2026.findings-acl)

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Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang, Zhaoqian Dai, Hailiang Huang, Yun Chen, Guanhua Chen

ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing robustness evaluations rely on hand-crafted templates or a limited set of perturbation rules, resulting in model failure.
Approach:	They propose a framework inspired by software stress testing that generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure.
Outcome:	The proposed framework generates adversarial variants dynamically for each LLM, minimizing the risk of data contamination.

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Hao Chen, Yukun Yan, Sen Mei, Wanxiang Che, Zhenghao Liu, Qi Shi, Xinze Li, Yuchun Fan, Pengcheng Huang, Qiushi Xiong, Zhiyuan Liu, Maosong Sun

Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models (2024.findings-emnlp)

Challenge:	Existing RAG systems often underutilize the retrieved documents, authors say . they fail to extract and integrate key clues needed to support faithful and interpretable reasoning .
Approach:	a new framework extracts key clues from retrieved content and generates multiple reasoning paths . the framework optimizes the model by selecting the most appropriate reasoning path .
Outcome:	Experiments show that ClueAnchor outperforms baseline RAG frameworks in completeness and robustness.

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Cheng-Hsun Hsueh, Paul Huang, Tzu-Han Lin, Che Liao, Hung-Chieh Fang, Chao-Wei Huang, Yun-Nung Chen

Harder Task Needs More Experts: Dynamic Routing in MoE Models (2024.acl-long)

Challenge:	Knowledge editing is a promising technique for updating factual knowledge in large language models (LLMs) but studies have identified side effects such as knowledge distortion and the deterioration of general abilities that have emerged after editing.
Approach:	They propose to evaluate the side effects of knowledge editing in large language models using metrics and benchmarks.
Outcome:	The results of the study highlight the limitations of current knowledge editing methods and outline potential research directions.

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Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Kun Xu, Liwei Chen, Songfang Huang, Yansong Feng

TreeRAG: Unleashing the Power of Hierarchical Storage for Enhanced Knowledge Retrieval in Long Documents (2025.findings-acl)

Challenge:	Unlike existing MoE approaches that rely on fixed TopK Routing, our dynamic expert selection framework dynamically allocates experts based on the confidence level in expert selection for each input.
Approach:	They propose a dynamic expert selection framework that dynamically allocates experts based on the confidence level in expert selection for each input.
Outcome:	The proposed method achieves an average improvement of 0.7% with less than 90% activated parameters and outperforms dense models in QA and machine translation tasks.

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Wenyu Tao, Xiaofen Xing, Yirong Chen, Linyi Huang, Xiangmin Xu

Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use (2024.acl-long)

Challenge:	Traditional RAG frameworks struggle to retrieve all relevant knowledge points . a new approach to retrieve long documents is proposed to improve performance in NLP .
Approach:	They propose a tree-based approach to document knowledge retrieval that preserves hierarchical structure . treeRAG is a key technique for enhancing the text generation capabilities of Large Language Models .
Outcome:	The proposed approach improves recall quality and precision compared to existing methods and better performance to question-answering tasks.

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Yuhan Chen, Ang Lv, Ting-En Lin, Changyu Chen, Yuchuan Wu, Fei Huang, Yongbin Li, Rui Yan

MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)

Challenge:	In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models significantly affects their performance in tasks demanding a high degree of context awareness.
Approach:	They propose a method that compensates an attention trough with an attention peak by a process to enhance the model's awareness to various contextual positions.
Outcome:	The proposed method improves the performance of a 7B model on the largest tool-use benchmark, comparable to that of GPT-4.

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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng

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

Challenge:	Existing approaches to cluster graphs with GNNs are limited due to label scarcity.
Approach:	They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals.
Outcome:	The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals.

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Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

ZARA: Improving Few-Shot Self-Rationalization for Small Language Models (2023.findings-emnlp)

Challenge:	Distant supervision models suffer from high label noise and are not reliable for DS.
Approach:	They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF.
Outcome:	The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function.

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Wei-Lin Chen, An-Zi Yen, Cheng-Kuang Wu, Hen-Hsen Huang, Hsin-Hsi Chen

Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)

Challenge:	Recent studies demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars.
Approach:	They propose to leverage explanations for small LMs to improve few-shot self-rationalization by reducing the problem of plausibility judgement to natural language inference.
Outcome:	The proposed approach achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric.

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Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, Yin Zhang

Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)

Challenge:	Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks .
Approach:	They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates.
Outcome:	The proposed model achieves new SOTA on CSQA, QASC, and OBQA.

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Chen Xu, Bojie Hu, Yanyang Li, Yuhao Zhang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu

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

Challenge:	End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it.
Approach:	They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency .
Outcome:	The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks.

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

Depression Detection in Clinical Interviews with LLM-Empowered Structural Element Graph (2024.naacl-long)

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.

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Zhuang Chen, Jiawen Deng, Jinfeng Zhou, Jincenzi Wu, Tieyun Qian, Minlie Huang

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing methods for assessing depression only capture part of relevant elements . scarcity of participant data constrains interview modeling due to privacy concerns .
Approach:	They propose a structural element graph (SEGA) that transforms clinical interviews into an expertise-inspired directed acyclic graph for comprehensive modeling.
Outcome:	The proposed model outperforms baseline methods and powerful LLMs on two real-world clinical datasets.

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Wenhao Zhu, Qianfeng Zhao, Yunzhe Lv, Shujian Huang, Siheng Zhao, Sizhe Liu, Jiajun Chen

Detection, Diagnosis, and Explanation: A Benchmark for Chinese Medial Hallucination Evaluation (2024.lrec-main)

Challenge:	kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants .
Approach:	They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks .
Outcome:	The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization.

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Chengfeng Dou, Ying Zhang, Yanyuan Chen, Zhi Jin, Wenpin Jiao, Haiyan Zhao, Yu Huang

All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection (2026.acl-long)

Challenge:	Large Language Models (LLMs) have made significant progress in recent years, but their practical use is hindered by their tendency to generate hallucinations.
Approach:	They propose to use ICD-10 and MeSH to evaluate LLMs' ability to detect medical hallucinations and make accurate diagnoses in noisy environments.
Outcome:	The proposed benchmark can be used to evaluate LLMs’ ability to detect medical hallucinations, make accurate diagnoses in noisy conditions, and provide plausible explanations.

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Yuechen Jiang, Zhiwei Liu, Yupeng Cao, Yueru He, Ziyang Xu, Chen Xu, Zhiyang Deng, Prayag Tiwari, Xi Chen, Alejandro Lopez-Lira, Jimin Huang, Junichi Tsujii, Sophia Ananiadou

From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)

Challenge:	RFC-Bench evaluates large language models on financial misinformation under realistic news . current models struggle to maintain coherent belief states without external grounding, study finds .
Approach:	They propose a benchmark for evaluating large language models on financial misinformation under realistic news.
Outcome:	The proposed model performs better when context is available, while reference-free settings expose significant weaknesses.

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Tianle Gu, Kexin Huang, Ruilin Luo, Yuanqi Yao, Xiuying Chen, Yujiu Yang, Yan Teng, Yingchun Wang

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

Challenge:	Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks.
Approach:	They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage.
Outcome:	Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility.

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

mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024.emnlp-main)

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

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Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen

VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery (2026.findings-eacl)

Challenge:	Recent studies have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement.
Approach:	They propose a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference.
Outcome:	The proposed method significantly improves performance on two multimodal LLMs of different sizes and three widely used benchmarks.

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Jinchao Ge, Tengfei Cheng, Biao Wu, Zeyu Zhang, Shiya Huang, Judith Bishop, Gillian Shepherd, Meng Fang, Ling Chen, Yang Zhao

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

Challenge:	MLLMs that use domain-specific data are limited in understanding cultural heritage artifacts such as ancient Greek pottery . supervised fine-tuning improves adaptation to domain knowledge, but it struggles with deeper reasoning tasks.
Approach:	They propose a visual question-answer tool that augments SFT with reinforcement learning using verifiable rewards.
Outcome:	The proposed model outperforms baseline models on reasoning-intensive questions on ancient Greek pottery.

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Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)

Challenge:	Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach:	They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
Outcome:	The proposed dataset can be used to evaluate Chinese grammar errors in Chinese.

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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values.
Approach:	They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values .
Outcome:	The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets.

An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval (2023.findings-emnlp)

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Weiqing Luo, Qiaosheng Chen, Zhiyang Zhang, Zixian Huang, Gong Cheng

Palette of Language Models: A Solver for Controlled Text Generation (2025.naacl-long)

Challenge:	Existing methods for ad hoc dataset retrieval are lexical and cannot capture semantic similarity.
Approach:	They propose to implement and evaluate a set of implicit and explicit knowledge-enhancement retrieval methods on two test collections to find semantic matches for ad hoc dataset retrieval.
Outcome:	The proposed methods are compared with existing methods on two test collections and reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice.

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Zhe Yang, Yi Huang, Yaqin Chen, XiaotingWu XiaotingWu, Junlan Feng, Chao Deng

Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)

Challenge:	Recent advances in large language models have revolutionized text generation with their remarkable capabilities.
Approach:	They propose to combine a single-attribute model with a discriminative model to achieve a combination strategy that incorporates positive correlation and attribute enhancement.
Outcome:	The proposed method is adapted for single-attribute control scenario and achieves surpassing results.

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Sen Yang, Shujian Huang, Wei Zou, Jianbing Zhang, Xinyu Dai, Jiajun Chen

Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation (2022.coling-1)

Challenge:	Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features.
Approach:	They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations.
Outcome:	The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation.

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Hongxiao Zhang, Hui Huang, Jiale Gao, Yufeng Chen, Jinan Xu, Jian Liu

Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)

Challenge:	Existing back-translation methods focus on in-domain lexical knowledge, which may lead to poor translation of unseen in- domain words.
Approach:	They propose an iterative constrained back-translation method to incorporate in-domain lexical knowledge into synthetic parallel data from BT.
Outcome:	The proposed method improves the BLEU score by up to 3.08 on four domains.

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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)

Challenge:	Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals.
Approach:	They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals.
Outcome:	The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations.

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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang

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

Challenge:	TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites .
Approach:	They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs .
Outcome:	The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification .

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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.

ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis (2026.findings-acl)

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Haitao Li, Chunxiang Jin, Chenglin Li, Wenhao Guan, Zhengxing Huang, Xie Chen

Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering (2026.findings-acl)

Challenge:	Recent controllable zero-shot text-to-speech systems can synthesize speech for unseen speakers from a short reference audio clip, but they also inherit the speaking style present in the reference.
Approach:	They propose a framework that enables continuous and reference-relative style control in zero-shot text-to-speech systems by combining style-specific LoRAs with Orthogonal LoRA Fusion.
Outcome:	The proposed framework reduces the model's dependence on reference style while preserving text fidelity while maintaining intelligibility and speaker timbre.

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Weikang Zhang, Zimo Zhu, Zhichuan Yang, Chen Huang, Wenqiang Lei, See-Kiong Ng

Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)

Challenge:	Existing methods for training patients with cognitive impairment rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across domains and severity levels.
Approach:	They propose to use steering vectors from contrastive pairs of instructions and responses to capture domain-specific features and introduce a Stochastic Token Modulation mechanism to regulate the intervention probability.
Outcome:	The proposed model outperforms baselines in clinical authenticity and severity controllability while remaining open-source.

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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu

Jailbreak LLMs through Internal Stance Manipulation (2025.emnlp-main)

Challenge:	Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs).
Approach:	They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts.
Outcome:	The proposed framework outperforms strong baselines while significantly improving explainability.

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Shuangjie Fu, Du Su, Beining Huang, Fei Sun, Jingang Wang, Wei Chen, Huawei Shen, Xueqi Cheng

Privacy Implications of Retrieval-Based Language Models (2023.emnlp-main)

Challenge:	Existing approaches to exploit LLMs' inherent safety mechanism, including GCG and AutoDAN, are ineffective for certain malicious requests.
Approach:	They propose a method that generates jailbreak prompts to suppress a refusal stance and induce affirmative responses by modifying adversarial prompts.
Outcome:	The proposed method outperforms the best baseline approach in Llama-2-7b-chat and achieves a 92.2% success rate across all models.

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Yangsibo Huang, Samyak Gupta, Zexuan Zhong, Kai Li, Danqi Chen

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

Challenge:	a study of retrieval-based language models shows improved interpretability, factuality, and adaptability compared to parametric counterparts . kNN-LMs are more susceptible to leaking private information from their private datastore than parametric models .
Approach:	They present the first study of privacy risks in retrieval-based language models . they aim to strike a balance between utility and privacy in domains where privacy is of concern .
Outcome:	The proposed methods improve interpretability, factuality, and adaptability compared to parametric models . the study finds that kNN-LMs are more susceptible to leaking private data than parametric ones .

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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

Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)

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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Yubo Chen, Yunqi Zhang, Yongfeng Huang

Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos (2026.acl-long)

Challenge:	Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples.
Approach:	They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths.
Outcome:	The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions.

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Haodong Chen, Qiang Huang, Jiaqi Zhao, Qiuping Jiang, Xiaojun Chang, Jun Yu

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

Challenge:	Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings . attribution under visual confounding is a central challenge in measuring social bias .
Approach:	They propose a face-only counterfactual evaluation paradigm that isolates demographic effects while preserving real-image realism.
Outcome:	The proposed paradigm isolates demographic effects while preserving real-image realism.

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

SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)

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

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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei

Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)

Challenge:	Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits.
Approach:	They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior.
Outcome:	The proposed model achieves state-of-the-art performance in multiple social media tasks.

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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang

One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)

Challenge:	Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency.
Approach:	They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them.
Outcome:	The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens.

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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)

Challenge:	Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality.
Approach:	They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets.
Outcome:	Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks.

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Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang

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

Challenge:	Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles.
Approach:	They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words.
Outcome:	The proposed generator significantly improves performance and provides explainable word alignments.

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

Learning to Generate Explanation from e-Hospital Services for Medical Suggestion (2022.coling-1)

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

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Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen

UCL-Bench: A Chinese User-Centric Legal Benchmark for Large Language Models (2025.findings-naacl)

Challenge:	Neural models have shown remarkable success in various tasks, however, simply offering the predictions may not satisfy the requirement of end-users.
Approach:	They propose a novel model which generates a medical suggestion and provides an explanation as the outline of the reasoning.
Outcome:	The proposed model achieves promising performances in both quantitative and human evaluation.

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Ruoli Gan, Duanyu Feng, Chen Zhang, Zhihang Lin, Haochen Jia, Hao Wang, Zhenyang Cai, Lei Cui, Qianqian Xie, Jimin Huang, Benyou Wang

Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)

Challenge:	Existing legal benchmarks focusing on knowledge and logic evaluate LLMs on various tasks in legal domain, but few have explored the practical application of LLM by actual users.
Approach:	They propose a Chinese user-centric legal benchmark that aims to assess the practical application of LLMs by real users.
Outcome:	The proposed model outperforms existing models on various tasks in legal domain but does not outperfect ChatGPT.

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Wei Huang, Chen Liu, Bo Xiao, Yihua Zhao, Zhaoming Pan, Zhimin Zhang, Xinyun Yang, Guiquan Liu

THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption (2022.findings-acl)

Challenge:	Existing methods for hierarchical text classification are lacking in the field of natural language processing.
Approach:	They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels.
Outcome:	The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro.

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Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, Furu Wei

A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)

Challenge:	enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks.
Approach:	They propose an approximation approach for transformers which enables inference on ciphertext data.
Outcome:	The proposed approach can infer pre-trained models on encrypted data with negligible performance drop but enjoy theory-guaranteed privacy-preserving advantage.

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Houquan Zhou, Zhenghua Li, Bo Zhang, Chen Li, Shaopeng Lai, Ji Zhang, Fei Huang, Min Zhang

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

Challenge:	Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes.
Approach:	They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task.
Outcome:	The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models.

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

Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards (2026.acl-long)

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

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Youliang Yuan, Qiuyang Mang, Jingbang Chen, Hong Wan, Xiaoyuan Liu, Junjielong Xu, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Pinjia He

Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension (2026.findings-acl)

Challenge:	Existing models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability.
Approach:	They propose a Rubric Reward Model that rewards the entire reasoning trajectory against problem-specific rubrics.
Outcome:	The proposed model outperforms outcome-only supervision on four math benchmarks and boosts Verified Pass@1024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%.

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Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, Kai Zhang

Communication-Efficient Desire Alignment for Proactive Embodied Human–Agent Interaction (2026.acl-long)

Challenge:	Existing training-free methods for extrapolating beyond training context lengths are semantics-agnostic . Existing methods that focus on relative token distances can indiscriminately blur semantically relevant and irrelevant tokens .
Approach:	They propose an adaptive positional zooming method that uses semantic relevance to extrapolate beyond training context lengths.
Outcome:	Experiments show that RiPRA outperforms existing training-free extrapolation methods . relevant tokens get higher positional resolution, while irrelevant tokens are compressed .

