Papers by Zhou Li

RealTalk-CN: A Realistic Chinese Speech Task-Oriented Dialogue Benchmark with Cross-Modal Analysis (2026.acl-long)

Challenge:	a new metric is developed to pinpoint the moment of invocation when hallucinations arise in small LMs.
Approach:	They propose a metric that measures hallucinations during the generation process of small LMs.
Outcome:	The proposed metric outperforms baselines in hallucination detection across multiple QA datasets.

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Enzhi Wang, Jiaming Zhou, Yuhang Jia, Aobo Kong, Qicheng Li, Yong Qin

Cross-media User Profiling with Joint Textual and Social User Embedding (C18-1)

Challenge:	Recent advances in speech large language models have enabled end-to-end spoken interactions, but their robustness in real-world applications remains unclear.
Approach:	They propose a multi-turn, multi-domain speech–text TOD dataset for Chinese users . it contains 5.4k dialogues with annotations for dialogue states, disfluency types, speaker characteristics .
Outcome:	The proposed model can be used to evaluate speech large language models in real-world scenarios . the proposed model is based on 5.4k real human-to-human dialogues with annotations .

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Jingjing Wang, Shoushan Li, Mingqi Jiang, Hanqian Wu, Guodong Zhou

Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization (2022.acl-long)

Challenge:	Empirical studies demonstrate the effectiveness of the proposed approach to cross-media user profiling tasks.
Approach:	They propose a uniform user embedding learning approach to address cross-media user profiling by bridging the knowledge between the source and target media.
Outcome:	Empirical results show that the proposed approach performs well on two cross-media user profiling tasks.

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Juncai Guo, Jin Liu, Yao Wan, Li Li, Pingyi Zhou

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs (2026.acl-long)

Challenge:	Existing approaches to describe the syntax structure of code are lacking in retaining the semantic structure of source code.
Approach:	They propose to use a triplet position to model hierarchical syntax structure of code by introducing a graph neural network and Transformer to preserve the structural and sequential information of code.
Outcome:	The proposed model preserves the structural and sequential information of code and a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation.

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Wu Li, Yigeng Zhou, Zesheng Shi, Yequan Wang, Min Zhang, Jing Li

WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)

Challenge:	Recent self-training approaches have reduced reliance on human-labeled data, which limits their scalability.
Approach:	They propose a team-based self-play algorithm that iteratively refines alignment without additional human supervision.
Outcome:	The proposed algorithm outperforms baselines and LLM benchmarks in the self-supervised setting.

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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei

Literature Meets Data: A Synergistic Approach to Hypothesis Generation (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions.
Approach:	They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach .
Outcome:	The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size.

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Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, Chenhao Tan

Challenge:	Existing methods for hypothesis generation are theory-driven and data-driven, but they lack the computational power to complement each other.
Approach:	They develop a method that combines literature-based insights with data to perform LLM-powered hypothesis generation.
Outcome:	The proposed method outperforms baseline methods on five datasets and shows human accuracy improves on deception detection and AI generated content detection tasks.

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Antonia Karamolegkou, Jiaang Li, Li Zhou, Anders Søgaard

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction (2025.acl-long)

Challenge:	a recent study examines the extent to which language models can memorize training data . a fair use exemption to copyright laws allows for limited use of copyrighted material .
Approach:	They examine the extent to which language models can redistribute copyrighted text . they use a range of popular books and coding problems to study copyright violations .
Outcome:	This study examines the extent to which language models can redistribute copyrighted text . it shows that language models may memorize entire chunks of training data .

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Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, Zifei Shan

Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time (2025.emnlp-main)

Challenge:	Existing studies have focused on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, but the interpretabilty of these metrics has been neglected.
Approach:	They propose a reference-based metric that describes four aspects of GEC systems: hit-correction, wrong-corrections, under-correcties, and over-corrects.
Outcome:	The proposed metric reveals critical qualities and locates drawbacks of GEC systems.

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Jiazheng Li, Yuxiang Zhou, Junru Lu, Gladys Tyen, Lin Gui, Cesare Aloisi, Yulan He

Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)

Challenge:	Existing LLMs struggle to reliably detect subtle reasoning errors in ASAS tasks.
Approach:	They propose a dual-model framework with a dedicated Critic model trained for effective reflection that generates precise verbal feedback.
Outcome:	The proposed framework outperforms existing ASAS benchmarks and provides valuable insights into the performance of the proposed framework.

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Xuan Xu, Zhongliang Yang, Haolun Li, Rui Tian, Beilin Chu, J Song, Yu Li, Shaolin Tan, Linna Zhou

Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination? (2024.naacl-long)

Challenge:	Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing.
Approach:	They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows.
Outcome:	The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows.

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Bangzheng Li, Ben Zhou, Fei Wang, Xingyu Fu, Dan Roth, Muhao Chen

Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)

Challenge:	Existing large language models (LLMs) suffer from hallucinations and unfaithful reasoning due to keyword/entity biases.
Approach:	They propose a new probing method and benchmark to quantify this phenomenon by using a keyword/entity biases-based probing technique called EUREQA.
Outcome:	The proposed method achieves 62% accuracy on multi-hop and complex QA benchmarks.

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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou

Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)

Challenge:	Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes.
Approach:	They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset.
Outcome:	The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets.

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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan

From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test (2025.emnlp-main)

Challenge:	Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it .
Approach:	They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers.
Outcome:	The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy .

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Xunlian Dai, Li Zhou, Benyou Wang, Haizhou Li

Challenge:	Multilingual and cross-cultural WAT reveal how culture modulates perceptual and interactive patterns.
Approach:	They propose to embed cultural-specific semantic associations directly within large language models (LLMs) to address cultural preference.
Outcome:	The proposed model significantly improves cross-cultural alignment, capturing diverse semantic associations.

Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)

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Minghui An, Jingjing Wang, Shoushan Li, Guodong Zhou

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

Challenge:	Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed.
Approach:	They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection.
Outcome:	The proposed approach improves the performance of the primary task by using topic information from text and images.

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

GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)

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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Ting Zhou, Ying Shen, Yinghui Li

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

Challenge:	Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence.
Approach:	They propose a GCNet that explicitly leverages global semantic information to guide context encoding.
Outcome:	The proposed model outperforms state-of-the-art methods on three public datasets.

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

OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)

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

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Xize Cheng, Tao Jin, Linjun Li, Wang Lin, Xinyu Duan, Zhou Zhao

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)

Challenge:	Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech.
Approach:	They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment .
Outcome:	The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively.

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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu

Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models (2026.acl-long)

Challenge:	Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance.
Approach:	They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions.
Outcome:	The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models.