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Yuanfei Wang, Xinju Huang, Fangwei Zhong, Yaodong Yang, Yizhou Wang, Yuanpei Chen, Hao Dong

FactAlign: Long-form Factuality Alignment of Large Language Models (2024.findings-emnlp)

Challenge:	Effective real-world human–agent interactions are long-term and repeated.
Approach:	They propose a simulation that uses a proxy user with value-driven preferences and natural language behavior to evaluate how agents adapt to users across interactions and satisfy their desires.
Outcome:	HA-Desire, a home assistance simulation, shows that agents can adapt to user needs and provide proactive assistance within limited communication.

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Chao-Wei Huang, Yun-Nung Chen

Towards Low-Resource Harmful Meme Detection with LMM Agents (2024.emnlp-main)

Challenge:	Large language models have demonstrated significant potential as the next-generation information access engines, but reliability is hindered by issues of hallucination and generating non-factual content.
Approach:	They propose a novel alignment framework that enhances the factuality of LLMs’ long-form responses while maintaining their helpfulness.
Outcome:	The proposed framework improves factuality of LLMs while maintaining helpfulness.

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Jianzhao Huang, Hongzhan Lin, Liu Ziyan, Ziyang Luo, Guang Chen, Jing Ma

DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)

Challenge:	Existing methods for harmful meme detection are limited due to the dynamic nature of memes . eliciting knowledge-revising behavior within the LMM agent is a key factor in achieving this goal .
Approach:	They propose an agency-driven framework for low-resource harmful meme detection . they use annotated memes to leverage label information as auxiliary signals for model .
Outcome:	The proposed framework achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task.

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Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu

GTA: Generating Long-horizon Tasks for Web Agents at Scale (2026.acl-long)

Challenge:	Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly.
Approach:	They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop.
Outcome:	The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop.

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Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey, Yilun Zhou, Muhao Chen, Jonathan May, Chien-Sheng Wu

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)

Challenge:	Existing benchmarks provide only coarse start–goal annotations without intermediate trajectories . Existing frameworks provide no supervision over the agent's latent decision process .
Approach:	They propose a framework that integrates crawling, retrieval-based seeding, in-context generation and automated quality control to produce realistic tasks paired with executable trajectories.
Outcome:	The proposed framework decouples crawling from generation for greater efficiency and ensures dense supervision through deterministic replays and systematic validation.

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Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Jun Huang, Ming Gao, Xiang Li

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

Challenge:	Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated.
Approach:	They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty.
Outcome:	The proposed framework improves performance and efficiency over multiple tasks over multiple datasets.

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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

Challenge:	Existing methods focus on knowledge and linguistic patterns of characters.
Approach:	They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome:	The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.

Why Machine Reading Comprehension Models Learn Shortcuts? (2021.findings-acl)

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Yuxuan Lai, Chen Zhang, Yansong Feng, Quzhe Huang, Dongyan Zhao

Machine Reading Comprehension

Challenge:	Existing studies show that many MRC models learn shortcuts to outwit benchmarks, but the performance is unsatisfactory in real-world applications.
Approach:	They propose to use shortcut questions to analyze learning difficulty of MRC models . they propose to analyze the learning difficulty regarding shortcut and challenging questions .
Outcome:	The proposed methods show that a large proportion of shortcut questions in training data make models rely on shortcut tricks excessively.

Guiding Variational Response Generator to Exploit Persona (2020.acl-main)

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Bowen Wu, MengYuan Li, Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun Wang

RecMind: Large Language Model Powered Agent For Recommendation (2024.findings-naacl)

Challenge:	Neural Response Generators (NRGs) use persona information of users to perform personalized conversations . current studies focus on incorporating explicit meta-data of user profiles or character descriptions to generate persona-aware responses.
Approach:	They propose to use persona information of users in Neural Response Generators to perform personalized conversations.
Outcome:	The proposed method improves persona-aware response generation and the metrics are reasonable to evaluate them.

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Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, Yingzhen Yang

Can Large Language Models Understand Internet Buzzwords Through User-Generated Content (2025.acl-long)

Challenge:	Existing recommendations systems are limited in generalizing to new tasks due to model scale and data size constraints.
Approach:	They propose an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge to provide zero-shot personalized recommendations.
Outcome:	The proposed model outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.

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Chen Huang, Junkai Luo, Xinzuo Wang, Wenqiang Lei, Jiancheng Lv

CARER: Contextualized Affect Representations for Emotion Recognition (D18-1)

Challenge:	Existing methods for generating definitions of internet buzzwords rely on user-generated content, such as posts and reviews, to understand them.
Approach:	They propose a method to generate accurate buzzword definitions using UGC as examples.
Outcome:	The proposed method mirrors human language learning skills and can produce more accurate definitions.

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Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, Yi-Shin Chen

Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference (2023.emnlp-main)

Challenge:	Existing methods to model emotion-relevant content are based on rule-based and statistics-based approaches.
Approach:	They propose a semi-supervised graph-based algorithm to produce rich structural descriptors . they use word embeddings to evaluate the algorithm on emotion recognition tasks .
Outcome:	The proposed method outperforms state-of-the-art methods on emotion recognition tasks.

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Biao Fu, Minpeng Liao, Kai Fan, Zhongqiang Huang, Boxing Chen, Yidong Chen, Xiaodong Shi

SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)

Challenge:	Existing approaches to streaming speech translation use an offline model with a wait-k policy . however, there is a mismatch problem with an offline inference model trained with complete utterances .
Approach:	They propose an offline streaming speech translation model with wait-k policy to support different latency requirements.
Outcome:	The proposed model achieves better trade-offs between translation quality and latency than baselines.

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Yan Yang, Zeguan Xiao, Xin Lu, Hongru Wang, Xuetao Wei, Hailiang Huang, Guanhua Chen, Yun Chen

Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)

Challenge:	Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks.
Approach:	They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically.
Outcome:	The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios.

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Libo Qin, Qiguang Chen, Fuxuan Wei, Shijue Huang, Wanxiang Che

Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)

Challenge:	Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development.
Approach:	They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages.
Outcome:	The proposed method outperforms existing prompting methods on several benchmarks.

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Chen Wang, Yuchen Liu, Boxing Chen, Jiajun Zhang, Wei Luo, Zhongqiang Huang, Chengqing Zong

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

Challenge:	Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data .
Approach:	They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space.
Outcome:	The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines.

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

A Semantic-based Method for Unsupervised Commonsense Question Answering (2021.acl-long)

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

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Yilin Niu, Fei Huang, Jiaming Liang, Wenkai Chen, Xiaoyan Zhu, Minlie Huang

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods to score candidates without labeled task data are difficult to use . e.g., pre-trained language models can be easily affected by irrelevant factors .
Approach:	They propose a method that generates plausible answers with generative models and uses them to select the correct answer.
Outcome:	The proposed method achieves the best results in unsupervised situations.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

Challenge:	Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English.
Approach:	They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy.
Outcome:	The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath).

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

GeoAgent: To Empower LLMs using Geospatial Tools for Address Standardization (2024.findings-acl)

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Chenghua Huang, Shisong Chen, Zhixu Li, Jianfeng Qu, Yanghua Xiao, Jiaxin Liu, Zhigang Chen

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

Challenge:	Existing approaches to address address standardization are lacking in the current field.
Approach:	They propose a framework that incorporates spatial knowledge into address texts and achieves efficient address standardization.
Outcome:	The proposed framework incorporates spatial knowledge into address texts and achieves efficient address standardization.

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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

MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)

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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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang

Learning to Maximize Mutual Information for Chain-of-Thought Distillation (2024.findings-acl)

Challenge:	Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture.
Approach:	They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition.
Outcome:	The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module.

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Xin Chen, Hanxian Huang, Yanjun Gao, Yi Wang, Jishen Zhao, Ke Ding

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)

Challenge:	Knowledge distillation is a technique of transferring knowledge from large, complex models to smaller ones.
Approach:	They propose a method utilizing chain-of-thought distillation to transfer knowledge from large, complex models to smaller ones by maximizing mutual information of the representation features of the two tasks.
Outcome:	The proposed method outperforms the state-of-the-art knowledge distillation method on four datasets.

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Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, Ruifeng Xu

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

Challenge:	Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection .
Approach:	They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples.
Outcome:	The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection.

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

BLSP-Emo: Towards Empathetic Large Speech-Language Models (2024.emnlp-main)

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

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Chen Wang, Minpeng Liao, Zhongqiang Huang, Junhong Wu, Chengqing Zong, Jiajun Zhang

Real-time Factuality Assessment from Adversarial Feedback (2025.acl-long)

Challenge:	BLSP-Emo model understands both semantics and emotions in speech and generates empathetic responses.
Approach:	They propose a language-speech pretraining with emotion support that utilizes existing speech and emotion recognition datasets to create an end-to-end speech-language model.
Outcome:	The proposed model can understand both semantics and emotions in speech and generate empathetic responses.

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Sanxing Chen, Yukun Huang, Bhuwan Dhingra

Hyperbolic Capsule Networks for Multi-Label Classification (2020.acl-main)

Challenge:	Existing evaluations for assessing the factuality of news from conventional sources, such as claims on fact-checking websites, result in high accuracies over time for LLM-based detectors.
Approach:	They propose a pipeline that leverages natural language feedback from a RAG-based detector to iteratively modify real-time news into deceptive variants that challenge LLMs.
Outcome:	The proposed pipeline reduces the binary classification ROC-AUC by 17.5 percent for a strong RAG-based GPT-4o detector.

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Boli Chen, Xin Huang, Lin Xiao, Liping Jing

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

Challenge:	Existing methods for classification of labels are limited by feature aggregation and encoding.
Approach:	They propose to use hyperbolic capsule networks to capture fine-grained label information . they also propose a new routing method to adaptively adjust capsule number during routing .
Outcome:	The proposed method significantly improves the performance of multi-label classification on tail labels.

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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.

TableVLM: Multi-modal Pre-training for Table Structure Recognition (2023.acl-long)

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Leiyuan Chen, Chengsong Huang, Xiaoqing Zheng, Jinshu Lin, Xuanjing Huang

ATLAS: Agent Tuning via Learning Critical Steps (2025.findings-acl)

Challenge:	Tables are useful for displaying data in an organized manner, but they are difficult to extract from images because of their structure, notation, and representation.
Approach:	They propose a multi-modal pre-training model for table structure recognition that captures table structure-related features by multiple unsupervised objectives inspired by masked visual-language modeling.
Outcome:	The proposed model improves tree-editing-distance-score on ComplexTable by 1.97% .

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Zhixun Chen, Ming Li, Yuxuan Huang, Yali Du, Meng Fang, Tianyi Zhou

LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)

Challenge:	Existing agent tuning approaches employ supervised finetuning on entire expert trajectories, but behavior-cloning of full traitories introduces expert bias and weakens generalization to states not covered by the expert data.
Approach:	They propose a method that finetunes LLMs on critical steps in expert trajectories and identifies and finetuns them on these steps with reduced costs.
Outcome:	The proposed method outperforms existing methods and open-source LLM agents on only 30% critical steps in extensive experiments.

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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)

Challenge:	LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions.
Approach:	They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results.
Outcome:	The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs.

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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman

Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy (2021.findings-emnlp)

Challenge:	State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text.
Approach:	They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling.
Outcome:	The proposed model can be used to train sentences on language modeling tasks.

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Tao Huang, Hong Chen

MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)

Challenge:	Latent Dirichlet allocation (LDA) is a widely used topic model to discover the latent semantic of text data.
Approach:	They propose to combine a subsampling method with CGS to improve efficiency while amplifying privacy by using a novel metric, the efficiency–privacy function.
Outcome:	The proposed algorithm improves efficiency while amplifying privacy while subsampling in CGS increases efficiency while preserving privacy.

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Yanghao Zhou, Haitian Li, Rexar Lin, Heyan Huang, Jinxing Zhou, Changsen Yuan, Tian Lan, Ziqin Zhou, Yudong Li, Jiajun Xu, Jingyun Liao, YiMing Cheng, Xuefeng Chen, Xian-Ling Mao, Yousheng Feng

MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis (2026.acl-long)

Challenge:	Existing evaluation benchmarks for text-to-audio-video (T2AV) generation are largely designed for human-recorded videos or single-speaker settings.
Approach:	They propose a failure-driven diagnostic benchmark for multi-talker dialogue-centric audio-video generation.
Outcome:	The benchmark evaluates multi-speaker dialogue generation at four levels: audio-visual signal fidelity, temporal attribute consistency, social interaction, and cinematic expression.

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Wenting Chen, Guolin Huang, Wenxuan Wang, Zhongrui Zhu

ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)

Challenge:	Existing medical benchmarks fail to detect the Einstellung Effect in clinical diagnosis . Existing models exhibit the Einstellung effect, relying on statistical shortcuts rather than logical reasoning.
Approach:	They propose a counterfactual benchmark that uses statistical shortcuts to diagnose patients . they propose CGME-based system that iteratively refines reasoning paths .
Outcome:	The proposed model achieves high baseline accuracy but severe bias trap rates . iteratively refines reasoning paths in an exemplar base and consolidates disease-specific knowledge into illness graphs.

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Yuhao Zhou, Wenxiang Chen, Rui Zheng, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction (L18-1)

Challenge:	Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness.
Approach:	They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks.
Outcome:	The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning.

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Chin-Ho Lin, Hen-Hsen Huang, Hsin-Hsi Chen

An ensemble CNN method for biomedical entity normalization (D19-57)

Challenge:	Currently, the construction and updating of knowledge bases rely on human labor.
Approach:	They propose to map relational phrases in triples from natural language to knowledge base predicate format.
Outcome:	The proposed mapping results show high quality and promising coverage on relational phrases compared to previous research.

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Pan Deng, Haipeng Chen, Mengyao Huang, Xiaowen Ruan, Liang Xu

Knowledge Representation

Challenge:	Named entity recognition (NER) and entity normalization (entity linking) are two fundamental natural language processing tasks to achieve entity normalizing.
Approach:	They propose a CNN method that normalizes microbiology-related entities to concepts in standard dictionaries.
Outcome:	The proposed method performs well in the BioNLP-OST19 shared task Bacteria Biotope.

Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling (2026.acl-long)

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Qiyuan Chen, Hongsen Huang, Jiahe Chen, Qian Shao, Jintai Chen, Hongxia Xu, Renjie Hua, Ren Chuan, Jian Wu

Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)

Challenge:	Existing multimodal reward models are interpretable but slow, while discriminative ones are opaque "black boxes."
Approach:	They propose a framework that dynamically decomposes evaluation into granular, interpretable dimensions.
Outcome:	The proposed framework outperforms open-source reward models on benchmarks like VL-RewardBench.

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Wei Huang, Yizhe Xiong, Xin Ye, Zhijie Deng, Hui Chen, Zijia Lin, Guiguang Ding

TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)

Challenge:	Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data.
Approach:	They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes .
Outcome:	The proposed framework preserves the benefits of token-level reasoning while reducing computational cost.

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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors.
Approach:	They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages.
Outcome:	The proposed model can translate audio-visual speech into audio-visual speech in other languages.

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Rui Qi, Zhibo Man, Yufeng Chen, Fengran Mo, Jinan Xu, Kaiyu Huang

SAM3-I: Segment Anything with Instructions (2026.acl-long)

Challenge:	Large Language Models struggle with multilingual reasoning tasks due to resource constraints . a training-free method improves performance on multilingual thinking tasks .
Approach:	They propose a training-free method that transforms language-specific semantic information into language-agnostic structured representations.
Outcome:	The proposed method outperforms strong baselines on multilingual reasoning tasks.

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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

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Long Chen, Shuoyu Guan, Xiaohua Huang, Wen-Jing Wang, Cai Xu, Ziyu Guan, Wei Zhao

Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020.aacl-main)

Challenge:	Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios.
Approach:	They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families.
Outcome:	The proposed method is superior to existing methods and can handle low-resource languages.

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Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, Jun Zhao

ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)

Challenge:	Existing approaches for event extraction focus on sentence-level event extraction, but they lack a broader view of the document context.
Approach:	They build graphs with candidate event filler extractions enriched by sentential embeddings as nodes and use graph attention networks to identify event regions in a document and aggregate event information.
Outcome:	The proposed method performs well on two languages and shows that it is faster than previous methods.

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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu

Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning (2026.acl-long)

Challenge:	Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead.
Approach:	ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.
Outcome:	ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.