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Yifu Chen, Shengpeng Ji, Zhengqing Liu, Qian Chen, Wen Wang, Ziqing Wang, Yangzhuo Li, Tianle Liang, Zhou Zhao

Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)

Challenge:	Reinforcement learning (RL) has improved text- and vision-language models, but its application in SDMs is hindered.
Approach:	They propose a dual-axis Generative Reward Model that provides semantic quality and interaction timing for SDMs.
Outcome:	The proposed model achieves state-of-the-art performance on interaction-quality assessment across a wide spectrum of datasets.

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Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen

Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)

Challenge:	mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events.
Approach:	They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously.
Outcome:	The proposed model performs better on four public datasets while saving time.

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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei

MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs (2024.acl-long)

Challenge:	MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness .
Approach:	They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization.
Outcome:	The proposed model achieves superior performance across multiple metrics and is more streamlined.

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Zimu Lu, Aojun Zhou, Houxing Ren, Ke Wang, Weikang Shi, Junting Pan, Mingjie Zhan, Hongsheng Li

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	Existing models have demonstrated outstanding capabilities in mathematical reasoning, but there is a performance gap between open-source models and closed-source ones.
Approach:	They propose a method for generating diverse and reliable math problems by leveraging the ground-truth solutions of the seed data.
Outcome:	The proposed model outperforms open-source models across five representative mathematical reasoning datasets.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Text Embedding as Treatment: A Meta Causal Approach for Robust Sentiment Classification (2026.findings-acl)

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

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Fengxiang Cheng, Chuan Zhou, Xiang Li, Haoxuan Li, Wen-li Wang, Jinkun Chen, Mingming Gong, Kun Zhang

Distilling ChatGPT for Explainable Automated Student Answer Assessment (2023.findings-emnlp)

Challenge:	Existing methods for sentiment classification use binary treatment of words . Existing approaches limit generalizability to novel words and low-frequency words if there is a word in a sentence that is not treated .
Approach:	They propose a meta-causal approach that uses a single training task to identify causal words for arbitrary words.
Outcome:	The proposed method reduces the spurious correlation between word treatment and sentiment classification by removing words with low treatment effects from a pre-trained language model.

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Jiazheng Li, Lin Gui, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He

RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation (2026.acl-long)

Challenge:	Existing automated student answer assessment models lack explainable and faithful feedback.
Approach:	They propose a framework that leverages ChatGPT for student answer scoring and rationale generation.
Outcome:	The proposed method improves the overall QWK score by 11% compared to ChatGPT.

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Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Chenghu Zhou, Maosong Sun

Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)

Challenge:	Existing RS agents built on general-purpose LLMs are domain-agnostic, resulting in brittle and error-prone workflows.
Approach:	They propose a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution.
Outcome:	Experiments show that the new model improves tool-use performance and accuracy . iteratively, iteration of the model integrates online experience for robust multi-step tool execution .

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Shuhuai Ren, Jinchao Zhang, Lei Li, Xu Sun, Jie Zhou

Scaling LLM Inference Efficiently with Optimized Sample Compute Allocation (2025.naacl-long)

Challenge:	Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations.
Approach:	They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy .
Outcome:	The proposed framework outperforms baseline methods on six benchmark datasets.

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Kexun Zhang, Shang Zhou, Danqing Wang, William Yang Wang, Lei Li

LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)

Challenge:	Existing methods to optimize sample allocations for large language models fail to account for the optimal sampling configuration.
Approach:	They propose an algorithm that optimizes sample allocation by finding an optimal mix of different inference configurations.
Outcome:	The proposed algorithm achieves better accuracy on SWE-Bench with 3x less compute than the default configuration.

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Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou, Lei Zou

GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)

Challenge:	Existing supervised learning methods in natural language processing require large amounts of data.
Approach:	They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently.
Outcome:	The proposed model outperforms existing models with few-shot performance in two NLP tasks.

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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao

Linguistics & Cognitive NLP

Challenge:	Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes.
Approach:	They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior.
Outcome:	The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior.

Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction (2024.emnlp-main)

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Jinchuan Zhang, Yan Zhou, Yaxin Liu, Ziming Li, Songlin Hu

VideoEraser: Concept Erasure in Text-to-Video Diffusion Models (2025.emnlp-main)

Challenge:	Existing approaches focus on improving attack success rates while overlooking the need for comprehensive test case coverage.
Approach:	They propose a top-down approach to automated red teaming that scales up the diversity of test cases using an extensible, fine-grained risk taxonomy.
Outcome:	The proposed approach scales up the diversity of test cases using a top-down approach based on an extensible, fine-grained risk taxonomy and leverages reinforcement learning techniques to facilitate multi-turn adversarial probing in a human-like manner.

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Naen Xu, Jinghuai Zhang, Changjiang Li, Zhi Chen, Chunyi Zhou, Qingming Li, Tianyu Du, Shouling Ji

Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization (2023.findings-emnlp)

Challenge:	Experimental results show that VideoEraser outperforms prior methods regarding efficacy, integrity, fidelity, robustness, and generalizability.
Approach:	They propose a training-free framework that prevents T2V diffusion models from generating videos with undesirable concepts even when explicitly prompted with those concepts.
Outcome:	The proposed framework outperforms existing methods in erasure, celebrity erasion, and explicit content erasing tasks.

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Weixiao Zhou, Gengyao Li, Xianfu Cheng, Xinnian Liang, Junnan Zhu, Feifei Zhai, Zhoujun Li

Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023.findings-acl)

Challenge:	Existing methods for dialogue summarization only apply to specific scenarios and domains.
Approach:	They propose a pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization.
Outcome:	The proposed model significantly outperforms state-of-the-art models on three dialogue summarization datasets from different scenarios and domains.

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Ye Wang, Tao Jin, Wang Lin, Xize Cheng, Linjun Li, Zhou Zhao

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)

Challenge:	Existing approaches to visual segmentation from language queries require expensive labeling and degradation when deployed to an unseen domain.
Approach:	They propose a task to adapt a visual segmentation model from a labeled domain to an unseen domain.
Outcome:	The proposed framework achieves precise feature- and relation-invariant across domains via universal semantic structure.

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Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

Reflect-RL: Two-Player Online RL Fine-Tuning for LMs (2024.acl-long)

Challenge:	Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data.
Approach:	They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
Outcome:	The proposed language model generalizes well across knowledge-grounded dialogue tasks.

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Runlong Zhou, Simon Du, Beibin Li

Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective (2024.emnlp-main)

Challenge:	supervised fine-tuning (SFT) on a limited offline dataset does not yield good performance.
Approach:	They propose a two-player system to fine-tune an LM using SFT and online RL . they use negative example generation to enhance error-correction ability of the reflection model .
Outcome:	The proposed system outperforms SFT and online RL without reflection on a GPT-2 XL 1.56B model.