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Weiqin Wang, Yile Wang, Kehao Chen, Hui Huang

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

Challenge:	RLVR is a paradigm for improving reasoning ability of large language models . but voting results often induce confirmation bias and suffer from sparse rewards .
Approach:	They propose a framework integrating model confidence and dynamic subgroup partitioning to address these issues.
Outcome:	The proposed framework outperforms recent baselines on multiple models and benchmarks.

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang

Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers (2026.acl-long)

Challenge:	Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed .
Approach:	They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks.
Outcome:	The proposed framework improves image quality and inference speed and can be used in industrial applications.

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Xin Chen, Feng Jiang, Yiqian Zhang, Hardy Chen, Shuo Yan, Wenya Xie, Min Yang, Shujian Huang

Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference (2025.emnlp-main)

Challenge:	Existing reasoning-oriented LLMs lack a blind self-thinking paradigm . current models fail to recognize when their reasoning is underinformed or based on ambiguous user instructions .
Approach:	They propose a new reasoning paradigm that transforms LLMs into proactive inquirers that interleave reasoning with clarification.
Outcome:	The proposed model outperforms baseline models on mathematical reasoning, code generation, and document editing.

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Zhuo Chen, Xinyu Wang, Yong Jiang, Zhen Zhang, Xinyu Geng, Pengjun Xie, Fei Huang, Kewei Tu

R-PRM: Reasoning-Driven Process Reward Modeling (2025.emnlp-main)

Challenge:	Existing methods to detect the knowledge boundary of Vision Large Language Models (VLLMs) are expensive and require indiscriminate retrieval to address questions that require real-time information or are knowledge-intensive.
Approach:	They propose a method that fine-tunes a VLLM on an automatically constructed dataset for boundary identification.
Outcome:	The proposed method reduces indiscriminate retrieval while maintaining or improving the performance of a VLLM on an automatically constructed dataset.

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Shuaijie She, Junxiao Liu, Yifeng Liu, Jiajun Chen, Xin Huang, Shujian Huang

Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)

Challenge:	Existing Process Reward Models (PRMs) output evaluation scores directly, limiting both learning efficiency and evaluation accuracy.
Approach:	They propose a Reasoning-Driven Process Reward Modeling (R-PRM) which activates inherent reasoning to enhance process-level evaluation.
Outcome:	The proposed model outperforms baseline models on ProcessBench and PRMBench by 13.9 and 8.5 F1 scores.

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Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, Jiajun Chen

INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation (2023.acl-long)

Challenge:	Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables .
Approach:	They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs .
Outcome:	The proposed model achieves comparable or better performance in machine translation tasks than strong baselines.

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Wenhao Zhu, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen

Enhancing Large Vision-Language Models with Ultra-Detailed Image Caption Generation (2025.emnlp-main)

Challenge:	Neural machine translation models induce a non-smooth representation space, which harms its generalization results.
Approach:	They propose a framework to smooth the representation space by adjusting neighbor representations with a small number of new parameters.
Outcome:	The proposed framework outperforms the state-of-the-art kNN-MT system with average gains of 1.99 COMET and 1.0 BLEU on four benchmark datasets.

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Yu Zeng, Yukun Qi, Yiming Zhao, Xikun Bao, Lin Chen, Zehui Chen, Shiting Huang, Jie Zhao, Feng Zhao

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

Challenge:	Existing pipelines for generating high-quality, ultra-detailed image captions are limited by the scarcity of image caption data.
Approach:	They propose a pipeline for generating high-quality, ultra-detailed image captions that integrates both pre-processing and post-processor stages.
Outcome:	The proposed pipeline improves LVLMs' perception and cognitive abilities across multiple vision-language benchmarks.

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SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking (2026.acl-long)

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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Weiyang Huang, Xuefeng Bai, Kehai Chen, Xinyang Chen, Yibin Chen, Weili Guan, Min Zhang

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

Challenge:	Large Reasoning Models (LRMs) produce excessively long Chains of Thought (COT) Existing solutions that improve token efficiency but sacrifice fine-grained control can disrupt the logical integrity of the reasoning process.
Approach:	They propose a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure.
Outcome:	Experiments show that SAT reduces reasoning tokens by 40% while maintaining or improving accuracy.

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Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen

Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)

Challenge:	Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability.
Approach:	They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain.
Outcome:	The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset.

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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li

Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)

Challenge:	Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored.
Approach:	They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation.
Outcome:	The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs.

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Qingbao Huang, Jielong Wei, Yi Cai, Changmeng Zheng, Junying Chen, Ho-fung Leung, Qing Li

X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification (2024.findings-acl)

Challenge:	Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words.
Approach:	They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question.
Outcome:	The proposed model achieves comparable performance with the state-of-the-art approaches.

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Hanzi Xu, Muhao Chen, Lifu Huang, Slobodan Vucetic, Wenpeng Yin

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

Challenge:	Recent studies have focused on few-shot and zero-shot learning, but label occurrences vary widely . authors propose a new classification challenge that can be used to manage labels across the full frequency spectrum .
Approach:	They propose a new classification challenge that allows for label co-occurrences without predefined limits.
Outcome:	The proposed system can handle freq-shot, few-shot and zero-shot labels without limits.

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

A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle (2020.acl-main)

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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Shyh-Shiun Hung, Hen-Hsen Huang, Hsin-Hsi Chen

Planning and Editing What You Retrieve for Enhanced Tool Learning (2024.findings-naacl)

Challenge:	Existing work on hierarchical discourse parsing in English is based on the RST-style one.
Approach:	They propose a Chinese discourse parser that integrates pre-trained text encoder and employs novel training strategies to improve rhetorical relation recognition.
Outcome:	The proposed system achieves state-of-the-art performance in Chinese discourse parsing.

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Tenghao Huang, Dongwon Jung, Vaibhav Kumar, Mohammad Kachuee, Xiang Li, Puyang Xu, Muhao Chen

TextHide: Tackling Data Privacy in Language Understanding Tasks (2020.findings-emnlp)

Challenge:	Existing methods for integrating external tools with Large Language Models fall short on effectively shortlisting relevant tools.
Approach:	They propose a plan-and-retrieve and edit-and ground paradigms for LLMs that decompose complex queries into actionable tasks.
Outcome:	The proposed paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models.

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Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora

LED: A Dataset for Life Event Extraction from Dialogs (2023.findings-eacl)

Challenge:	Unsolved privacy challenges in distributed or federated learning are a challenge for many domains including Natural Language Processing.
Approach:	They propose a federated learning framework that adds an encryption step to prevent an eavesdropping attacker from recovering private text data.
Outcome:	The proposed model can effectively defend against attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9%.

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Yi-Pei Chen, An-Zi Yen, Hen-Hsen Huang, Hideki Nakayama, Hsin-Hsi Chen

Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)

Challenge:	Existing methods for extracting life events from conversations are limited.
Approach:	They propose a dataset containing fine-grained life event annotations on conversational data.
Outcome:	The proposed dataset combines three information extraction frameworks to extract life events from conversations.

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Hui Huang, Shuangzhi Wu, Kehai Chen, Hui Di, Muyun Yang, Tiejun Zhao

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

Challenge:	State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment.
Approach:	They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs.
Outcome:	The proposed method improves the estimation performance while mitigating the bias.

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

Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling (2024.findings-eacl)

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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Chao-Wei Huang, Chen-An Li, Tsu-Yuan Hsu, Chen-Yu Hsu, Yun-Nung Chen

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

Challenge:	Existing sparse retrieval methods often yield inferior performance in multilingual retrieval, requiring a large amount of paired data, which is costly.
Approach:	They propose an Unsupervised Multilingual dense Retriever trained without paired data which iteratively improves performance of multilingual retrievers.
Outcome:	The proposed framework outperforms supervised baselines on two benchmark datasets and shows that iterative training improves the performance.

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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

Learning Spoken Language Representations with Neural Lattice Language Modeling (2020.acl-main)

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.

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Chao-Wei Huang, Yun-Nung Chen

Online Distilling from Checkpoints for Neural Machine Translation (N19-1)

Challenge:	Existing methods for pretraining language models do not consider spoken language properties.
Approach:	They propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks.
Outcome:	The proposed framework outperforms baselines on spoken inputs on intent detection and dialogue act recognition datasets.

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Hao-Ran Wei, Shujian Huang, Ran Wang, Xin-yu Dai, Jiajun Chen

Retrieval-Augmented Generation with Hierarchical Knowledge (2025.findings-emnlp)

Challenge:	Existing neural machine translation models have a deep structure with large amounts of parameters, making them hard to train.
Approach:	They propose an online method to generate a teacher model from checkpoints . they show steady improvement over a strong self-attention-based baseline system .
Outcome:	The proposed method improves on-the-fly on several datasets and language pairs.

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Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng

Enhancing Society-Undermining Disinformation Detection through Fine-Grained Sentiment Analysis Pre-Finetuning (2024.findings-eacl)

Challenge:	Existing RAG methods do not utilize hierarchical knowledge in human cognition, which limits the capabilities of RAG systems.
Approach:	They propose a graph-based approach that utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems.
Outcome:	The proposed approach achieves significant performance improvements over the state-of-the-art methods.

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Tsung-Hsuan Pan, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Visually-Enhanced Phrase Understanding (2023.findings-acl)

Challenge:	a new method for disinformation detection is needed to address the issue of disinformation, authors argue . a series of rigorous experiments establishes a notable connection between disinformation and fine-grained sentiment labels .
Approach:	They propose a method leveraging pre-finetuning concept for efficient detection and removal of disinformation that may undermine society.
Outcome:	The proposed method improves performance across languages and languages, showing promising results.

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Tsu-Yuan Hsu, Chen-An Li, Chao-Wei Huang, Yun-Nung Chen

Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)

Challenge:	Large-scale vision-language pre-training models generate high-quality textual representations, which often outperform models that are purely text-based, such as BERT.
Approach:	They propose to utilize both textual and visual encoders of multi-modal pre-trained models to enhance language understanding tasks by generating an image associated with a textual prompt.
Outcome:	The proposed method outperforms models that are purely text-based on visual and textual understanding tasks and significantly improves the entity clustering task.

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Zhihong Zhu, Xuxin Cheng, Hao An, Zhichang Wang, Dongsheng Chen, Zhiqi Huang

Towards Robustness of Text-to-SQL Models against Synonym Substitution (2021.acl-long)

Challenge:	Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods.
Approach:	They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs.
Outcome:	The proposed framework can be built by directly prompting LLMs to understand user needs without training data.

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Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver, John R. Woodward, Jinxia Xie, Pengsheng Huang

Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)

Challenge:	Existing text-to-SQL models rely on lexical matching between words in NL questions and tokens in table schemas, which may break the schema linking mechanism.
Approach:	They propose a human-curated dataset for text-to-SQL translation . they replace schema-related words with manually selected synonyms .
Outcome:	The proposed model outperforms its counterparts without the defense.

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Yijiong Yu, Zhixiao Qi, Yongfeng Huang, Wei Wang, null Weifeng.liu, Ran Chen, Ji Pei

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities (2026.acl-long)

Challenge:	despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks .
Approach:	a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps .
Outcome:	The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts.

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Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng

AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025.coling-main)

Challenge:	Large language models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors pose significant risks.
Approach:	They propose a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality.
Outcome:	The proposed framework offers a principled and interpretable framework for safe and controllable LLM behavior serving as a foundation for future research.

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Zhihao Fan, Lai Wei, Jialong Tang, Wei Chen, Wang Siyuan, Zhongyu Wei, Fei Huang

Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)

Challenge:	Recent large language models (LLMs) have demonstrated superior performance in static medical question answering benchmarks, rivaling even human experts.
Approach:	They propose a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner to assess the performance of LLM-driven Doctor agents in simulated clinical scenarios.
Outcome:	The proposed framework emulates dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner.

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Rikui Huang, Wei Wei, Xiaoye Qu, Shengzhe Zhang, Dangyang Chen, Yu Cheng

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)

Challenge:	Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time.
Approach:	They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets.

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Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen

Contrastive Instruction Tuning (2024.findings-acl)

Challenge:	Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation.
Approach:	They propose to have large language models actively involved in retrieval to guide retrieval with generation.
Outcome:	The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations.

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Tianyi Yan, Fei Wang, James Y. Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, Muhao Chen

MCLF: A Multi-grained Contrastive Learning Framework for ASR-robust Spoken Language Understanding (2023.findings-emnlp)

Challenge:	Current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the same instruction is phrased with slightly varied forms or language styles.
Approach:	They propose a method which maximizes the similarity between the hidden representations of semantically equivalent instruction-instance pairs while minimizing the similarities between semantically different ones.
Outcome:	Experiments on the PromptBench benchmark show that Contrastive Instruction Tuning improves LLMs’ robustness to unseen instructions with variations across character, word, sentence, and semantic levels by +2.5% in accuracy.

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Zhiqi Huang, Dongsheng Chen, Zhihong Zhu, Xuxin Cheng

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

Challenge:	Trending ASR-robust SLU systems have seen impressive improvements through global contrastive learning, but they can easily lead to severe semantic changes.
Approach:	They propose a two-stage multi-grained contrastive learning framework to improve ASR robustness . they first adapt pre-trained language models to downstream SLU datasets and then fine-tune it on the corresponding dataset.
Outcome:	The proposed framework improves on four datasets and four BERT-like backbone models.

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations (2025.coling-main)

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

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Nuo Chen, Hongguang Li, Jianhui Chang, Juhua Huang, Baoyuan Wang, Jia Li

LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)

Challenge:	Existing retrieval-based methods for long-term conversations face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions.
Approach:	They propose a framework that eschews traditional retrieval modules and memory databases and adopts a “One-for-All” approach to manage memory generation, compression, and response generation.
Outcome:	The proposed framework produces more nuanced and human-like experiences than retrieval-based methods.

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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang

Challenge:	Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making.
Approach:	They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain.
Outcome:	The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.

Exclusion of Thought: Mitigating Cognitive Load in Large Language Models for Enhanced Reasoning in Multiple-Choice Tasks (2025.acl-long)

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Qihang Fu, Yongbin Qin, Ruizhang Huang, Yanping Chen, Yulin Zhou, Lintao Long

Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation (2026.findings-acl)

Challenge:	Multiple-choice questions (MCQs) are widely used and vital assessment format for evaluating large language models (LLMs).
Approach:	They propose a reasoning prompt strategy that redirects the model's attention away from erroneous options and eliminates incorrect options.
Outcome:	The proposed reasoning prompt reduces cognitive load by steering the model’s attention away from erroneous options, enabling the model to focus more effectively on reasonable answers.

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Chen Huang, Zitan Jiang, Zou Changyi, Wenqiang Lei, See-Kiong Ng

Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning (2023.findings-emnlp)

Challenge:	a new technology is transforming customer service chatbots into strategic bridges for business intelligence . a recent study shows that customer service bots are increasingly being used as reactive support tools .
Approach:	They propose a task of Proactive Information Probing which optimizes when to probe users for pre-specified information while minimizing conversation turns and user friction.
Outcome:	The proposed framework outperforms baselines in both information probing and service quality.

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Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, Yong Dou

Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection (P18-2)

Challenge:	Existing methods for lifelong TKG reasoning only address part of the challenges.
Approach:	They propose a temporal-path-based reinforcement learning framework for lifelong TKG reasoning . they add temporal displacement into the action space of RL to extrapolate for the future .
Outcome:	The proposed model outperforms existing models against well-adapted baselines on three lifelong TKG reasoning benchmarks.

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Hen-Hsen Huang, Chiao-Chen Chen, Hsin-Hsi Chen

CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering (2024.emnlp-main)

Challenge:	Existing methods to collect self-labeled data for irony detection are based on false-alarm hashtags.
Approach:	They propose a neural network-based model which disambiguates hashtag usages and prunes the self-labeled training data.
Outcome:	The proposed model outperforms the models trained on the less but cleaner training instances.

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Yike Wu, Yi Huang, Nan Hu, Yuncheng Hua, Guilin Qi, Jiaoyan Chen, Jeff Pan

Question Translation Training for Better Multilingual Reasoning (2024.findings-acl)

Challenge:	Existing knowledge rewriting methods may include irrelevant information, omit crucial details, or fail to align with the question’s semantics.
Approach:	They propose a new rewriting method CoTKR for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewrite.
Outcome:	The proposed method mitigates the limitations of single-step knowledge rewriting and bridges the preference gap between the knowledge reactor and the question answering (QA) model.

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Wenhao Zhu, Shujian Huang, Fei Yuan, Shuaijie She, Jiajun Chen, Alexandra Birch

Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention (2023.findings-emnlp)

Challenge:	Large language models have shown compelling performance on reasoning tasks but they tend to perform much worse in languages other than English.
Approach:	They propose to train a model to translate reasoning questions into English by fine tuning on X-English parallel question data.
Outcome:	The proposed approach improves on LLaMA2-13B on the MGSM and MSVAMP multilingual reasoning benchmarks.