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Zhihao Zhang, Sophia Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)

Challenge:	Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP)
Approach:	They propose to automatically generate task-oriented knowledge using large language models (LLMs) and then employ task-orientated pre-training (TOPT) to facilitate domain adaptation.
Outcome:	The proposed model can learn to distinguish between different entities and improve its domain adaptation.

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Xin Xie, Dongyun Xue, Wuguannan Yao, Mingxiao Feng, Wengang Zhou, Xiang Qi, Houqiang Li, Peng Zhang

Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements (2024.findings-acl)

Challenge:	a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations.
Approach:	They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space .
Outcome:	Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning.

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Ming Li, Jiuhai Chen, Lichang Chen, Tianyi Zhou

Challenge:	Existing LLMs lack sufficient controllability to generate statements supporting diverse or even controversial perspectives.
Approach:	They develop a pipeline that fine tunes LLMs to generate statements generated via debate.
Outcome:	The proposed pipeline improves the controllability of LLMs in generating statements supporting an argument the user defined in the prompt.

EDEN: Empathetic Dialogues for English Learning (2024.findings-emnlp)

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Siyan Li, Teresa Shao, Zhou Yu, Julia Hirschberg

Emotion Analysis

Challenge:	Recent studies have shown that student passion and perseverance, or grit, is associated with language learning success.
Approach:	They hypothesize that as students perceive their English teachers to be more supportive, their grit improves.
Outcome:	The proposed chatbot improves student persistence in learning a second language.

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

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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

How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States (2024.findings-emnlp)

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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Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Yongbin Li

FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs’ Responsiveness to Human Feedback (2025.emnlp-main)

Challenge:	Large language models (LLMs) rely on safety alignment to avoid malicious user inputs.
Approach:	They employ weak classifiers to explain LLM safety through the intermediate hidden states.
Outcome:	The proposed model can identify malicious and normal inputs and detect malicious ones without jailbreak.

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Youquan Li, Miao Zheng, Fan Yang, Guosheng Dong, Bin Cui, Weipeng Chen, Zenan Zhou, Wentao Zhang

Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? (2024.findings-naacl)

Challenge:	Existing research focuses on benchmarking LLMs in single-turn dialogues, neglecting the nuanced nature of human feedback within real-world usage scenarios.
Approach:	They propose a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese.
Outcome:	The proposed benchmarks show that human feedback can significantly impact LLMs’ responsiveness in real-world usage scenarios.

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Jingyan Zhou, Minda Hu, Junan Li, Xiaoying Zhang, Xixin Wu, Irwin King, Helen Meng

MARS2: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation (2026.acl-long)

Challenge:	Existing approaches to making moral judgments are mostly bottom-up and lack explainability.
Approach:	They propose a top-down framework to steer Large Language Models to perform moral reasoning with well-established moral theories.
Outcome:	The proposed framework can integrate various moral theories on moral datasets.

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Pengfei Li, Shijie Wang, Fangyuan Li, Yikun Fu, Kaifeng Liu, Kaiyan Zhang, Dazhi Zhang, Yuqiang Li, Biqing Qi, Bowen Zhou

Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions (2025.emnlp-industry)

Challenge:	Existing approaches to reinforcement learning are decoupled from structured search due to limited trajectory diversity.
Approach:	They propose a unified RL framework that integrates multiple agents within a shared tree-structured search environment.
Outcome:	Experiments show that MARS2 improves performance across diverse model combinations and training settings.

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Jihang Li, Bing Xu, Zulong Chen, Chuanfei Xu, Minping Chen, Suyu Liu, Ying Zhou, Zeyi Wen

ReFreeKV: Towards Threshold-Free KV Cache Compression (2026.findings-acl)

Challenge:	Existing talent search approaches fail to capture nuanced job-specific preferences and mitigate noise from subjective human judgments.
Approach:	They propose a framework that extracts fine-grained recruitment signals from job descriptions and historical hiring data and employs a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles.
Outcome:	The proposed framework improves talent search effectiveness and delivers substantial business value.

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Xuanfan Ni, Liyan Xu, Chenyang Lyu, Longyue Wang, Mo Yu, Lemao Liu, Fandong Meng, Jie Zhou, Piji Li

ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)

Challenge:	Towards the KV cache efficiency, we propose a new objective that lifts the threshold constraints for robust KV compression.
Approach:	They propose a method that adjusts KV cache budgets while preserving full-cache performance.
Outcome:	The proposed method can reduce memory consumption while preserving full-cache performance.

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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou

Challenge:	Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth.
Approach:	They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning.
Outcome:	The proposed framework outperforms existing methods on ICLR 2025 papers.

On Efficient Retrieval of Top Similarity Vectors (D19-1)

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Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li

Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge (2025.naacl-long)

Challenge:	Existing representation learning methods such as Word2vec represent word embeddings in the semantic space.
Approach:	They propose an efficient method for searching vectors via a non-metric matching function: inner product.
Outcome:	Experiments on data representations learned for different machine learning tasks show the proposed method outperforms existing methods.

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Li Zhou, Taelin Karidi, Wanlong Liu, Nicolas Garneau, Yong Cao, Wenyu Chen, Haizhou Li, Daniel Hershcovich

On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)

Challenge:	Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet lack a robust methodology to dissect these phenomena comprehensively.
Approach:	They propose a multilingual dataset centered on food-related cultural facts and variations in food practices.
Outcome:	The proposed model incorporates cultural context significantly and improves its ability to access cultural knowledge.

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Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, Lei Li

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)

Challenge:	Pre-trained contextual representations like BERT have been widely used for NLP tasks.
Approach:	They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective.
Outcome:	The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks.

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Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao

Exploring Knowledge Filtering for Retrieval-Augmented Discriminative Tasks (2025.findings-acl)

Challenge:	Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline.
Approach:	They propose a framework that addresses transcription, alignment, and refined style annotations.
Outcome:	The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace.

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Minjie Qiang, Zhongqing Wang, Xiaoyi Bao, HaoYuan Ma, Shoushan Li, Guodong Zhou

Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles (2025.acl-long)

Challenge:	Recent studies have focused on generative tasks, while its potential in discriminative tasks remains largely unexplored.
Approach:	They propose a framework that incorporates knowledge filtering and prediction fusion mechanisms to improve model performance.
Outcome:	The proposed framework improves model performance on discriminative tasks by filtering out harmful knowledge and integrating it into the input context.

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Kuang Wang, Xianfei Li, Shenghao Yang, Li Zhou, Feng Jiang, Haizhou Li

An Empirical Study of Position Bias in Modern Information Retrieval (2025.findings-emnlp)

Challenge:	Existing user simulators lack authenticity and user-level diversity in interactions with large language models.
Approach:	They propose a user simulator with implicit user profiles that infers user profiles from human-machine interactions to simulate personalized and realistic dialogues.
Outcome:	The proposed framework outperforms baselines in authenticity and diversity while maintaining comparable consistency.