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Changjiang Gao, Shujian Huang, Jixing Li, Jiajun Chen

Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)

Challenge:	Recent large language models (LLMs) have shown strong abilities to understand natural language, but how these factors affect the models’ language perception is unclear.
Approach:	They compare the self-attention of several existing large language models in different sizes to assess the effect of scaling and instruction tuning on language perception.
Outcome:	The proposed models are closer to non-native speakers than native speakers in attention, suggesting a sub-optimal language perception of all models.

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Yao Zhao, Jiacheng Huang, Wei Hu, Qijin Chen, XiaoXia Qiu, Chengfu Huo, Weijun Ren

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention (2026.acl-long)

Challenge:	Existing methods rely on textual similarities between NL and KG to build relation links.
Approach:	They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG.
Outcome:	The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets.

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Yifan Zhang, Chen Huang, Yueke Zhang, Jiahao Zhang, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

Challenge:	Code Language Models learn attention based on statistical input-output token correlations.
Approach:	They propose a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes.
Outcome:	The proposed model outperforms baselines in three languages, with gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization.

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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

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Zheng Ma, Shi Zong, Mianzhi Pan, Jianbing Zhang, Shujian Huang, Xinyu Dai, Jiajun Chen

METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)

Challenge:	In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.
Approach:	They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models.
Outcome:	The proposed method analyzes captions generated by five popular VLP models to reveal how well they align with visual words and how well these align with images.

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Pengfeng Li, Chen Huang, Chaoqun Hao, Hongyao Chen, Xiao-Yong Wei, Wenqiang Lei, See-Kiong Ng

ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)

Challenge:	Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy.
Approach:	They use a unified context to benchmark large language models' contextual causal reasoning skills.
Outcome:	The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality.

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Huadai Liu, Rongjie Huang, Xuan Lin, Wenqiang Xu, Maozong Zheng, Hong Chen, Jinzheng He, Zhou Zhao

Towards Unsupervised Language Understanding and Generation by Joint Dual Learning (2020.acl-main)

Challenge:	Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy .
Approach:	They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio.
Outcome:	The proposed model outperforms existing models regardless of visibility of the scene . it can generate high-perceived audio, opening up new avenues for AR and VR applications .

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Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen

ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

Challenge:	Existing work exploits dual property between understanding and generation to improve performance of modular dialogue systems.
Approach:	They propose a dual supervised learning framework that exploits the dual property between understanding and generation.
Outcome:	The proposed framework improves both NLU and NLG performance by incorporating supervised and unsupervised learning algorithms.

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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang

Can ChatGPT’s Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge (2024.acl-long)

Challenge:	Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance.
Approach:	They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 .
Outcome:	The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs.

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Cheng Yang, Puli Chen, Qingbao Huang

New Frontiers of Information Extraction (2022.naacl-tutorials)

Challenge:	Existing methods for supervised metaphor detection are limited by their performance.
Approach:	They propose to use ChatGPT to detect most prevalent verb metaphors among metaphors . they use literal collocations of target verbs and subject-object pairs of verbs to detect them .
Outcome:	The proposed method achieves the best performance on the unsupervised verb metaphors detection task compared to existing unsupervised methods or direct prediction using ChatGPT.

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Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth

Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)

Challenge:	Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data.
Approach:	This tutorial will provide an introduction to recent advances in IE by answering several important research questions.
Outcome:	The tutorial will address several important research questions and outline directions for further investigation.

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Zijin Hong, Zheng Yuan, Hao Chen, Qinggang Zhang, Feiran Huang, Xiao Huang

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

Challenge:	Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs.
Approach:	They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models.
Outcome:	The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models.

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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang

Compressing LLM Knowledge into Graph Representations for Text-attributed Graphs Learning (2026.acl-long)

Challenge:	Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks.
Approach:	They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions.
Outcome:	The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks.

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Runhuai Chen, Dian Shen, Dandan Zhang, Kaihong Huang, Linghui Meng, Beilun Wang

Adaptive Helpfulness–Harmlessness Alignment with Preference Vectors (2026.eacl-long)

Challenge:	Existing GNN-LLM approaches use large language models at inference time for processing text attributes, resulting in costly deployment.
Approach:	They propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning.
Outcome:	The proposed framework internalizes LLM knowledge within graph models and supports inference-efficient TAG learning.

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Ren-Wei Liang, Chin Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun

CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation (2021.findings-acl)

Challenge:	Existing approaches to balancing helpfulness and harmlessness suffer from performance conflicts, limited controllability, and poor extendability.
Approach:	They propose a framework that allows users to control their own preferences and dynamically merge them at test time.
Outcome:	The proposed framework improves helpfulness without conservatism and smooth control over preference trade-offs.

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Chujie Zheng, Yong Liu, Wei Chen, Yongcai Leng, Minlie Huang

HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)

Challenge:	Existing methods for empathetic response generation ignore hierarchical relationships between different factors, leading to a weak ability of empathy modeling.
Approach:	They propose a multi-factor hierarchical framework for empathetic response generation which models the above three key factors in a hierarchically structured way.
Outcome:	The proposed model generates more empathetic responses than previous methods.

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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao

Efficient Beam Search for Large Language Models Using Trie-Based Decoding (2025.emnlp-main)

Challenge:	Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences.
Approach:	They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning.
Outcome:	The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks.

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Brian J Chan, Mao-xun Huang, Jui-Hung Cheng, Chao-Ting Chen, Hen-Hsen Huang

Strategy-Induct: Task-Level Strategy Induction for Instruction Generation (2026.findings-acl)

Challenge:	Large language models (LLMs) face memorybound performance bottlenecks due to their high memory requirements.
Approach:	They propose a trie-based parallel decoding method that shares a single KV cache across beams with common prefixes to dramatically reduce memory usage and enables efficient decoding.
Outcome:	The proposed method significantly reduces memory usage and enables efficient decoding without compromising generation quality.

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Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen

RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)

Challenge:	Existing methods for task-level instruction generation rely on input-output pairs . obtaining labeled answers can be difficult or costly, limiting generalization across architectures.
Approach:	They propose a framework that derives task-level instructions solely from a small set of example questions without requiring labeled answers.
Outcome:	The proposed framework outperforms state-of-the-art methods in question-only settings.

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Zhongzhan Huang, Guoming Ling, Yupei Lin, Yandong Chen, Shanshan Zhong, Hefeng Wu, Liang Lin

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

Challenge:	a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers.
Approach:	They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations .
Outcome:	The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs.

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

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data (2026.findings-acl)

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

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Kai Xiong, Yanwei Huang, Rongjunchen Zhang, Kun Chen, Haipang WU, Yingcai Wu

NumHG: A Dataset for Number-Focused Headline Generation (2024.lrec-main)

Challenge:	Existing datasets with verifiable answers are limited in reliability, diversity, and scalability . a new approach to generate verifikatable data at scale is needed to improve models' performance .
Approach:	They propose a formal framework for synthesizing verifiable data at scale using a novel DSL-driven approach.
Outcome:	The proposed framework improves performance on a wide range of puzzles and logic benchmarks.

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Jian-Tao Huang, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning (2026.findings-acl)

Challenge:	a lack of fine-grained annotations for accurate numeral generation in headlines is a major roadblock . a new dataset, the NumHG, provides over 27,000 annotated numeral-rich news articles for detailed investigation .
Approach:	They propose a dataset that provides annotated numerals for headline generation . they evaluate five well-performing headline-generation models using human evaluation .
Outcome:	The proposed dataset provides annotated numeral-rich news articles for detailed investigation.

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Fanding Huang, Guanbo Huang, Xiao Fan, Yi He, Xiao Liang, Xiao Chen, Qinting Jiang, Faisal Nadeem Khan, Jingyan Jiang, Zhi Wang

GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)

Challenge:	Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have substantially improved the reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a method that balances exploration and exploitation in the hidden-state space of response trajectories.
Outcome:	The proposed model yields consistent improvements across models, algorithms and reasoning benchmarks.

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Yang Chen, Shuwan Yang, Yan Xiang, Ran Song, Yuxin Huang, Zhengtao Yu

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

Challenge:	Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities.
Approach:	They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one .
Outcome:	The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs.

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

EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation (2025.emnlp-main)

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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Sen Yang, Yu Bao, Yu Lu, Jiajun Chen, Shujian Huang, Shanbo Cheng

CANDY: Benchmarking LLMs’ Limitations and Assistive Potential in Chinese Misinformation Fact-Checking (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation.
Approach:	They propose a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities by extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy.
Outcome:	The proposed framework achieves significant improvement across 72 x2x directions while generalizing to enhance en2x performance.

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Ruiling Guo, Xinwei Yang, Chen Huang, Tong Zhang, Yong Hu

Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)

Challenge:	CANDY is a benchmark to evaluate the capabilities and limitations of large language models (LLMs) for fact-checking misinformation.
Approach:	a team of researchers develop a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in Chinese.
Outcome:	CANDY is a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in China.

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Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Wu, Zhan Chen

METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues (2026.acl-long)

Challenge:	The 20 Questions (Q20) game encourages deductive reasoning and creativity.
Approach:	They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward .
Outcome:	The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment.

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Haofu Yang, Jiaji Liu, Chen Huang, Faguo Wu, Wenqiang Lei, See-Kiong Ng

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)

Challenge:	Developing non-collaborative dialogue agents traditionally requires manual codification of expert strategies.
Approach:	They propose a method that formalizes expert knowledge into a Strategy Forest from raw transcripts.
Outcome:	The proposed method outperforms existing methods by 9%-10% in two benchmarks.

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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun

ICL: Iterative Continual Learning for Multi-domain Neural Machine Translation (2024.findings-emnlp)

Challenge:	Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT.
Approach:	They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness.
Outcome:	The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability.

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Zhibo Man, Kaiyu Huang, Yujie Zhang, Yuanmeng Chen, Yufeng Chen, Jinan Xu

Examining Gender Bias in Languages with Grammatical Gender (D19-1)

Challenge:	Existing studies have focused on learning domain knowledge from multiple domains, but task-specific parameters hinder mutual transfer of knowledge between new domains.
Approach:	They propose an iterative Continual learning framework for multi-domain neural machine translation that leverages previously acquired domain knowledge.
Outcome:	The proposed model outperforms baseline models on UM-Corpus and OPUS datasets.

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Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen, Ryan Cotterell, Kai-Wei Chang

Dynamic Graph Transformer for Implicit Tag Recognition (2021.eacl-main)

Challenge:	Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender .
Approach:	They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables .
Outcome:	The proposed methods reduce gender bias while preserving the original embeddings.

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Yi-Ting Liou, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)

Challenge:	Existing studies focus on using explicit information in articles and do not consider the implicit information.
Approach:	They propose a dynamic graph transformer that distills the textual information and the entity relations on the fly.
Outcome:	The proposed model can extract the textual information and the entity relations on the fly.

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Bo Ouyang, Wenbing Huang, Runfa Chen, Zhixing Tan, Yang Liu, Maosong Sun, Jihong Zhu

LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments (2024.acl-long)

Challenge:	Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training.
Approach:	They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation.
Outcome:	The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks.

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Junzhe Chen, Xuming Hu, Shuodi Liu, Shiyu Huang, Wei-Wei Tu, Zhaofeng He, Lijie Wen

Can Large Language Models Identify Authorship? (2024.findings-emnlp)

Challenge:	Existing benchmarks for evaluating large language models use static datasets, leading to data leakage or overlooking the complexities of multi-agent interactions.
Approach:	They propose a framework that evaluates the diverse capabilities of LLM agents in multi-agent dynamic environments.
Outcome:	The proposed framework assesses the diverse capabilities of LLM agents in multi-agent dynamic environments.

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Baixiang Huang, Canyu Chen, Kai Shu

Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving, but their potential in authorship analysis remains under-explored.
Approach:	They propose to integrate explicit linguistic features into LLMs to provide explanations into their reasoning processes.
Outcome:	The proposed models demonstrate their ability to perform zero-shot, end-to-end authorship verification effectively and provide explainability through explicit linguistic features.

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Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)

Challenge:	Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL).
Approach:	They propose a solution that takes multiple compositions as inputs and constrains disentangled primitive features to be general across compositions.
Outcome:	The proposed architecture significantly improves performance on three popular CZSL benchmarks and has been verified by solid ablation studies.

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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao

Training Medical QA Models Based on Mixed Rewards from Multiple-Choice and Open-Ended Questions (2025.findings-emnlp)

Challenge:	Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis.
Approach:	They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge.
Outcome:	The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks.

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Yue Qiu, Yujan Ting, Pei Dong, Terrence Chen, Weijing Huang

Question Answering

Challenge:	Reinforcement learning (RL) for large language models typically requires clear reward signals, which are often unavailable for open-ended (OE) questions where answer evaluation is ambiguous without scalable expert labeling.
Approach:	They propose a mixed-data approach to training large language models with varying reward clarity . they combine Multiple-choice questions (MCQs) with OE questions for which they use simpler, potentially noisy rewards such as Jaccard similarity or LLM-based evaluators.
Outcome:	The mixed-data approach improves medical question-answering performance across model scales.

Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (2021.emnlp-main)

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Ran Wang, Xi’ao Su, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Manifold Adversarial Augmentation for Neural Machine Translation (2021.findings-acl)

Challenge:	Large-scale multi-label text classification tasks often face long-tailed label distributions, where many labels have few or even no training instances.
Approach:	They propose a meta-learning approach that incorporates the objective of adapting to new low-resource tasks into the meta-Learning phase.
Outcome:	The proposed approach achieves state-of-the-art against strong baselines and can still enhance powerful BERTlike models.

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Guandan Chen, Kai Fan, Kaibo Zhang, Boxing Chen, Zhongqiang Huang

Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)

Challenge:	Recent studies show that NMT models can drop significantly when small perturbations are added to input sentences.
Approach:	They propose a data augmentation approach to sample sentences from the vicinity distributions in higher-level representations.
Outcome:	The proposed method improves translation accuracy on training samples from higher-level representations.

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Zixiang Xu, Yanbo Wang, Yue Huang, Xiuying Chen, Jieyu Zhao, Meng Jiang, Xiangliang Zhang

Merely Judging Metaphor is Not Enough: Research on Reasonable Metaphor Detection (2024.findings-emnlp)

Challenge:	Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge.
Approach:	They propose a method to identify cross-lingual weaknesses in Large Language Models . they construct bilingual question pairs that expose performance discrepancies between English and target languages .
Outcome:	The proposed method uncovers over 50% accuracy drops in target languages across models.

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Puli Chen, Cheng Yang, Qingbao Huang

Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)

Challenge:	Current metaphor detection tasks only provide labels without interpreting how to understand them.
Approach:	They propose to improve the current metaphor detection task by using mainstream Large Language Models.
Outcome:	The proposed model is based on the original sentence, target word, and usage . the model is then evaluated using manual evaluation .

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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang

Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)

Challenge:	Recent studies show that Large language models struggle with handling long token sequences due to limited training context size.
Approach:	They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities.
Outcome:	The proposed method outperforms existing methods on 4 language modeling benchmarks.

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Kun Liu, Yao Fu, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang, Sheng Gao

Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations (2023.emnlp-main)

Challenge:	Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises.
Approach:	They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions.
Outcome:	The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance.

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James Y. Huang, Wenlin Yao, Kaiqiang Song, Hongming Zhang, Muhao Chen, Dong Yu

No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)

Challenge:	Existing approaches to learn sentence embeddings do not capture the semantic similarity of sentences.
Approach:	They propose a framework that integrates compositional sentence operations into the embedding space and optimizes operator networks and a bottleneck encoder-decoder model to produce meaningful and interpretable sentence embeddables.
Outcome:	The proposed framework improves the interpretability of sentence embeddings on four textual generation tasks while maintaining strong performance on traditional semantic similarity tasks.

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Wei-Chi Wu, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen

Small Agents, Big Gains: Journey-Aware and Critic-Guided Simulation for Long-Horizon Shopping Dialogues (2026.acl-industry)

Challenge:	Existing studies show that translation-based prompting is not universally optimal for multilingual LLMs.
Approach:	They evaluate translation-based prompting across ten languages and four benchmarks . they propose a lightweight classifier that predicts whether native or translation- based prompts are optimal .
Outcome:	The proposed classifiers achieve statistically significant improvements over fixed prompting strategies across ten languages and four benchmarks.

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Qing Ping, Changyou Chen, Binxuan Huang

LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks (2025.coling-main)

Challenge:	e-commerce assistants must support inspiration, comparison, and tool-grounded fact-checking . lack of data-coverage and verification problem hampers efficient, deployable models . eaa: "training trajectories must cover diverse user workflows with high fidelity"
Approach:	They propose a system that synthesizes diverse, faithful, and policy-aligned shopping trajectories . a small model can significantly outperform same-size baselines and surpass a large-model baseline .
Outcome:	The proposed model outperforms existing models and surpasses large models with 8 higher inference throughput.