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Ziyang Zeng, Dun Zhang, Jiacheng Li, null Zoupanxiang, Yudong Zhou, Yuqing Yang

Think Smart, Not Hard: Difficulty Adaptive Reasoning for Large Audio Language Models (2026.findings-acl)

Challenge:	a new evaluation framework is used to assess the extent and impact of position bias in information retrieval.
Approach:	They introduce a position-aware retrieval benchmark and a diagnostic metric to quantify position bias . they compare models with BM25, dense embedding models, ColBERT-style late-interaction models .
Outcome:	The proposed framework evaluates retrieval models for position bias from a worst-case perspective.

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Zhichao Sheng, Shilin Zhou, Chen Gong, Zhenghua Li

Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

Challenge:	Existing methods to determine whether to perform reasoning lack fine-grained mechanisms to adapt reasoning length to problem complexity.
Approach:	They propose a difficulty-adaptive reasoning method that dynamically links reasoning length to the model’s perceived problem difficulty.
Outcome:	The proposed method reduces average reasoning length by 50%, achieving higher efficiency without sacrificing accuracy.

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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards (2026.acl-long)

Challenge:	Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach:	They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome:	The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.

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Xinyu Tang, Yuliang Zhan, Zhixun Li, Xin Zhao, Zhenduo Zhang, Zujie Wen, Zhiqiang Zhang, Jun Zhou

Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (2023.findings-acl)

Challenge:	Large reasoning models are typically trained using reinforcement learning with verifiable reward (RLVR) positive and negative self-generated rollouts are used to update the model's policy . positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths.
Approach:	They propose a method that allocates advantage signals to key tokens across different polarities.
Outcome:	The proposed method improves the ability of large reasoning models to learn from their own generated rollouts.

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Wangchunshu Zhou, Qifei Li, Chenle Li

Schoenfeld’s Anatomy of Mathematical Reasoning by Language Models (2026.acl-long)

Challenge:	Existing approaches to personalize dialogue agents rely on explicit persona descriptions during inference, which severely limits their application in real-world scenarios.
Approach:	They propose a method that learns to predict persona information based on the dialogue history to personalize dialogue agents without relying on explicit persona descriptions during inference.
Outcome:	The proposed method improves the consistency and engagingness of generated responses when conditioning on the predicted profile of the dialogue agent.

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Ming Li, Chenrui Fan, Yize Cheng, Soheil Feizi, Tianyi Zhou

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)

Challenge:	Large language models expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statistics.
Approach:	They propose a framework that explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, Verify, etc.
Outcome:	The proposed framework reveals reproducible thinking dynamics and structural differences between reasoning and non-reasoning models, which are not apparent from token-level views.

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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li

Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information (2020.coling-main)

Challenge:	Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly.
Approach:	They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show .
Outcome:	The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models.

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Wenyu Zhao, Dong Zhou, Lin Li, Jinjun Chen

Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)

Challenge:	Recent studies show word embedding models underestimate similarities between similar words and overestimate similarities between distant words.
Approach:	They propose two new word embedding methods that align original and re-fined embeddable spaces to a new refined semantic space.
Outcome:	The proposed methods outperform state-of-the-art methods for word representation refinement.

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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang

Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)

Challenge:	Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering.
Approach:	They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction.
Outcome:	The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions.

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Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, Guodong Zhou

Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025.coling-main)

Challenge:	Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately.
Approach:	They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis.
Outcome:	The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects.

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Wenjie Wei, Yanyue Zhang, Jinyan Li, Panfei Liu, Deyu Zhou

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)

Challenge:	Existing approaches to detect fake news in unseen domains are limited by domain-specific training.
Approach:	They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations .
Outcome:	The proposed method can detect fake news in unseen domains with the help of pre-trained language models.

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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu

Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring (2024.findings-emnlp)

Challenge:	Existing methods for grounding video frames with dense annotations require enormous amount of human effort.
Approach:	They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames .
Outcome:	The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques .

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Jiazheng Li, Hainiu Xu, Zhaoyue Sun, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He

Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)

Challenge:	Existing methods for generating rationales that justify scoring decisions are not accurate and often contain hallucinated information.
Approach:	They propose a framework capable of generating more faithful rationales and matching performance with classifier-based scoring systems.
Outcome:	The proposed framework achieves 38% improvement in QWK score compared to prior work . it can be used to match performance with classifier-based scoring systems .

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Zhaochen Su, Juntao Li, Zikang Zhang, Zihan Zhou, Min Zhang

Selective Differential Privacy for Language Modeling (2022.naacl-main)

Challenge:	Existing temporal language models are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components.
Approach:	They propose a method that captures syntactically changed tokens and captures the relationship between the time prefix and tokens.
Outcome:	The proposed method outperforms existing temporal language models on two datasets and three tasks.

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Weiyan Shi, Aiqi Cui, Evan Li, Ruoxi Jia, Zhou Yu

Ethical NLP

Challenge:	Existing methods to protect sensitive data from leaking are over-pessimistic and undifferentiated.
Approach:	They propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility.
Outcome:	The proposed privacy-preserving mechanism achieves better utility while remaining safe under various privacy attacks compared to baselines.

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)

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Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu

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

Challenge:	Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors.
Approach:	They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training.
Outcome:	The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets.

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

Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning (2026.eacl-long)

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

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Qifan Yu, Zhenyu He, Sijie Li, Zhou Xun, Jun Zhang, Jingjing Xu, Di He

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)

Challenge:	Chain-of-Thought prompting is a powerful technique for enhancing language model’s reasoning capabilities, but generating long and correct CoT trajectories is challenging.
Approach:	They propose to align the steps of Chain-of-Thought reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers.
Outcome:	The proposed method generates accurate reasoning chains for complex problems exceeding training length, and improves performance of the auto-regressive model.

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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su

IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)

Challenge:	Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
Approach:	They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples.
Outcome:	The proposed model outperforms several competitive benchmarks on four translation benchmarks.

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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection (2026.findings-acl)

Challenge:	Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion.
Approach:	They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree .
Outcome:	The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work.

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Boyang Li, Hongzhe Shou, Yuanyuan Liang, JingBin Zhang, Fang Zhou

Sparser Mixture-of-Adapters with Cross-Layer Generalization (2025.naacl-long)

Challenge:	Existing toxic content detection methods focus on sentence-level classification but fail to provide readable and contiguous toxic evidence spans.
Approach:	They propose an explainability-oriented method for Chinese toxic content detection methods . they refine saliency cues into fine-grained toxic spans with lightweight LLM guidance .
Outcome:	The proposed method improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent explanations.