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Xiang Li, Chen Shi, Yong Xu, Jun Huang

Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language (C18-1)

Challenge:	Existing methods emphasize contextual semantics while others pay more attention to explicit logical features. Existing models utilize graph convolutional networks (GCN) for node updates, still exhibiting some shortcomings.
Approach:	They propose a logical reasoning method with contrastive learning and lightweight graph networks (LogiGraph) they employ conjunction and punctuation marks as two types of edges to construct a dual graph.
Outcome:	The proposed method improves the GCN and employs conjunction and punctuation marks as two types of edges to construct a dual graph.

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Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen

Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization (2024.findings-emnlp)

Challenge:	a word usage error is the most common error type in Chinese, according to the HSK dynamic composition corpus . a system that considers both target erroneous token and context can generate a correction vector .
Approach:	They propose a neural network model that considers target erroneous token and context to generate a correction vector and compare it against a candidate vocabulary to propose suitable corrections.
Outcome:	The proposed model can detect 91% of the cases and propose suitable corrections within a list of five candidates.

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Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, Wei-Lin Chen, Chao-Wei Huang, Yu Meng, Yun-Nung Chen

Parameter-Efficient Tuning with Special Token Adaptation (2023.eacl-main)

Challenge:	Existing literature on leveraging persona in large language models is disorganized and lacks a systematic taxonomy . leveraging peopleas has resurfaced as an ideal lens for adapting LLMs for specific contexts .
Approach:	They propose to categorize current research on leveraging persona in large language models . they propose to use a comprehensive survey to categorize existing studies .
Outcome:	The proposed framework is a promising framework for tailoring large language models to specific contexts.

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Xiaocong Yang, James Y. Huang, Wenxuan Zhou, Muhao Chen

Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)

Challenge:	a recent study shows that parameter-efficient tuning is a challenge for multitask deployments.
Approach:	They propose a parameter-efficient tuning technique that only updates a small subset of parameters when adapting a pretrained model to downstream tasks.
Outcome:	The proposed method achieves comparable performance to fine-tuning in natural language understanding tasks including text classification and NER with only 0.029% of parameters trained.

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Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang

Challenge:	Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents.
Approach:	They propose a cognitive framework that incorporates first-order and second-order perspective transitions into LLMs to enhance their ability to identify and counteract deceptive information.
Outcome:	The proposed framework enhances LLMs’ ability to identify and counteract deceptive information without extra fine-tuning and data.

Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (2021.naacl-main)

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Yubo Chen, Yunqi Zhang, Changran Hu, Yongfeng Huang

Knowledge Representation

Challenge:	Existing methods for relational triple extraction ignore implicit triples that lack explicit expressions, leading to incomplete knowledge graphs.
Approach:	They propose a binary pointer network to extract explicit and implicit relational triples from sentences and to retain the information of extracted triples in an external memory.
Outcome:	The proposed framework extracts overlapping triples relevant to each word sequentially and retains the information of extracted triples in an external memory.

Extractive Summarization as Text Matching (2020.acl-main)

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Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, Xuanjing Huang

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

Challenge:	Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences.
Approach:	They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space.
Outcome:	The proposed framework is faster and more efficient than existing frameworks.

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

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning (2025.acl-long)

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

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Chenxi Huang, Shaotian Yan, Liang Xie, Binbin Lin, Sinan Fan, Yue Xin, Deng Cai, Chen Shen, Jieping Ye

LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? (2026.acl-long)

Challenge:	Representation Fine-tuning (ReFT) is a proposed method for improving parameter efficiency . however, it yields suboptimal performance, as fixed-position representations have uncertain impact on outputs .
Approach:	They propose a method that fine-tunes critical representations in a low-rank linear subspace while freezing the base model.
Outcome:	The proposed method improves accuracy of LLaMA-2-7B and ReFT by 18.2 and 3.8 on GSM8K.

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Jingyuan Wang, Yankai Chen, Zhonghang Li, Chao Huang

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable progress in reasoning, but are resource-intensive and require large curated datasets.
Approach:	They propose a framework that leverages the behavioral divergence between a stronger expert model and a weaker amateur model.
Outcome:	The proposed framework improves accuracy by up to 28.1% while reducing time consumption by 90% and tuning token usage by 99%.

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang

Empirical Analysis of Decoding Biases in Masked Diffusion Models (2026.acl-long)

Challenge:	Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions.
Approach:	They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency .
Outcome:	The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo .

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Pengcheng Huang, Tianming Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Tong Xiao, Zulong Chen, Maosong Sun

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)

Challenge:	Existing MDMs employ uncertainty-based decoding strategies that limit their reasoning ability and ultimately degrade generation quality.
Approach:	They propose a framework that regularizes uncertainty-based decoding by incorporating two complementary priors to shape global decoding trajectories and promote content informativeness.
Outcome:	The proposed framework outperforms existing decoding strategies by more than 7% while achieving comparable performance to autoregressive models of similar parameter scales.

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Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)

Challenge:	Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well.
Approach:	They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet.
Outcome:	The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future.

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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin

TempParaphraser: “Heating Up” Text to Evade AI-Text Detection through Paraphrasing (2025.emnlp-main)

Challenge:	Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain .
Approach:	They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora.
Outcome:	The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions.

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Junjie Huang, Ruiquan Zhang, Jinsong Su, Yidong Chen

KVFKT: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration (2025.coling-main)

Challenge:	Existing detectors that perform well on benchmark datasets have weaknesses that can be exploited to manipulate AI-text.
Approach:	They propose a framework that simulates high-temperature sampling effects through multiple normal-temperaturing generations, effectively evading detection.
Outcome:	The proposed framework reduces detector accuracy by an average of 82.5% while preserving high text quality.

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Quanlong Guan, Xiuliang Duan, Kaiquan Bian, Guanliang Chen, Jianbo Huang, Zhiguo Gong, Liangda Fang

Supervised Optimism Correction: Be Confident When LLMs Are Sure (2025.findings-acl)

Challenge:	Existing knowledge tracing models do not incorporate forgetting features to improve the learning and answering processes.
Approach:	They propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration using four real-world datasets to test the model.
Outcome:	The proposed model outperforms the existing knowledge tracing models and eliminates the need for artificial engineering features.

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Junjie Zhang, Rushuai Yang, Shunyu Liu, Ting-En Lin, Fei Huang, Yi Chen, Yongbin Li, Dacheng Tao

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

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable success across diverse tasks such as instruction following, code generation, and medical diagnosis.
Approach:	They propose a supervised fine-tuning-based auxiliary loss for Q-value estimations during supervised refinement.
Outcome:	The proposed method outperforms beam search on GSM8K, MATH, and GAOKAO on reasoning benchmarks.

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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

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

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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Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Attending via both Fine-tuning and Compressing (2021.findings-acl)

Challenge:	Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit.
Approach:	They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally.
Outcome:	The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets.

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Jie Zhou, Yuanbin Wu, Qin Chen, Xuanjing Huang, Liang He

Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization (2025.findings-emnlp)

Challenge:	Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable.
Approach:	They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation .
Outcome:	The proposed framework improves performance and interpretability on eight benchmark datasets.

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Shuyang Hao, Yiwei Wang, Bryan Hooi, Jun Liu, Muhao Chen, Zi Huang, Yujun Cai

ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming (2025.acl-long)

Challenge:	HKVE selectively accepts gradient optimization results based on the distribution of attention scores across different layers, ensuring that every optimization step positively contributes to the attack.
Approach:	They propose a framework that selectively accepts gradient optimization results based on the distribution of attention scores across different layers and selectively takes them into account when calculating the attack success rate.
Outcome:	The proposed framework outperforms existing methods by achieving success rates of 75.08% on MiniGPT4, 85.84% on LLaVA and 81.00% on Qwen-VL.

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Xinwei Yang, Zhaofeng Liu, Chen Huang, Jiashuai Zhang, Tong Zhang, Yifan Zhang, Wenqiang Lei

TRANS-ZERO: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data (2025.findings-acl)

Challenge:	Existing studies on human-LLM competitive programming use scattered, application-specific human feedback.
Approach:	They propose a taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation.
Outcome:	The proposed benchmark pinpoints strengths and weaknesses of existing methods and will be openly released.

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Wei Zou, Sen Yang, Yu Bao, Shujian Huang, Jiajun Chen, Shanbo Cheng

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

Challenge:	Large Language Models (LLMs) have reshaped machine translation, but multilingual MT still relies heavily on parallel data for supervised fine-tuning.
Approach:	They propose a framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs).
Outcome:	The proposed framework matches models trained on large-scale parallel data and excels in non-English translation directions.

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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

DMIN: A Discourse-specific Multi-granularity Integration Network for Conversational Aspect-based Sentiment Quadruple Analysis (2024.findings-acl)

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.

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Peijie Huang, Xisheng Xiao, Yuhong Xu, Jiawei Chen

Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)

Challenge:	Existing studies focus on enhancing token-level interactions, but lack sufficient modeling of discourse structure information.
Approach:	They propose to use a discourse structure called "thread" to enhance token interaction among different utterances.
Outcome:	The proposed model achieves state-of-the-art on two datasets.

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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality.
Approach:	They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward.
Outcome:	The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability.

R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)

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Kaijie Chen, Zihao Lin, Zhiyang Xu, Ying Shen, Yuguang Yao, Joy Rimchala, Jiaxin Zhang, Lifu Huang

LLM as a metric critic for low resource relation identification (2024.findings-emnlp)

Challenge:	Reasoning is a fundamental capability underpinning text-to-image (T2I) generation.
Approach:	They propose a benchmark to rigorously assess reasoning-driven T2I generation.
Outcome:	Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation .

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Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng, Chao Deng

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)

Challenge:	Existing studies show that small language models (SLMs) overfit in low resource situations . however, the gap between pre-training and fine-tuning leads to performance decay .
Approach:	They propose to combine large language models and LLM for relation identification by co-evolution . they propose to use a masked language model prompt to generate a relation identification task .
Outcome:	The proposed model can handle low resource relation identification tasks with minimal overfitting . the proposed model provides essential background knowledge to assist training process .

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Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)

Challenge:	Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage.
Approach:	They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE.
Outcome:	The proposed framework can be used to infer whether a specific relation exists in a private client.

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Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li

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

Challenge:	Recent advances in text generation have limited applications due to multimodality problem.
Approach:	They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem.
Outcome:	The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

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

Affective and Dynamic Beam Search for Story Generation (2023.findings-emnlp)

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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Tenghao Huang, Ehsan Qasemi, Bangzheng Li, He Wang, Faeze Brahman, Muhao Chen, Snigdha Chaturvedi

When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation (2021.acl-short)

Challenge:	AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking.
Approach:	They propose to use dynamic beam sizing and affective reranking to generate interesting stories using two novel techniques.
Outcome:	The proposed method outperforms baseline models in generating affectively charged and interesting narratives.

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Jiahuan Li, Yutong Shen, Shujian Huang, Xinyu Dai, Jiajun Chen

FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)

Challenge:	Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules.
Approach:	They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena .
Outcome:	The character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains.

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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao

Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs (2020.acl-main)

Challenge:	Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge .
Approach:	They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance.
Outcome:	The proposed model improves performance on e-commerce image-text retrieval task by a large margin.

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Jun Chen, Xiaoya Dai, Quan Yuan, Chao Lu, Haifeng Huang

SemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment (2026.findings-acl)

Challenge:	a novel framework for text-based diagnosis of diseases requires appropriate balance between accuracy and interpretability.
Approach:	They propose a framework that stacks Bayesian Network Ensembles on top of CNN to build an accurate yet interpretable diagnosis system.
Outcome:	The proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable.

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Ziyang Chen, Zhenxuan Huang, Yile Wang, Weiqin Wang, Lu Yin, Hui Huang

A Unified RvNN Framework for End-to-End Chinese Discourse Parsing (C18-2)

Challenge:	Existing sentence embedding methods rely on fixed prompt templates or involve modifications to the model architecture, compromising its generative capabilities.
Approach:	They propose a sentence-level direct preference optimization approach that boosts the sentence representations while preserving the generative ability of LLMs.
Outcome:	The proposed method improves representations of semantically meaningful vectors without sacrificing generation capability.

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Lin Chuan-An, Hen-Hsen Huang, Zi-Yuan Chen, Hsin-Hsi Chen

Syntax Matters: Towards Spoken Language Understanding via Syntax-Aware Attention (2023.findings-emnlp)

Challenge:	Existing work on Chinese discourse parser relies on external packages to extract linguistic features from free text.
Approach:	They propose an end-to-end Chinese discourse parser based on recursive neural network to jointly model the subtasks including elementary discourse unit segmentation, tree structure construction, center labeling, and sense labeling.
Outcome:	The proposed framework achieves state-of-the-art in the Chinese Discourse Treebank dataset.

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Yifeng Xie, Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen

EchoMLLM: Incentivizing Echocardiographic Video Understanding with Keyframe Grounding and Report Generation (2026.findings-acl)

Challenge:	Existing studies on SLU systems have focused on integrating syntactic information into language models.
Approach:	They propose a model where attention scopes are constrained based on syntactic relationships.
Outcome:	The proposed model improves on three datasets and can be integrated into other language models to further boost their performance.

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Heyu Huang, Wanran Sun, Chi Chen, Bo Chen, Zonghao Guo, Yuhua Li, Ruixuan Li, Kunlun He, Maosong Sun

IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing (2024.emnlp-industry)

Challenge:	Echocardiography analysis requires a dual capability: rigorous quantitative keyframe localization and comprehensive qualitative synthesis.
Approach:	They propose a unified framework designed for real-world echocardiography video understanding.
Outcome:	a new framework is designed to support real-world echocardiography video understanding . it reduces temporal grounding errors by up to 76% and improves report generation quality by 65% .

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Kang Chen, Qing Zhang, Chengbao Lian, Yixin Ji, Xuwei Liu, Shuguang Han, Guoqiang Wu, Fei Huang, Jufeng Chen

DivScore: Zero-Shot Detection of LLM-Generated Text in Specialized Domains (2025.emnlp-main)

Challenge:	Unlike professional Business-to-Consumer (B2C) e-commerce platforms, consumer-to consumer (C2C), is mainly targeting individual sellers.
Approach:	They develop an intelligent product listing tool that generates product descriptions using various product attributes such as category, brand, color, condition, etc.
Outcome:	The proposed tool outperforms the base model in domain-specific tasks while producing less hallucination.

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Zhihui Chen, Kai He, Yucheng Huang, Yunxiao Zhu, Mengling Feng

Induct-Learn: Short Phrase Prompting with Instruction Induction (2024.emnlp-main)

Challenge:	Existing zero-shot detectors fail when applied to specialized content due to domain shift . DivScore outperforms state-of-the-art detectors in specialized domains .
Approach:	They propose a zero-shot detection framework that uses normalized entropy-based scoring and domain knowledge distillation to identify LLM-generated text in specialized domains.
Outcome:	The proposed framework outperforms state-of-the-art detectors on medical and legal datasets with 14.4% higher AUROC and 64.0% higher recall.

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Po-Chun Chen, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)

Challenge:	Existing methods for generating instructions from demonstrations rely on large datasets or numerous examples, which is impractical and costly in real-world scenarios.
Approach:	They propose a task-level framework that induces pseudo instructions from a few demonstrations and a short phrase, adding a CoT process into existing demonstrations.
Outcome:	The proposed framework outperforms state-of-the-art methods on two datasets and exhibits cross-model adaptability and lower cost.

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Hieu Tran, Junda Wang, Yujan Ting, Hong Yu, Weijing Huang, Terrence Chen

Ethical NLP

Challenge:	Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare.
Approach:	They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components .
Outcome:	Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores.

Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)

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Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang

Robust Natural Language Understanding with Residual Attention Debiasing (2023.findings-acl)

Challenge:	Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes.
Approach:	They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion.
Outcome:	The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes.

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Fei Wang, James Y. Huang, Tianyi Yan, Wenxuan Zhou, Muhao Chen

MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)

Challenge:	Existing ensemble-based debiasing methods do not address unintended dataset biases . attention plays a crucial role in providing robust prediction in NLU models .
Approach:	They propose an end-to-end debiasing method that mitigates unintended biases from attention.
Outcome:	The proposed method improves the OOD performance of BERT-based models on three benchmarks.

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Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang

Towards Effective Automatic Debt Collection with Persona Awareness (2023.emnlp-industry)

Challenge:	Existing recommender systems rely on semantic user and item memories to make predictions, but these memories are kept in isolation.
Approach:	They propose a framework that architecturally decouples memory management from reasoning to decouple memory management and reasoning from the user and item memories.
Outcome:	The proposed framework decouples memory management from reasoning and achieves state-of-the-art performance on four benchmarks.