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Ziyue Li, Tianyi Zhou

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Existing methods for training large language models do not allow sharing adapters across layers . existing methods do not support sharing adapter pools, leading to redundancy and poor generalization .
Approach:	They propose a mixture-of-adapter framework that trains a pool of lightweight adapters at each layer and selects the most suitable ones for each input.
Outcome:	The proposed framework reduces active adapters by over 85% while boosting task accuracy.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

What’s Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews (2026.acl-long)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

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Fanxiao Li, Jiaying Wu, Tingchao Fu, Dayang Li, Herun Wan, Wei Zhou, Min-Yen Kan

TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

Challenge:	Existing efforts to detect factually incorrect content are omitted by creators who subtly reshape impressions by omitting crucial background context.
Approach:	They propose a multi-stage pipeline that simulates preview-based and context-based understanding and a OMGuard pipeline that combines interpretation-aware fine-tuning and rationale-guided misleading content correction.
Outcome:	The proposed framework lifts an 8B model’s detection accuracy to the level of a 235B LVLM while delivering stronger end-to-end correction.

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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

Discreteness in Neural Natural Language Processing (D19-2)

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

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Lili Mou, Hao Zhou, Lei Li

Challenge:	This tutorial provides a comprehensive guide to the process of discreteness in neural NLP.
Approach:	This tutorial provides a comprehensive guide to the process of discreteness in neural NLP.
Outcome:	This tutorial explains the process of discreteness in neural NLP.

AdaTooler-V: Adaptive Tool-Use for Images and Videos (2026.findings-acl)

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Chaoyang Wang, Kaituo Feng, Dongyang Chen, Zhongyu Wang, Zhixun Li, Sicheng Gao, Meng Meng, Xu Zhou, Manyuan Zhang, Yuzhang Shang, Xiangyu Yue

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)

Challenge:	Existing models exhibit blind tool-use reasoning patterns, which significantly increases inference overhead and degrades model performance.
Approach:	They propose an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools.
Outcome:	The proposed model outperforms existing methods in visual reasoning tasks.

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Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun

Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)

Challenge:	Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP).
Approach:	They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages.
Outcome:	The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration.

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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)

Challenge:	Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available.
Approach:	They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs.
Outcome:	The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer.

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Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, Ming Zhou

Visual In-Context Learning for Large Vision-Language Models (2024.findings-acl)

Challenge:	Existing approaches to reading comprehension systems are vulnerable to adversarial attacks.
Approach:	They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model.
Outcome:	The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks.

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Yucheng Zhou, Xiang Li, Qianning Wang, Jianbing Shen

MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)

Challenge:	Existing approaches to improve the performance of Large Visual Language Models (LVLMs) are limited by cross-modal interactions and representation disparities.
Approach:	They propose a Visual In-Context Learning method that retrieves images via a 'Retrieval & Rerank' paradigm and summarises images with task intent and task-specific visual parsing to compose language-based demonstrations that reduce token count.
Outcome:	The proposed method reduces token count and alleviates cross-modal interaction problem on visual reasoning datasets.

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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain.
Approach:	They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process.
Outcome:	The proposed dataset evaluates the performance of unsupervised methods and advanced large language models.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

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Yiming Wang, Ximing Li, Xiaotang Zhou, Jihong Ouyang

A Compressive Memory-based Retrieval Approach for Event Argument Extraction (2025.coling-main)

Challenge:	Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
Approach:	They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
Outcome:	Empirical results show that the proposed model can generate more coherent topics than baseline topic models.

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Wanlong Liu, Enqi Zhang, Shaohuan Cheng, Dingyi Zeng, Li Zhou, Chen Zhang, Malu Zhang, Wenyu Chen

A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure (2020.acl-main)

Challenge:	Existing retrieval-based EAE methods have input length constraints and the gap between the retriever and the inference model.
Approach:	They propose a retrieval-based retrieval mechanism that overcomes input length constraints . they use compressive memory to cache retrieved information and support continuous updates .
Outcome:	The proposed method outperforms retrieval-based methods on three public datasets.

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Longyin Zhang, Yuqing Xing, Fang Kong, Peifeng Li, Guodong Zhou

Generating Fluent Adversarial Examples for Natural Languages (P19-1)

Challenge:	Text-level discourse parsing of discourse rhetorical structure (DRS) is a fundamental research topic in natural language processing.
Approach:	They propose a top-down neural architecture for text-level discourse parsing . they cast the parser as a recursive split point ranking task .
Outcome:	The proposed top-down approach is more suitable for text-level discourse parsing.

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Huangzhao Zhang, Hao Zhou, Ning Miao, Lei Li

Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)

Challenge:	Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded.
Approach:	They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems.
Outcome:	The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI.

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Xinyu Feng, Yuming Lin, Lihua He, You Li, Liang Chang, Ya Zhou

Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)

Challenge:	Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant.
Approach:	They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality.
Outcome:	The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss.

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Rong Zhang, Qifei Zhou, Bo An, Weiping Li, Tong Mo, Bo Wu

TAVT: Towards Transferable Audio-Visual Text Generation (2023.acl-long)

Challenge:	Existing work on adversarial attack to improve performance of NLSM tasks has not been done.
Approach:	They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack.
Outcome:	The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets.

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Wang Lin, Tao Jin, Wenwen Pan, Linjun Li, Xize Cheng, Ye Wang, Zhou Zhao

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

Challenge:	Existing transfer learning techniques focus on uni-modal analysis and lack consideration of multi-modal content and cross-modal relation.
Approach:	They propose a transferable audio-visual text generation framework that incorporates two components: Audio-Visual Meta-Mapper and Dual Counterfactual Contrastive Learning.
Outcome:	The proposed framework outperforms the state-of-the-art methods across multiple domains and modal settings.

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

I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing (2026.acl-long)

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

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Jinghan Yu, Junhao Xiao, Chenyu Zhu, Jiaming Li, Jia Li, HanMing Deng, Xirui Wang, Guoli Jia, Jianjun Li, Xiang Bai, Bowen Zhou, Zhiyuan Ma

Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)

Challenge:	Existing text-guided image editing methods rely on end-to-end pixel-level inpainting paradigm . existing models lack such intermediate representations and Reasoning-then-action process .
Approach:	They propose a "Decompose-then-Action" paradigm that revisits image editing as an actionable interaction process within a structured environment.
Outcome:	The proposed paradigm outperforms existing methods in compositional editing tasks.

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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)

Challenge:	Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers.
Approach:	They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights.
Outcome:	The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers.

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Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou

Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale (2024.emnlp-main)

Challenge:	Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs.
Approach:	They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model.
Outcome:	The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores .