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Tong Zhang, Junhong Liu, Chen Huang, Jia Liu, Hongru Liang, Zujie Wen, Wenqiang Lei

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

Challenge:	Existing debt collection agents fail to tailor strategies to debtor personas, leading to ineffective collection.
Approach:	They present a commercial practice on debt collection agents that organizes debtor personas into a taxonomy and constructs a persona-aware conversation dataset.
Outcome:	The proposed agent increases recovery rate by 3.31% and collects additional 100K RMB after two months of testing.

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

BioGraphia: A LLM-Assisted Biological Pathway Graph Annotation Platform (2025.emnlp-demos)

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

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Xi Xu, Sumin Jo, Adam Officer, Angela Chen, Yufei Huang, Lei Li

Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality (2024.findings-acl)

Challenge:	Existing methods for obtaining pathway information from biomedical literature rely on simplifying assumptions that limit their ability to capture true complexity of biological reactions.
Approach:	They propose a web-based platform to facilitate collaborative pathway graph annotation.
Outcome:	The platform supports multi-user collaboration with real-time monitoring, curation, and interactive pathway graph visualization.

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Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Jiang Yiming, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo Cesar

DAGS: A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition (2025.findings-acl)

Challenge:	a fine-grained, comprehensive understanding of multimodal environments remains under-explored.
Approach:	They propose an automated workflow for integrating AI agents into extended reality (XR) they propose a cerebral language agent that integrates LLM with memory, planning, and interaction with XR tools and a vision-language agent .
Outcome:	The proposed workflow integrates AI agents seamlessly into extended reality (XR) applications for fine-grained training.

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Puli Chen, Cheng Yang, Xingmao Zhang, Qingbao Huang

SportQA: A Benchmark for Sports Understanding in Large Language Models (2024.naacl-long)

Challenge:	Existing methods for metaphor recognition ignore interference caused by literal annotations . et al., 2018: Metaphor recognition plays an important role in cognition and communication .
Approach:	They propose a dependency-based Dual-Attention and Global Semantic Improvement framework to improve metaphor recognition.
Outcome:	The proposed framework can extract features from multiple information sources while improving on mainstream metaphor datasets.

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Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-fang Wang, Weining Shen

Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition (2022.naacl-main)

Challenge:	SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) sports knowledge is characterized by its fast pace, variety of types, abundance of strategies, and rich player narratives .
Approach:	They propose a benchmark specifically designed for evaluating Large Language Models in the context of sports understanding.
Outcome:	The proposed benchmark aims to bridge the gap between existing and specialized benchmarks in sports understanding.

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Pei Chen, Haotian Xu, Cheng Zhang, Ruihong Huang

Icon2: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation (2025.emnlp-main)

Challenge:	Named Entity Recognition (NER) datasets annotate coarse-grained entities such as a continent, a country, or a city.
Approach:	They propose a dataset HarveyNER with fine-grained locations annotated in tweets that characterizes many complex and long location mentions in informal descriptions.
Outcome:	The proposed dataset outperforms existing systems on hard cases and improves on the heuristic curricula.

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Qiyuan Chen, Hongsen Huang, Qian Shao, Jiahe Chen, Jintai Chen, Hongxia Xu, Renjie Hua, Ren Chuan, Jian Wu

Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions (2026.acl-long)

Challenge:	Large Language Models (LLMs) require high quality preference datasets to align with human preferences.
Approach:	They propose a framework that leverages inherent regulation of LLMs’ representation space for efficient and tailored preference dataset construction, named Icon2.
Outcome:	The proposed framework improves performance on benchmarks like AlpacaEval 2.0 and Arena-Hard while reducing computational costs by up to 48.1%.

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Guo Gan, Yuxuan Ding, Cong Chen, Yuwei Ren, Yin Huang, Hong Zhou

MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation (2024.naacl-long)

Challenge:	Existing reinforcement learning methods are expensive due to high latency and sample inefficiency . Currently, RL is limited to one-to-one state-action pairs .
Approach:	They propose a framework that shifts the training paradigm to Single State Multiple Actions and introduce a group-wise advantage estimator based on the averaged critic outputs.
Outcome:	The proposed framework achieves 7.5% and 8.3% success rate improvements on AndroidLab and AndroidWorld over UI-TARS-1.5-7B and attains 1.4x higher training efficiency than existing methods.

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Jiahuan Li, Shanbo Cheng, Shujian Huang, Jiajun Chen

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have shown their strong ability in the field of machine translation, yet they suffer from high computational cost and latency.
Approach:	They propose a framework which transfers knowledge from LLMs to existing MT models in a selective, comprehensive and proactive manner.
Outcome:	The proposed framework transfers knowledge from LLMs to existing MT models in a selective, comprehensive and proactive manner.

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Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang

Challenge:	Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach:	They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome:	The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .

FinQA: A Dataset of Numerical Reasoning over Financial Data (2021.emnlp-main)

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Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, William Yang Wang

Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model (2024.findings-emnlp)

Challenge:	Popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge.
Approach:	They propose a large-scale dataset with Question-Answering pairs over financial reports written by financial experts to facilitate analytical progress.
Outcome:	The proposed dataset is the first of its kind and is available on github.

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Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Ido Dagan

RTCFake: Speech Deepfake Detection in Real-Time Communication (2026.findings-acl)

Challenge:	Existing methods to obtain high-quality annotations under limited budgets focus on selecting informative data for expert annotations while the rest of the data is assigned to model annotation.
Approach:	They propose a semi-automatic annotation framework that uses error-aware triage and bi-weighting mechanisms to obtain high-quality annotations under limited budget.
Outcome:	The proposed framework outperforms baselines in the data annotation problem under limited budgets.

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Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren, Yujie Chen, Cunhang Fan, Zicheng Su, Yongcheng Zhang, Bo Cai

Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)

Challenge:	Existing detection studies focus on offline simulations and struggle to cope with complex distortions introduced during RTC transmission.
Approach:	They propose a large-scale speech deepfake dataset tailored for RTC scenarios . the dataset is constructed by transmitting speech through multiple social media and conferencing platforms .
Outcome:	The proposed dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms, enabling precise pairing between offline and online speech.

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Huachi Zhou, Shuang Zhou, Hao Chen, Ninghao Liu, Fan Yang, Xiao Huang

Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation (2026.acl-long)

Challenge:	Existing models learn user and item embeddings and generate reasons based on these embedds.
Approach:	They propose a concept-based explanation framework that leverages macro concepts to bridge the gap between the user/item embeddings and the recommendation reasons.
Outcome:	Extensive experiments on three datasets prove the proposed model is superior to existing models.

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Fangda Ye, Kuicai Dong, Xie Zhifei, Yuxin Hu, Yihang Yin, Shurui Huang, Shikai Dong, Chen Zhang, Jianzhu Bao, Shuicheng Yan

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Recent agentic search frameworks are text-centric, overlooking multimodal evidence . a pressing task is multimodal long-form generation, a new paper argues .
Approach:	They propose a unified agentic framework for grounded multimodal long-form generation.
Outcome:	The proposed framework is based on a unified agentic framework for grounded multimodal long-form generation.

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling (N19-1)

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

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Zhifang Fan, Zhen Wu, Xin-Yu Dai, Shujian Huang, Jiajun Chen

Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)

Challenge:	Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA).
Approach:	They propose a new subtask for Aspect Based Sentiment Analysis to extract opinion words as pairs from a given opinion target.
Outcome:	The proposed model outperforms existing methods significantly on several popular ABSA benchmarks.

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Chuang Zhou, Zhu Wang, Shengyuan Chen, Jiahe Du, Qiyuan Zheng, Zhaozhuo Xu, Xiao Huang

MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)

Challenge:	Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information.
Approach:	They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a .
Outcome:	The proposed framework outperforms state-of-the-art learning methods while requiring less resources.

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Xuxin Cheng, Zhihong Zhu, Xianwei Zhuang, Zhanpeng Chen, Zhiqi Huang, Yuexian Zou

Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)

Challenge:	Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems.
Approach:	They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts .
Outcome:	The proposed framework achieves state-of-the-art on three benchmark SLU datasets.

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Zejun Li, Zhihao Fan, Jingjing Chen, Qi Zhang, Xuanjing Huang, Zhongyu Wei

SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction (2025.emnlp-main)

Challenge:	Existing studies address the problem of translating English data into other languages, but they are limited in form and scale.
Approach:	They propose a framework to unify cross-lingual and cross-modal pre-training by using English data.
Outcome:	The proposed framework unifies cross-lingual and cross-modal pre-training on different data.

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Runfei Chen, Shuyang Jiang, Wei Huang

Challenge:	Existing spatiotemporal models struggle to interpret and adapt to abrupt changes caused by external events.
Approach:	They propose a LLM-powered semantic synthesis pipeline that extracts spatiotemporally related text from online texts and integrates it with spatio-temporal data.
Outcome:	The proposed framework achieves maximal reductions of 13.92% in MAE and 11.12% in RMSE compared to the spatiotemporal model.

AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)

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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun

Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach (2026.acl-long)

Challenge:	AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery.
Approach:	They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows.
Outcome:	The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages.

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Dongqi Huang, Tong Zhou, Zhuoran Jin, Shenghui Shi, null Maoyujiao, Kang Liu, Jun Zhao, Yubo Chen

Understanding LLMs’ Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From (2025.emnlp-main)

Challenge:	Large language models (LLMs) have great potential to facilitate explainable diagnosis, but their effectiveness is often constrained by insufficient diagnostic expertise.
Approach:	They propose a unified LLM-based framework for faithful and explainable diagnosis that builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm.
Outcome:	The proposed framework outperforms baselines on the DiReCT and JAMA benchmarks and improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods.

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Changjiang Gao, Hankun Lin, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Jiajun Chen, Shujian Huang

Cross-Lingual Transfer

Challenge:	Cross-lingual context retrieval is a fundamental aspect of cross-lingual alignment, but the performance and mechanism of it for large language models (LLMs) remains unclear.
Approach:	They evaluate cross-lingual context retrieval of over 40 large language models . they use cross-linguistic machine reading comprehension as a representative scenario .
Outcome:	The results show that open LLMs show strong cross-lingual context retrieval ability . the results also show that their oracle performances improve after training .

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm (2022.acl-long)

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Shaoyi Huang, Dongkuan Xu, Ian Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding

MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026.acl-long)

Challenge:	Conventional wisdom in pruning Transformer-based language models is that it reduces model expressiveness, but new research shows pruning increases risk of overfitting when performed at the fine-tuning phase.
Approach:	They propose to reduce pruning risk under pretrain-and-finetune paradigm . they propose to use knowledge distillation to improve pruning performance .
Outcome:	The proposed method outperforms the leading competitors on the GLUE benchmark.

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WenHao Wang, Peizhi Niu, Zhao Xu, Zhaoyu Chen, Jian Du, Yaxin Du, Xianghe Pang, Keduan Huang, Yanfeng Wang, Qiang Yan, Siheng Chen

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

Challenge:	Existing research on Large Language Models (LLMs) relies on few servers and lacks training support.
Approach:	They propose a web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training that collects and filters data from 1166 servers and 11536 tools.
Outcome:	Empirical evidence shows that MCP-Flow generates higher quality instruction-function call pairs and higher agentic task performance than previous work.

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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

Location-Aware Visual Question Generation with Lightweight Models (2023.emnlp-main)

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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Nicholas Suwono, Justin Chen, Tun Hung, Ting-Hao Huang, I-Bin Liao, Yung-Hui Li, Lun-Wei Ku, Shao-Hua Sun

Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models (2025.findings-acl)

Challenge:	a novel task aims to generate engaging questions from location-aware information . a lightweight model can be used to generate such questions .
Approach:	They propose a task to generate engaging questions from location-aware data . they represent location-based information with surrounding images and a GPS coordinate .
Outcome:	The proposed method outperforms baselines regarding human evaluation and evaluation metrics.

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You Li, Heyu Huang, Chi Chen, Kaiyu Huang, Chao Huang, Zonghao Guo, Zhiyuan Liu, Jinan Xu, Yuhua Li, Ruixuan Li, Maosong Sun

Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach (2023.acl-long)

Challenge:	Existing MLLMs still struggle to achieve precise grounding in multi-image scenarios.
Approach:	They propose a Chain-of-Thought framework that integrates single-image grounding with multi-image comprehension to address this challenge.
Outcome:	The proposed model outperforms existing models in multi-image grounding tasks by 24.94% and surpasses larger 70B models.

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Jinfeng Zhou, Zhuang Chen, Bo Wang, Minlie Huang

Progressively Modality Freezing for Multi-Modal Entity Alignment (2024.acl-long)

Challenge:	Existing approaches to provide emotional support (ESC) ignore the effect on ES and lack explicit goals to guide emotional positive transition.
Approach:	They propose a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation.
Outcome:	The proposed model outperforms existing models in achieving positive emotion elicitation while maintaining conversational goals like coherence.

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Yani Huang, Xuefeng Zhang, Richong Zhang, Junfan Chen, Jaein Kim

Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time (2026.findings-acl)

Challenge:	Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs.
Approach:	They propose a strategy of progressive modality freezing that focuses on alignment-relevant features and enhances multi-modal feature fusion.
Outcome:	The proposed approach demonstrates state-of-the-art performance and the rationale for freezing modalities.

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Mingkuan Zhao, Yide Gao, Wentao Hu, Suquan Chen, Tianchen Huang, Zhenhua An, Zetao Chang, Xiayu Sun, Yuheng Min

Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)

Challenge:	Existing mitigation strategies rely on suppressing specific neuron activations or employing computationally expensive contrastive decoding mechanisms, which often result in increased perplexity or significantly elevated inference latency.
Approach:	They propose a lightweight inference-time intervention method grounded in the perspective of residual stream signal dynamics to resolve the signal attenuation of external evidence during its propagation through deep networks.
Outcome:	The proposed method improves contextual faithfulness across multiple factual consistency and strong knowledge-conflict tasks while maintaining the model’s general language understanding capabilities.

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Zujie Liang, Huang Hu, Can Xu, Chongyang Tao, Xiubo Geng, Yining Chen, Fan Liang, Daxin Jiang

R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory (2025.acl-long)

Challenge:	Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world.
Approach:	They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge .
Outcome:	The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation.

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Tenghao Huang, Kinjal Basu, Ibrahim Abdelaziz, Pavan Kapanipathi, Jonathan May, Muhao Chen

Challenge:	Existing methods for web agents struggle with efficient navigation and action execution due to limited visibility and understanding of web structures.
Approach:	They propose a framework that integrates memory-enhanced navigation and reflective learning to improve web agents' performance.
Outcome:	The proposed framework shows significant improvements over existing methods, including 50% reduction in navigation errors and threefold increase in task completion rates.

The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)

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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul

Challenge:	Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking.
Approach:	They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model.
Outcome:	The proposed model is more useful than the F-scores reflect and can be used in clinical notes.

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

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

Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-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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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag

PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022.coling-1)

Challenge:	Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation.
Approach:	They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem.
Outcome:	The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance.

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Xiaochen Liu, Yang Gao, Yu Bai, Jiawei Li, Yinan Hu, Heyan Huang, Boxing Chen

Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding (2023.emnlp-industry)

Challenge:	Experimental results show that our method outperforms full-model tuning in few-shot abstractive summarization tasks.
Approach:	They propose a soft prompts architecture with prompt pre-training and prompt fine-tuning paradigm to support few-shot abstractive summarization.
Outcome:	The proposed model outperforms Prompt Tuning and Profix-Tuning on CNN/DailyMail and XSum datasets and outperfies Profix Tuning by a large margin.

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Zheng Chen, Ziyan Jiang, Fan Yang, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Aram Galstyan

XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding (2023.findings-acl)

Challenge:	Defective queries impact the robustness of conversational AI systems such as Alexa, Siri or Google Assistant.
Approach:	They propose a Personalized Query Rewriting system that takes into account individual preferences or unique error patterns identified from a user's historical interactions with the conversational AI.
Outcome:	The proposed approach has been proven on a large-scale real-world dataset and online A/B experiments.

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Moming Tang, Chengyu Wang, Jianing Wang, Chuanqi Tan, Songfang Huang, Cen Chen, Weining Qian

LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing approaches to fine-tune visual-language understanding (VLU) require tasks-specific designs and sufficient training data.
Approach:	They propose a simple yet efficient paradigm for low-resource Visual Language Understanding (VLU) they reformulate a series of VLU tasks as an open-book affinity-matching problem.
Outcome:	The proposed framework outperforms baselines in low-resource settings.