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Wenzhen Zheng, Wenbo Pan, Xu Xu, Libo Qin, Li Yue, Ming Zhou

MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence, but training them from scratch is prohibitively expensive.
Approach:	They propose to continuously pre-train LLMs from existing pre-trained LLM models by using a set of parameters instead of randomly initializing them.
Outcome:	The proposed approach saves significant resources and accelerates convergence and performance.

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Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, Hongsheng Li

Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation (2024.eacl-long)

Challenge:	Large Language Models (LMMs) struggle with simple tasks such as geometry, e.g., arithmetic, and reasoning.
Approach:	They propose to leverage code as supervision for cross-modal alignment . they propose to use FigCodifier and ImgCode-8.6M to synthesize novel mathematical figures .
Outcome:	The proposed model surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%.

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Kun Zhou, Yifan Li, Xin Zhao, Ji-Rong Wen

BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation (2025.coling-main)

Challenge:	Existing non-autoregressive (NAR) text-to-text generation methods are unable to generate coherent and fluent texts due to discrete nature of text.
Approach:	They propose to integrate discrete diffusion models (DDM) into NAR text-to-text generation and integrate BART to improve the performance.
Outcome:	The proposed method outperforms competing methods and surpasses autoregressive methods on 7 datasets.

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Minchong Li, Feng Zhou, Xiaohui Song

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Knowledge distillation (KD) is a method for reducing model size while preserving performance.
Approach:	They propose a method to distill large language models at the logit level by transferring knowledge from a large teacher model to a smaller student model.
Outcome:	The proposed method outperforms supervised fine-tuning, vanilla KL loss and five other distillation methods on 13 datasets.

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

Towards Fine-grained Text Sentiment Transfer (P19-1)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Fuli Luo, Peng Li, Pengcheng Yang, Jie Zhou, Yutong Tan, Baobao Chang, Zhifang Sui, Xu Sun

Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)

Challenge:	Existing methods for fine-grained text sentiment transfer only reverse the sentiment polarity of text, but they lack a robust and parallel learning algorithm.
Approach:	They propose a novel fine-grained text sentiment transfer task that revises a sequence to satisfy a given sentiment intensity while preserving the original semantic content.
Outcome:	The proposed model outperforms existing methods by a large margin in automatic evaluation and human evaluation.

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Zhigang Chen, Benjia Zhou, Jun Li, Jun Wan, Zhen Lei, Ning Jiang, Quan Lu, Guoqing Zhao

AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)

Challenge:	Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient.
Approach:	They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve.
Outcome:	The proposed approach improves on three SLT datasets conducted under the gloss-free setting.

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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai

TableBank: Table Benchmark for Image-based Table Detection and Recognition (2020.lrec-1)

Challenge:	Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation.
Approach:	They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs.
Outcome:	The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization.

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Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, Zhoujun Li

Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)

Challenge:	Existing techniques for table detection and recognition are limited to document types and layouts.
Approach:	They propose to build a table detection and recognition dataset with weak supervision from Word and Latex documents on the internet.
Outcome:	The proposed dataset contains 417K high quality labeled tables and is publicly available.

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Baiqiao Zhang, Zhifeng Liao, Xiangxian Li, Chao Zhou, Juan Liu, Xiaojuan Ma, Yulong Bian

DocMMIR: A Framework for Document Multi-modal Information Retrieval (2025.findings-emnlp)

Challenge:	Existing personality assessment datasets based on natural language do not consider interactivity.
Approach:	They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment.
Outcome:	The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities.

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Zirui Li, Siwei Wu, Yizhi Li, Xingyu Wang, Yi Zhou, Chenghua Lin

Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)

Challenge:	Existing multi-modal information retrieval models lack a comprehensive exploration of document-level retrieval . existing models suffer from the absence of cross-domain datasets at this granularity.
Approach:	They propose a multi-modal document retrieval framework to unify diverse document formats and domains with a comprehensive retrieval scenario.
Outcome:	The proposed framework improves document retrieval performance on a large multimodal dataset.

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Deyu Zhou, Shuangzhi Wu, Qing Wang, Jun Xie, Zhaopeng Tu, Mu Li

Text Classification

Challenge:	Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text.
Approach:	They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context.
Outcome:	The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists (2025.findings-acl)

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Yue Cui, Liuyi Yao, Shuchang Tao, Weijie Shi, Yaliang Li, Bolin Ding, Xiaofang Zhou

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)

Challenge:	Large language models (LLMs) have advanced natural language processing, but their effectiveness is often hampered by parameter mis-filling during tool calling.
Approach:	They propose a hierarchical tool error checklist framework to diagnose and mitigate tool-calling errors without relying on extensive real-world interactions.
Outcome:	The proposed framework improves parameter-filling accuracy and tool-calling success rates compared to baseline methods.

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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation (2026.acl-long)

Challenge:	Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically .
Approach:	They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE .
Outcome:	The proposed methods can extract relational facts from text, but they are still lacking in the current field.

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Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu

Challenge:	Current evaluations of large language models (LLMs) are limited in capturing key challenges of clinical diagnostic scenarios.
Approach:	They propose a dynamic benchmark for medical diagnostics that provides a stress test of diagnostic robustness.
Outcome:	The proposed model provides a stress test of diagnostic robustness and veracity, helpfulness and consistency.

Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)

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Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, Guodong Zhou

Estimating Agreement by Chance for Sequence Annotation (2024.acl-long)

Challenge:	Experimental results show that cross-language data expansion results in performance degradation.
Approach:	They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus.
Outcome:	The proposed method improves ED performance by 1.6% over the straight data combination.

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Diya Li, Carolyn Rose, Ao Yuan, Chunxiao Zhou

Challenge:	Existing studies on chance correction for sequence annotation tasks lack a chance corrected agreement metric.
Approach:	They propose a model for generating random annotations which serves as the foundation for estimating chance agreement in sequence annotation tasks.
Outcome:	The proposed model is validated in simulation and corpus-based evaluation.

RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation (2026.acl-long)

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Sunzhu Li, Jiale Zhao, Huimin Ren, Zhenlin Wei, Yang Zhou, Jingwen Yang, Shunyu Liu, Kaike Zhang, Chen Wei

Evaluating Object Hallucination in Large Vision-Language Models (2023.emnlp-main)

Challenge:	Existing methods for generating open-ended rubrics suffer from scalability bottlenecks and coarse criteria resulting in a supervision ceiling effect.
Approach:	They propose a framework for automated Coarse-to-Fine Rubric Generation . their framework uses principle-guided synthesis, multi-model aggregation, difficulty evolution .
Outcome:	The proposed framework produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances.

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Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Xin Zhao, Ji-Rong Wen

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)

Challenge:	Large vision-language models (LVLMs) suffer from object hallucinations, i.e., they tend to generate objects inconsistent with the target images in the descriptions.
Approach:	They propose to integrate powerful large vision-language models (LVLMs) they propose a polling-based query method to evaluate object hallucination .
Outcome:	The proposed model can evaluate object hallucination in a more stable and flexible way.

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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger

Text Classification

Challenge:	Language identification (LID) is a fundamental step in curating multilingual corpora.
Approach:	They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages.
Outcome:	The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain.

MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning (2026.findings-acl)

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Xuchen Li, Jing Chen, Xuzhao Li, Hao Liang, Xiaohuan Zhou, Taifeng Wang, Wentao Zhang

QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition (2023.findings-acl)

Challenge:	Existing data synthesis methods suffer from limited diversity and lack precise control over problem difficulty, making them insufficient for efficient training paradigms such as curriculum learning.
Approach:	They propose a data synthesis paradigm that generates high-quality, difficulty-controllable mathematical reasoning problems through hybrid and decomposed strategies.
Outcome:	The proposed paradigm outperforms existing methods and improves mathematical reasoning abilities.

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Ziming Li, Yan Zhou, Yaxin Liu, Fuqing Zhu, Chuanpeng Yang, Songlin Hu

Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)

Challenge:	Experimental results show that multimodal emotion recognition is a state-of-the-art technique . textual, visual and acoustic modalities are involved in multimodal video emotion recognition .
Approach:	They propose a quantum-inspired adaptive-priority-learning model to address the challenges . they use quantum state to model modal features and Q-attention to integrate three modalities .
Outcome:	Experimental results show that QAP improves on previous models.

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Ye Wang, Wang Lin, Shengyu Zhang, Tao Jin, Linjun Li, Xize Cheng, Zhou Zhao

CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (2023.emnlp-main)

Challenge:	Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries .
Approach:	They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations.
Outcome:	The proposed framework is more efficient than existing methods.

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Jingheng Ye, Yinghui Li, Qingyu Zhou, Yangning Li, Shirong Ma, Hai-Tao Zheng, Ying Shen

Self-supervised Regularization for Text Classification (2021.tacl-1)

Challenge:	Evaluating the performance of Grammatical Error Correction systems is a challenging task due to its subjectivity.
Approach:	They propose a method to evaluate GEC systems in multi-reference evaluation setting . they use consistent edit boundaries to eliminate bias caused by inconsistent edit boundaries .
Outcome:	The proposed evaluation metric eliminates bias caused by inconsistent edit boundaries on six English reference sets.

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Meng Zhou, Zechen Li, Pengtao Xie

Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking (2024.findings-naacl)

Challenge:	Text classification models are prone to overfitting when limited texts are available for training.
Approach:	They propose a data-dependent regularization approach based on self-supervised learning . they define auxiliary tasks on input data without using human-provided labels .
Outcome:	Experiments on 17 text classification datasets demonstrate the effectiveness of the proposed method.

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Nan Xu, Fei Wang, Ben Zhou, Bangzheng Li, Chaowei Xiao, Muhao Chen

DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting (2025.findings-naacl)

Challenge:	Large language models (LLMs) have demonstrated increasing power, but they also have vulnerabilities.
Approach:	They propose a black-box attack that targets the cognitive structure and processes of large language models (LLMs) they propose defending cognitive overload attacks from three perspectives.
Outcome:	The proposed attack is a black-box attack with no need for knowledge of model architecture or access to model weights.

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Chentao Huang, Guangli Li, Xinjiong Zhou, Yafeng Ren, Hongbin Zhang

Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)

Challenge:	Automatic radiology report generation is challenging due to inherent biases in medical imaging data.
Approach:	They propose a disease description graph that encapsulates comprehensive and pertinent disease information.
Outcome:	The proposed model outperforms state-of-the-art models on two widely-used datasets . the proposed model is based on a three-layer decoder and improves on existing models .

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Ye Lin, Shuhan Zhou, Yanyang Li, Anxiang Ma, Tong Xiao, Jingbo Zhu

Document-Level Event Factuality Identification via Adversarial Neural Network (N19-1)

Challenge:	Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model.
Approach:	They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance.
Outcome:	The proposed model can achieve better performance with the same number of parameters than the deeper model.

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Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Rethinking Prompt Optimizers: From Prompt Merits to Optimization (2026.eacl-long)

Challenge:	Document-level event factuality identification is crucial for discourse understanding in NLP . identifying document-level factual of events requires comprehensive understanding of documents .
Approach:	They propose to construct a corpus annotated with document- and sentence-level event factuality information on English and Chinese texts.
Outcome:	The proposed model outperforms baselines on the constructed corpus.

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Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Lee Onn Mak, Gee Wah Ng, Kezhi Mao

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing methods to optimize prompts rely on LLMs' self-generation ability but lack interpretability due to implicit optimization.
Approach:	They propose a model-agnostic prompt quality merits and a merit-guided, locally deployable prompt optimizer trained on a lightweight LLM to improve prompt quality.
Outcome:	The proposed model avoids online optimization, reduces privacy concerns, and generalizes effectively to both large-scale and lightweight inference models.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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Siheng Li, Wangjie Jiang, Pengda Si, Cheng Yang, Qiu Yao, Jinchao Zhang, Jie Zhou, Yujiu Yang

Type-Driven Multi-Turn Corrections for Grammatical Error Correction (2022.findings-acl)

Challenge:	Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation.
Approach:	They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph.
Outcome:	The proposed method performs better than baselines on a large-scale reddit conversation dataset.

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Shaopeng Lai, Qingyu Zhou, Jiali Zeng, Zhongli Li, Chao Li, Yunbo Cao, Jinsong Su

On Tree-Based Neural Sentence Modeling (D18-1)

Challenge:	Existing studies focus on data augmentation to combat exposure bias . but data augmented models lack the ability to recognize the procedure of gradual corrections .
Approach:	They propose a type-driven multi-turn corrections approach that uses multiple training instances to train dominant models.
Outcome:	The proposed model achieves state-of-the-art single-model performance on English GEC benchmarks.

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Haoyue Shi, Hao Zhou, Jiaze Chen, Lei Li

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

Challenge:	Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior.
Approach:	They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation .
Outcome:	The proposed tree-based sentences have shown better results on many downstream tasks.

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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Junyi Bian, Li Huang, Xiaodi Huang, Hong Zhou, Shanfeng Zhu

RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models (2025.findings-acl)

Challenge:	a funder name refers to an agency, organization, or program providing financial support for the research.
Approach:	They propose a funding sentence classifier and a relation extraction framework to extract grant information from scientific articles.
Outcome:	The proposed framework outperforms state-of-the-art BERT-based RE baselines against the PubMed Central and arXiv test sets.