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Yilin Xiao, Jin Chen, Qinggang Zhang, Yujing Zhang, Chuang Zhou, Longhao Yang, Lingfei Ren, Xin Yang, Xiao Huang

On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization (2024.findings-emnlp)

Challenge:	Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) however, traditional RAG attacks are difficult to pose an effective threat to GraphRAg systems.
Approach:	They propose a novel attack framework that targets logical reasoning rather than injecting false contents into GraphRAG systems by grounding their responses in structured knowledge graphs.
Outcome:	The proposed framework outperforms state-of-the-art attacks on GraphRAG systems in both effectiveness and stealth.

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Yong Lin, Skyler Seto, Maartje Ter Hoeve, Katherine Metcalf, Barry-John Theobald, Xuan Wang, Yizhe Zhang, Chen Huang, Tong Zhang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences.
Approach:	They compare the accuracy of DPORM and EXRM with a reward function for scoring human preferences.
Outcome:	The proposed methods can approximate an EXRM on the limit infinite samples, but it is unclear how effective they are in practice.

FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion (2024.findings-acl)

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Fred Xu, Song Jiang, Zijie Huang, Xiao Luo, Shichang Zhang, Yuanzhou Chen, Yizhou Sun

Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models (2024.naacl-long)

Challenge:	Existing work models taxonomy concepts as vectors or geometric objects, but fuzzy sets are efficient for concept modeling.
Approach:	They propose a set representation learning task based on fuzzy set approximation . they demonstrate remarkable improvements in taxonomy expansion using FUSE .
Outcome:	The proposed framework improves taxonomy expansion performance by 23% over baselines.

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Shuaijie She, Shujian Huang, Xingyun Wang, Yanke Zhou, Jiajun Chen

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

Challenge:	LLMs generate responses following user's instructions, which requires high dialogue comprehension ability.
Approach:	They propose to evaluate LLMs' dialogue comprehension ability using a dialogue summarization task to derive factual questions from the generated summaries and use them as a more flexible measurement of dialogue comprehension.
Outcome:	The proposed model reduces the error rate by 11% on the dialogue summarization task.

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

Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model (2025.emnlp-main)

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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Xinyue Lou, You Li, Jinan Xu, Xiangyu Shi, Chi Chen, Kaiyu Huang

Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)

Challenge:	Existing Large Reasoning Models have demonstrated broad application potential, yet their safety and reliability remain critical concerns.
Approach:	They conduct a safety evaluation of 13 MLRMs across 5 benchmarks and examine their safety performance.
Outcome:	The proposed model improves safety on jailbreak and safety-awareness benchmarks.

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Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang

Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System (2021.emnlp-main)

Challenge:	Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive.
Approach:	They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory .
Outcome:	The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient.

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Libo Qin, Tianbao Xie, Shijue Huang, Qiguang Chen, Xiao Xu, Wanxiang Che

Pre-trained Semantic Interaction based Inductive Graph Neural Networks for Text Classification (2025.coling-main)

Challenge:	Consistency Identification has been used for preventing inconsistent response generation, but few efforts have been made to task-oriented dialogue.
Approach:	They propose a dataset for Consistency Identification in task-oriented dialog system.
Outcome:	The proposed dataset is based on a single label and provides fine-grained labels to encourage model to know what inconsistent sources lead to it.

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Shiyu Wang, Gang Zhou, Jicang Lu, Jing Chen, Ningbo Huang

ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research (2025.acl-industry)

Challenge:	Existing methods for text classification have vanishing or exploding gradients when dealing with long sequences, making it difficult to handle long-distance dependencies.
Approach:	They propose a graph neural network based on pre-trained semantic interaction called PaSIG . they construct a text-word heterogeneity graph and use context representation capability .
Outcome:	The proposed model outperforms existing methods on five datasets and achieves state-of-the-art performance.

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Zhiyuan Wang, Bokui Chen, Yinya Huang, Qingxing Cao, Ming He, Jianping Fan, Xiaodan Liang

Self-Adjust Softmax (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown promising results in various domains, but their practical application in industry-relevant operations research presents significant challenges and opportunities.
Approach:	They propose a cognitive-inspired framework that enhances optimization through counterfactual reasoning . they use a workflow that transforms requirements into mathematical models and executable solver code .
Outcome:	Experiments show that ORMind outperforms existing methods in the NL4Opt dataset and ComplexOR dataset.

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Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li

OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)

Challenge:	Usually, tokens with larger attention scores are important for the final prediction.
Approach:	They propose to modify softmax(z) to z softmax and its normalized variant to improve the Transformer attention mechanism by making minor adjustments to the softmax function.
Outcome:	The proposed model provides enhanced gradient properties compared to the vanilla softmax function.

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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen

Inquisitive Question Generation for High Level Text Comprehension (2020.emnlp-main)

Challenge:	a large number of natural language processing tasks focus on token-level or sentence-level understandings.
Approach:	They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction .
Outcome:	The proposed model can be used to extract information from text without training and deployment.

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Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, Greg Durrett, Junyi Jessy Li

Label-Specific Document Representation for Multi-Label Text Classification (D19-1)

Challenge:	Existing data-driven questions generate questions that fill gaps in knowledge . a dataset of 19K questions is used to generate meaningful questions .
Approach:	They propose a dataset of 19K questions that are elicited while a person is reading a document.
Outcome:	The proposed model generates reasonable questions, but the task is challenging.

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Lin Xiao, Xin Huang, Boli Chen, Liping Jing

SEAL: Structure and Element Aware Learning Improves Long Structured Document Retrieval (2025.emnlp-main)

Challenge:	Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels.
Approach:	They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation.
Outcome:	The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis .

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Xinhao Huang, Zhibo Ren, Yipeng Yu, Ying Zhou, Zulong Chen, Zeyi Wen

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026.findings-acl)

Challenge:	Existing methods for document retrieval use contrastive learning on datasets lacking explicit structural information.
Approach:	They propose a contrastive learning framework that preserves semantic hierarchies and masked element alignment for fine-grained semantic discrimination.
Outcome:	The proposed framework preserves semantic hierarchies and masked element alignment for fine-grained semantic discrimination.

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Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, Jiecao Chen

JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning (2025.emnlp-main)

Challenge:	Currently, there are no efficient reinforcement learning (RL) frameworks specifically designed for tool use.
Approach:	They propose an automated environment construction pipeline that incorporates scenario decomposition, document generation, function integration, complexity scaling, and localized deployment to enable high-quality training environments without external tools.
Outcome:	The proposed framework significantly improves the models’ tool-use performance without degrading their general capabilities.

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Huanghai Liu, Quzhe Huang, Qingjing Chen, Yiran Hu, Jiayu Ma, Yun Liu, Weixing Shen, Yansong Feng

Knowledge Representation

Challenge:	Recent studies have introduced legal theories into LLM workflows to improve their understanding of legal texts and reasoning accuracy.
Approach:	They evaluate an expert-annotated four-element knowledge base covering 155 criminal charges.
Outcome:	The proposed model can be used to analyze criminal charges and retrieve them in legal cases.

Query-Aware Knowledge Retrieval via Hyperbolic Structuring (2026.acl-long)

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Chuang Zhou, Junnan Dong, Yilin Xiao, Shengyuan Chen, Su Dong, di Yin, Xing Sun, Zhaozhuo Xu, Xiao Huang

PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments (2026.findings-acl)

Challenge:	Existing approaches focus primarily on retrieving isolated factual knowledge entities while neglecting the critical reasoning relationships.
Approach:	They propose a query-centric retrieval framework that explicitly integrates structured knowledge graphs to support complex reasoning tasks.
Outcome:	Extensive experiments on three benchmark datasets show that HyperRAG outperforms baselines.

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Zhuang Chen, Dazhen Wan, Zhangkai Zheng, Guanqun Bi, Xiyao Xiao, Binghang Li, Minlie Huang

Challenge:	evaluating therapeutic competence of large language models remains challenging due to unstructured and longitudinal nature of counseling.
Approach:	They propose a framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments.
Outcome:	The proposed framework calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments.

Analyzing and Modeling LLM Response Lengths with Extreme Value Theory: Anchoring Effects and Hybrid Distributions (2025.emnlp-main)

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Liuxuan Jiao, Chen Gao, Yiqian Yang, Chenliang Zhou, YiXian Huang, Xinlei Chen, Yong Li

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

Challenge:	Existing approaches treat length as an incidental output property rather than a statistically regular phenomenon worthy of rigorous modeling.
Approach:	They propose a statistical framework for modeling and controlling large language model response lengths using extreme value theory and cross-validation on Qwen and DeepSeek architectures.
Outcome:	The proposed model improves tail fit and generalizability while maintaining generalizzability.

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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

Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness (2024.findings-emnlp)

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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Jian Li, Haojing Huang, Yujia Zhang, Pengfei Xu, Xi Chen, Rui Song, Lida Shi, Jingwen Wang, Hao Xu

MHALO: Evaluating MLLMs as Fine-grained Hallucination Detectors (2025.findings-acl)

Challenge:	Recent studies have focused on replacing the reward model in Reinforcement Learning with Human Feedback (RLHF) methods for Large Language Models (LLMs).
Approach:	They propose a self-supervised preference optimization framework that replaces the reward model with a preference loss and alignment loss to improve LLMs' ability to understand human preferences.
Outcome:	The proposed framework can be integrated with existing preference optimization methods and significantly boost their performance.

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Yishuo Cai, Renjie Gu, Jiaxu Li, Xuancheng Huang, Junzhe Chen, Xiaotao Gu, Minlie Huang

Explainable and Sparse Representations of Academic Articles for Knowledge Exploration (2020.coling-main)

Challenge:	Hallucination remains a critical challenge for multimodal large language models, undermining their reliability in real-world applications.
Approach:	They propose a benchmark specifically designed for evaluating MLLMs’ capability in performing token-level hallucination detection (FHD) . they use curated training data to train a specialized model that significantly outperforms existing models.
Outcome:	The proposed model outperforms existing models in the evaluation of 9 MLLMs and reaches an average F1IoU of 40.59%.

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Keng-Te Liao, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, PoChun Chen, Kuansan Wang, Shou-de Lin

VLMInferSlow: Evaluating the Efficiency Robustness of Large Vision-Language Models as a Service (2025.acl-long)

Challenge:	a system for summarizing academic articles by concept tagging has shown great coverage and high accuracy of concept identification.
Approach:	They propose to transform tagged concepts into sparse vectors as representations of academic documents.
Outcome:	The proposed system can be applied to a broader class of applications.

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Xiasi Wang, Tianliang Yao, Simin Chen, Runqi Wang, Lei Ye, Kuofeng Gao, Yi Huang, Yuan Yao

Sharper and Faster mean Better: Towards More Efficient Vision-Language Model for Hour-scale Long Video Understanding (2025.acl-long)

Challenge:	Existing studies evaluate efficiency robustness of vision-language models under unrealistic assumptions, requiring access to model architecture and parameters.
Approach:	They propose a novel approach to evaluate VLM efficiency robustness in a realistic black-box setting.
Outcome:	The proposed approach generates adversarial images with imperceptible perturbations, increasing the computational cost by up to 128.47%.

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Daoze Zhang, Yuze Zhao, Jintao Huang, Yingda Chen

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Existing multimodal large language models (LLMs) have shown impressive performance on the video understanding task, but extremely long videos still pose significant challenges to their context length, memory consumption, and computational complexity.
Approach:	They propose a vision-language model named Sophia for long video understanding which can efficiently handle hour-scale long videos.
Outcome:	The proposed model exhibits competitive performance compared to existing video understanding baselines across various benchmarks for long video understanding with reduced time and memory consumption.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Pei Chen, Haibo Ding, Jun Araki, Ruihong Huang

FaLA: Fast Linear Adaptation for Replacing Backbone Models on Edge Devices (2023.findings-emnlp)

Challenge:	Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains.
Approach:	They propose to explicitly connect entity mentions based on global coreference relations and local dependency relations to build better entity mention representations.
Outcome:	The proposed system improves the NER performance even with a tiny amount of labeled data.

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Shuo Huang, Lizhen Qu, Xingliang Yuan, Chunyang Chen

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

Challenge:	Current NLP models heavily rely on pre-trained models, such as BERT and RoBERTa.
Approach:	They propose a lightweight method for personalized NLP classification tasks post-backbone replacement using a personalized matrix calculated from documents corresponding to users' old and new backbones.
Outcome:	The proposed method achieves over 1000 times computation reduction in Flops for backpropagation and brings the user-specific initialization for personal matrix yielding significant performance boost compared with popular transfer learning methods.

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

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

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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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarization (2020.aacl-main)

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Kuan-Hao Huang, Chen Li, Kai-Wei Chang

Dialogue State Tracking with Explicit Slot Connection Modeling (2020.acl-main)

Challenge:	Existing methods to generate sports summarization tasks are laborintensive and infeasible.
Approach:	They propose a Chinese dataset for sports game summarization and a model that consists of a selector and rewriter to evaluate the correctness of generated sports summaries.
Outcome:	The proposed model performs better on ROUGE and the two designed scores.

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Yawen Ouyang, Moxin Chen, Xinyu Dai, Yinggong Zhao, Shujian Huang, Jiajun Chen

RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction (2022.emnlp-main)

Challenge:	Existing methods to track dialogue state are lacking in multi-domain scenarios.
Approach:	They propose a model that explicitly considers slot correlations across domains . they propose ellipsis and reference to express values that have been mentioned by slots from other domains.
Outcome:	The proposed model outperforms existing models on multi-domain datasets and achieves state-of-the-art performance.

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Yunqi Zhang, Yubo Chen, Yongfeng Huang

Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)

Challenge:	Existing methods focused on capturing semantic information but failed to incorporate syntactic structures of the sentence, which is proved to contain rich relational information.
Approach:	They propose a framework to capture syntactic information for relational triple extraction by contracting dependency tree into a core relational topology and eliminating redundant information with graph pooling operations.
Outcome:	The proposed framework incorporates syntactic information for relational triple extraction.

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Jiashu Pu, Ziyi Huang, Yadong Xi, Guandan Chen, Weijie Chen, Rongsheng Zhang

ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)

Challenge:	Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions.
Approach:	They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features.
Outcome:	The proposed method replaces text with linguistic or statistical features and improves the accuracy of the model.

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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)

Challenge:	Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models.
Approach:	They propose a dataset that provides rigorous evaluation of multi-hop tool use.
Outcome:	The proposed model achieves 49.04% accuracy across five model families.

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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks.
Approach:	They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment.
Outcome:	The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment.

BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training (2023.acl-long)

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Yiming Yan, Tao Wang, Chengqi Zhao, Shujian Huang, Jiajun Chen, Mingxuan Wang

CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)

Challenge:	Automated metrics are used for machine translation, but they are often considered to be black boxes with potential biases that are difficult to detect.
Approach:	They analyze automatic metrics from the perspective of their guidance for machine translation training.
Outcome:	The proposed measures improve the performance of machine translation models.

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Xin Sheng, Linli Xu, Yinlong Xu, Changcun Bao, Huang Chen, Bo Ren

Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)

Challenge:	Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators.
Approach:	They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples.
Outcome:	The proposed model improves on synthetic and real category text generation datasets.

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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee

Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)

Challenge:	Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components.
Approach:	They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components.
Outcome:	The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility.

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Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang

KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)

Challenge:	Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously.
Approach:	They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality.
Outcome:	The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency.

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Wei Chen, Shiqi Wei, Zhongyu Wei, Xuanjing Huang

Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models (2025.acl-long)

Challenge:	Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate.
Approach:	They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom .
Outcome:	The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios.

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Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, Michael R. Lyu

GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering (2025.coling-main)

Challenge:	Medical Multi-Modal Large Language Models (Med-MLLMs) are a promising new form of artificial general intelligence due to their ability to tackle complex tasks.
Approach:	They propose a new benchmark that comprehensively assesses medical multi-modal large language models in terms of distinct medical specialties and different diagnostic capacities.
Outcome:	The proposed model covers 15 medical specialties and different diagnostic capacities, and excludes overlap with existing VQA dataset.

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Qianlong Li, Chen Huang, Shuai Li, Yuanxin Xiang, Deng Xiong, Wenqiang Lei

Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)

Challenge:	Existing methods for complex table question answering are often implicit, feeding the entire table into prompts.
Approach:	They propose a GraphOTTER that explicitly establishes the reasoning process to pinpoint the correct answers.
Outcome:	The proposed method is able to identify the correct answers on two benchmark datasets and two LLM backbones.

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Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang

Challenge:	Existing methods to measure scholarly impact of documents without citations only consider word frequency change.
Approach:	They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts.
Outcome:	The proposed model outperforms existing models on document influence evaluation without citations.

CodeV: Issue Resolving with Visual Data (2025.findings-acl)

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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang

From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding (2026.acl-long)

Challenge:	Large Language Models (LLMs) have expanded to more complex repository-level tasks.
Approach:	They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues.
Outcome:	The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data.

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Yandi Wang, Libin Zhan, Ziwei Huang, Tiancheng Luo, Yuxuan Jiang, Wang Dong, Leilei Gan, Jun Chen

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

Challenge:	Existing models for visual information extraction suffer from limitations in scale and realism . ReceiptBench is a large-scale, human-annotated benchmark for receipts .
Approach:	They propose a large-scale, human-annotated benchmark for visual information extraction . the method organizes information extraction into four hierarchical sub-tasks .
Outcome:	The proposed method surpasses proprietary models on complex reasoning tasks.

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

Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)

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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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen

Privacy Evaluation Benchmarks for NLP Models (2024.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem.
Approach:	They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces.
Outcome:	The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems.

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Wei Huang, Yinggui Wang, Cen Chen

SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning (2024.naacl-long)

Challenge:	Several kinds of privacy attacks are studied in depth, but they are non-systematic and lack a comprehensive understanding of the impact caused by the attacks.
Approach:	They propose a privacy attack and defense evaluation benchmark in the field of NLP . they propose an improved attack method and a chained framework for privacy attacks .
Outcome:	The proposed framework can be chained to achieve a higher-level attack objective.

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Bin Wang, Zhengyuan Liu, Xin Huang, Fangkai Jiao, Yang Ding, AiTi Aw, Nancy Chen

Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)

Challenge:	a new benchmark for multilingual foundation models is being developed . brittleness of foundation models in the dimensions of semantics and multilinguality is a key limitation .
Approach:	They propose a benchmark for multilingual foundation models, SeaEval . they examine how well these models comprehend cultural practices, nuances, and values .
Outcome:	The proposed model can be used to evaluate multilingual and multicultural scenarios.

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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang

H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents (2026.eacl-long)

Challenge:	Recent advances in neural models have shown promising progress on this task, but key challenges remain .
Approach:	They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template .
Outcome:	The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial.

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Zihe Ye, Jingyuan Huang, Weixin Chen, Yongfeng Zhang

Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)

Challenge:	Existing frameworks for long-context conversational agents struggle to organize information across dimensions like time and topic, leading to poor retrieval.
Approach:	They propose a Hybrid Multi-Dimensional Memory architecture that stores conversational facts in two parallel hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that arranges it conceptually.
Outcome:	The proposed architecture improves performance on long-context QA datasets by 8.4% compared to current systems.

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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu

GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning (2026.findings-acl)

Challenge:	Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections.
Approach:	They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight.
Outcome:	The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%.

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Fengyi Wu, Yifei Dong, Yilong Dai, Guangyu Chen, Qifeng Wu, Huiting Huang, Hang Wang, Qi Dai, Alexander G Hauptmann, Zhi-Qi Cheng

All Languages Matter: On the Multilingual Safety of LLMs (2024.findings-acl)

Challenge:	Current methods for instruction generation depend on privileged inputs such as semantic maps, landmark annotations, and panoramic views.
Approach:	They propose a task that generates coherent navigation instructions from egocentric visual observations.
Outcome:	The proposed task generates coherent navigation instructions from egocentric visual data . the proposed task improves performance over state-of-the-art methods in BLEU-4 and CIDEr scores .

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Wenxuan Wang, Zhaopeng Tu, Chang Chen, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, Michael Lyu

History-Aware Conversational Dense Retrieval (2024.findings-acl)

Challenge:	Existing safety benchmarks only concern the safety in one language, e.g. the majority language in the pretraining data such as English.
Approach:	They propose a prompting method to improve multilingual safety of ChatGPT by enhancing cross-lingual generalization of safety alignment.
Outcome:	The proposed method can significantly reduce the ratio of unsafe responses by 42% for non-English queries.

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Fengran Mo, Chen Qu, Kelong Mao, Tianyu Zhu, Zhan Su, Kaiyu Huang, Jian-Yun Nie

Task-Level Instructions Induction for Audio Question Answering from Few Examples (2026.eacl-short)

Challenge:	Current approaches for conversational dense retrieval rely on fine-tuning a pre-trained ad-hoc retriever, which can be lengthy and noisy.
Approach:	They propose a context-denoised query reformulation and automatic mining of supervision signals based on historical turns.
Outcome:	The proposed system improves on two public conversational search datasets.

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Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)

Challenge:	Large audio-language models benefit from Chain-of-Thought (CoT) prompting for audio question answering (AQA) however, acquiring audio CoT examples is difficult as it requires sequential listening and careful integration of acoustic and linguistic information.
Approach:	They propose a method which induces reusable task instructions from few audio examples once per task.
Outcome:	Evaluated on 9 LALMs across two benchmarks, Audio-Induct outperforms state-of-the-art prompting methods while maintaining low inference costs.

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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li

Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement (2025.findings-naacl)

Challenge:	Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities.
Outcome:	The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks.

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Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Taha Kass-Hout, Furong Huang, Cao Xiao

On Synthetic Data for Back Translation (2022.naacl-main)

Challenge:	Existing methods for visual and language alignment depend on external models or data, leading to uncontrollable and unstable results.
Approach:	They propose a framework that enhances visual and language alignment without external dependencies by incorporating an in-context self-critic mechanism that constructs preference pairs for tuning.
Outcome:	The proposed framework outperforms existing methods and improves performance on 14 hallucination and comprehensive benchmarks.

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Jiahao Xu, Yubin Ruan, Wei Bi, Guoping Huang, Shuming Shi, Lihui Chen, Lemao Liu

A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)

Challenge:	Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance.
Approach:	They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields .
Outcome:	The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks.

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Lei Chen, Guanying Li, Zhongyu Wei, Yang Yang, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges.
Approach:	They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification.
Outcome:	The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification.

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation (2026.acl-long)

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Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi

MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)

Challenge:	Multi-domain machine translation (MDMT) is a unique challenge due to varying levels of linguistic complexity across domains.
Approach:	They propose a resource-rational framework that learns to modulate inference between intuitive and deliberate reasoning.
Outcome:	Evaluated on 15 benchmarks spanning in-domain and out-of-domain settings, as well as 3 seen and 59 unseen languages, with ablations across three backbone models, TwT-7B and Twt-14B outperform much larger SOTA reasoning models in translation quality, while reducing token usage by 32–60%.

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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang

Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)

Challenge:	Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases.
Approach:	They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process.
Outcome:	The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations.

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Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu

Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification (2021.findings-emnlp)

Challenge:	Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands.
Approach:	They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans .
Outcome:	The proposed approach reduces human intervention and improves safety and efficiency.

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Siyu Lai, Hui Huang, Dong Jing, Yufeng Chen, Jinan Xu, Jian Liu

PairDistill: Pairwise Relevance Distillation for Dense Retrieval (2024.emnlp-main)

Challenge:	Recent multilingual pre-trained models have been demonstrated effective in many cross-lingual tasks.
Approach:	They propose a framework that leverages code-switched data with multi-view learning to fine-tune XLM-R.
Outcome:	The proposed model achieves state-of-the-art on zero-shot cross-lingual sentiment classification and dialogue state tracking tasks.

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Chao-Wei Huang, Yun-Nung Chen

Prompt-based Zero-shot Text Classification with Conceptual Knowledge (2023.acl-srw)

Challenge:	Recent advances in dense retrieval have demonstrated remarkable efficacy compared to traditional sparse retrieval methods.
Approach:	They propose to use pairwise relevance distillation to leverage pairwise reranking to enrich the training of dense retrieval models.
Outcome:	The proposed method outperforms existing methods and achieves state-of-the-art results on multiple benchmarks.

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Yuqi Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, Suparna De

Named Entity Recognition in Multi-level Contexts (2020.aacl-main)

Challenge:	Existing approaches to pre-training language models rely on verbalizers to translate the predicted vocabulary to task-specific labels.
Approach:	They propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting.
Outcome:	The proposed framework outperforms prompt-based approaches on four widely-used datasets for sentiment analysis and topic detection on the same experimental settings.

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Yubo Chen, Chuhan Wu, Tao Qi, Zhigang Yuan, Yongfeng Huang

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

Challenge:	Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts.
Approach:	They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information.
Outcome:	The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information.

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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

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

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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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang

Guided K-best Selection for Semantic Parsing Annotation (2022.acl-demo)

Challenge:	Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult.
Approach:	They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples.
Outcome:	The proposed approach improves preference while preserving utility.

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Anton Belyy, Chieh-yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme

Modeling Semantic Compositionality with Sememe Knowledge (P19-1)

Challenge:	a prototype model trained on a small amount of data is not available, leading to limited prediction performance.
Approach:	They propose a human-in-the-loop process that generates a set of valid candidates and allows users to quickly traverse the set and filter incorrect parses.
Outcome:	The proposed process can be used to efficiently traverse the candidate set and select the correct parse, with minimal modification when necessary.

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Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun

Learning Diverse Responses with Prefix-Conditioned Supervised Fine-Tuning (2026.acl-long)

Challenge:	Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents.
Approach:	They propose to incorporate sememes into SC models and employ them in learning multiword expressions.
Outcome:	The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge.

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Zhiyuan Fan, Guanqiao Chen, Yanyi Huang, Mingkuan Zhao, Dadi Guo, Yi R. Fung

Improving Long-Context Translation via Self-Supervised Dual Learning (2026.acl-long)

Challenge:	Large language models exhibit highly homogeneous, repetitive responses, resulting in inefficient exploration.
Approach:	They propose a method that constructs semantically consistent yet distributionally distinct prior contents to different responses and decouple the one-to-many mapping.
Outcome:	The proposed method improves absolute performance by 5.3% and increases generation diversity by 198.3% on average while significantly enhancing output diversity and test-time scaling.

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Shanbo Cheng, Shuaijie She, Yu Bao, Jianbing Zhang, Jiajun Chen, Shujian Huang

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)

Challenge:	Large language models with long context windows suffer from catastrophic information distortion, undermining the strict faithfulness required for translation.
Approach:	They propose a self-supervised post-training framework that improves long-document translation reliability via round-trip consistency.
Outcome:	The proposed framework improves long-document translation reliability via round-trip consistency.

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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo

Challenge:	Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench .
Approach:	They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool .
Outcome:	The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions .

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction (2020.coling-main)

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Charles Hinson, Hen-Hsen Huang, Hsin-Hsi Chen

Energy-based Unknown Intent Detection with Data Manipulation (2021.findings-acl)

Challenge:	Recent work in the field of grammatical error correction (GEC) rely on neural machine translation-based models.
Approach:	They propose a heterogeneous approach to Chinese grammatical error correction using NMT-based models, sequence editing models, and a spell checker.
Outcome:	The proposed model achieves state-of-the-art performance without data augmentation or changes in architecture . it adapts the ERRANT scorer to be able to score Chinese sentences .

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Yawen Ouyang, Jiasheng Ye, Yu Chen, Xinyu Dai, Shujian Huang, Jiajun Chen

Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments (P19-1)

Challenge:	Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
Approach:	They propose a framework to generate high-quality OOD utterances with importance weighTs (GOT) their framework is fine-tuned to detect out-of-distribution utterrances .
Outcome:	The proposed framework can achieve state-of-the-art results on two benchmark datasets.

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Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen

Challenge:	Numeracy is the ability to predict the magnitude of a numeral at some specific position in a text description.
Approach:	They propose to use a dataset to test whether neural network models can learn numeracy . numerability is the ability to predict the magnitude of a numeral at some specific position in a text description.
Outcome:	The proposed task can predict the magnitude of a numeral at a specific position in a text description.

Improved Unbiased Watermark for Large Language Models (2025.acl-long)

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Ruibo Chen, Yihan Wu, Junfeng Guo, Heng Huang

OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference (2025.acl-long)

Challenge:	Unbiased watermarks allow to distinguish between text generated by humans and machines without causing distortion.
Approach:	They introduce a family of unbiased, Multi-Channel-based watermarks that partition the language model into segments and promote token probabilities within a selected segment based on a watermark key.
Outcome:	The proposed watermarks preserve the original distribution of the language model and offer significant improvements in detectability and robustness over existing unbiased watermark systems.

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Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang, Maosongcao Maosongcao, Jiaqi Wang, Weiyun Wang, Xinyu Fang, Wenhai Wang, Guangtao Zhai, Hua Yang, Haodong Duan, Kai Chen

Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)

Challenge:	Existing open-source multi-modal large language models (MLLMs) focus on enhancing foundational capabilities, leaving a significant gap in human preference alignment.
Approach:	They propose a dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences.
Outcome:	The proposed dataset of 200K high-quality training samples improves human preference alignment while maintaining or enhancing performance on standard VQA benchmarks.

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Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, Junnan Li

CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)

Challenge:	Using a multimodal model, GUI agents can ground from language instructions to target elements . relying on HTML or AXTree inputs is a challenge for GUI agents .
Approach:	They propose a large multimodal model specifically designed for GUI grounding that adopts a pure vision approach instead of auxiliary inputs.
Outcome:	The proposed model outperforms vision-only and AXTree-reliant models on offline and online agents.

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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang

KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)

Challenge:	Large language models (LLMs) have revolutionized natural language processing.
Approach:	They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy.
Outcome:	CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding.

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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu

Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (2024.findings-acl)

Challenge:	Recent advances in pre-trained language models have made it possible to generate human-like text.
Approach:	They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached.
Outcome:	The proposed game lacks incentives and relies on players to explore on their own.

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Zhuo Chen, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, Kewei Tu

MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models (2020.lrec-1)

Challenge:	Retrieval Augmented Generation can be used to process long contexts in Open-Domain Question-Answering tasks.
Approach:	They propose a method to cover longer contexts in Open-Domain Question-Answering tasks by using a small encoder language model and cross-attention with origin inputs.
Outcome:	The proposed method can cover longer contexts while keeping the computing requirements close to the baseline.

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Ting-Yu Yen, Yang-Yin Lee, Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)

Challenge:	Existing benchmarks for sense embeddings do not account for polysemy, and there are six issues with evaluations based on these datasets.
Approach:	They propose a multi-sense dataset with a high ratio of multi-word pairs to address the polysemy issue in word embeddings.
Outcome:	The proposed model performs better than existing models with single-sense word pairs and has a high ratio of multi-sensor word pairs.

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Shuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu, Tao Feng, Jun Cen, Zeying Huang, Yi Yang

PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer (2023.emnlp-main)

Challenge:	Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.
Approach:	They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities.
Outcome:	The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.

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Lichang Chen, Jiuhai Chen, Heng Huang, Minhao Cheng

Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation (2025.acl-long)

Challenge:	Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data .
Approach:	They propose an algorithm that smooths the loss landscape of vanilla prompt tuning by perturbation-based regularizers.
Outcome:	The proposed method improves the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks.

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Xiang Geng, Zhejian Lai, Jiajun Chen, Hao Yang, Shujian Huang

Teaching Language Models To Gather Information Proactively (2025.findings-emnlp)

Challenge:	Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task.
Approach:	They propose a framework for alleviating distribution shift in synthetic QE data . they employ a constrained beam search algorithm and distinct generation models to enhance translation diversity.
Outcome:	The proposed framework outperforms SOTA baselines like CometKiwi in supervised and unsupervised settings.

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Tenghao Huang, Sihao Chen, Muhao Chen, Jonathan May, Longqi Yang, Mengting Wan, Pei Zhou

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Large language models are often defaulted to passive responses or narrow clarifications when faced with incomplete or under-specified prompts.
Approach:	They propose a new task paradigm where LLMs must identify gaps in context and strategically elicit implicit user knowledge through targeted questions.
Outcome:	The proposed framework outperforms o3-mini on evaluation metrics and human annotators favor clarification questions and final outlines.

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Xin Zheng, Zhirui Zhang, Shujian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen

VChain: Chain-of-Visual-Thought for Reasoning in Video Generation (2026.findings-acl)

Challenge:	kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc.
Approach:	They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval.
Outcome:	The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation.

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Ziqi Huang, Ning Yu, Gordon Chen, Haonan Qiu, Paul Debevec, Ziwei Liu

MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships (2020.lrec-1)

Challenge:	Recent video generation models struggle to synthesize complex dynamics with a coherent chain of consequences.
Approach:	They propose a framework that injects visual reasoning signals from multimodal models into video generation.
Outcome:	a new framework that leverages multimodal models to generate sparse keyframes significantly improves quality of generated videos.

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Yi-Ting Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Open-Domain Conversational Question Answering with Historical Answers (2022.findings-aacl)

Challenge:	Existing datasets with emotion and relation labels for dialogues are limited.
Approach:	They use a Chinese dialogue dataset to annotate emotions and interpersonal relationships on each utterance.
Outcome:	The proposed dataset contains 25,548 utterances from 4,142 dialogues.

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Hung-Chieh Fang, Kuo-Han Hung, Chen-Wei Huang, Yun-Nung Chen