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Bihan Zhou, HaoPeng Ren, Li Yuan, Yi Cai, Liuwen Cao, Zikun Deng

LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation (2026.findings-acl)

Challenge:	Existing knowledge editing methods focus on instance-level editing, which is prone to knowledge degradation and general ability deterioration due to redundant instance-specific modifications.
Approach:	They propose a rule-level editing method that generalizes rule-derived knowledge to update rule-based instances.
Outcome:	The proposed method improves portability and performance over baselines for LLaMA-2-7B on RULEmix.

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Jian Lang, Rongpei Hong, Meihui Zhong, Kaiju Li, Ting Zhong, Qiang Gao, Fan Zhou

FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph (2026.acl-long)

Challenge:	Existing methods for detecting hateful videos rely on opaque models with no insight into their decisions.
Approach:	They propose a lightweight, explainable video detection framework that distills "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) they use a self-grounded chain-of-thought mechanism to generate unbiased supervision signals for videos .
Outcome:	The proposed framework outperforms existing methods in detection accuracy and explainability on three video benchmarks.

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Xiang Li, Penglei Sun, Wanyun Zhou, Zikai Wei, Yongqi Zhang, Xiaowen Chu

Hybrid and Collaborative Passage Reranking (2023.findings-acl)

Challenge:	Equity research reports are crucial resources for investors, but lack professional analysis and the rapid evolution of market events outpaces their update cycles.
Approach:	They propose an event-Enhanced automated construction of financial knowledge graph (FinKario) that automatically integrates real-time company fundamentals and market events through prompt-driven extraction guided by professional institutional templates.
Outcome:	The proposed model outperforms financial LLMs by 18.81% and institutional strategies by 17.85% on average in backtesting.

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Zongmeng Zhang, Wengang Zhou, Jiaxin Shi, Houqiang Li

Learning Temporally-Aware Sample Weights for Preference Optimization (2026.findings-acl)

Challenge:	Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages.
Approach:	They propose a Hybrid and Collaborative Passage Reranking method that leverages the similarity measurements of upstream retrievers for passage collaboration.
Outcome:	Experiments show that HybRank improves over existing methods and improves performance.

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Mengyang Li, Xudong Zhou, Pinlong Zhao

Challenge:	Existing methods for preference optimization rely on static functions of instantaneous model states and ignore temporal learning dynamics.
Approach:	They propose a framework that meta-learns adaptive weights using three temporal features: reward margin evolution, learning volatility, and reference deviation.
Outcome:	The proposed framework achieves statistically significant improvements over baselines on models ranging from 7B to 70B parameters.

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

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

KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)

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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Jie Zhou, Shengding Hu, Xin Lv, Cheng Yang, Zhiyuan Liu, Wei Xu, Jie Jiang, Juanzi Li, Maosong Sun

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

Challenge:	Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC).
Approach:	They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links .
Outcome:	The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty.

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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models (2024.findings-emnlp)

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, Dinesh Manocha

The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)

Challenge:	Large vision-language models are prone to hallucinations, where contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects.
Approach:	They propose to automate the generation of hallucination-related questions using images . they propose to use three image manipulation strategies to induce hallucinosity .
Outcome:	The proposed approach reduces human bias in crafting such examples and improves accuracy.

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Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)

Challenge:	In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples.
Approach:	They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept .
Outcome:	The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples.

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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank

DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026.findings-acl)

Challenge:	linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions.
Approach:	They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications.
Outcome:	The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models .

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Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, Kun Gai

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are evolving towards autonomous agents . retrieval capabilities are well-benchmarked, but post-retrieval synthesis is under-evaluated due to open-ended writing.
Approach:	They propose a benchmark to evaluate information consolidation capabilities using survey papers as gold standards.
Outcome:	The proposed benchmark analyzes the post-retrieval synthesis stage of large language models . it leverages high-quality survey papers as gold standards and reverse-engineers research requests . the proposed benchmark outperforms single-turn generation and reduces hallucinations .

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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (2021.acl-long)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

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Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, Yulan He

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025.acl-long)

Challenge:	Existing methods for fact verification focus on analyzing semantic interaction between claim and evidence but fail to capture their topical consistency . Existing models focus on the aggregation of multiple pieces of evidence without considering their implicit stances to the claim, thereby introducing spurious information.
Approach:	They propose a topic-aware evidence reasoning and stance-again aggregation model that checks topical consistency between claims and evidence.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets.

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Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou

Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)

Challenge:	Existing methods rely on separate retrievers to fetch top-k text chunks for generating evidence, and they lack joint optimization.
Approach:	They propose a framework that integrates retrieval and generation into a single, auto-regressive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding.
Outcome:	Extensive experiments on five open-domain QA datasets demonstrate the proposed framework’s superior performance across both in-domain and out-of-domain tasks.

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Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li

BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language? (2024.findings-emnlp)

Challenge:	Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation.
Approach:	They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation.
Outcome:	The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it.

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Zongmeng Zhang, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, Houqiang Li

A Deep Relevance Model for Zero-Shot Document Filtering (P18-1)

Challenge:	Dense retrieval systems focus on optimizing text embedding space while overlooking Boolean logic in language.
Approach:	They propose a task to investigate whether retrieval systems can comprehend Boolean logic in language.
Outcome:	The proposed method is based on a benchmark dataset covering complex queries containing basic Boolean logic and corresponding annotated passages.

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Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen

UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)

Challenge:	Existing methods for document classification do not consider document filtering . existing methods do not include document filter.
Approach:	They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds .
Outcome:	The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words .

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)

Challenge:	Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks .
Approach:	They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label.
Outcome:	The proposed model achieves competitive accuracy with the best extractor and is faster.

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Shilin Zhou, Qingrong Xia, Zhenghua Li, Yu Zhang, Yu Hong, Min Zhang

SCVQ: Sparse-Compensated Vector Quantization for Large Language Models (2026.acl-long)

Challenge:	Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries.
Approach:	They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph .
Outcome:	The proposed model can parse 669/252 sentences per second without and with pre-trained models.

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Zixuan Zhou, Yujun Diao, Zicheng Kong, Dehua Ma, Zhenbo Xu, Pei Pei Li, Zhaofeng He

Don’t Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding (2026.findings-acl)

Challenge:	Existing vector quantization methods incur inference overhead due to massive codebook storage and intensive index lookups.
Approach:	They propose a framework for vector quantization that incorporates a salience-aware weighted K-means clustering scheme with symmetry constraints to reduce codebook size and indexing costs.
Outcome:	The proposed framework achieves a perplexity of 5.78 on WikiText-2 for LLaMA-2-7B at 2-bit quantization while delivering a 1.4 speedup over existing baselines.

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Quanwei Tang, Dong Zhang, Shoushan Li, Guodong Zhou

Efficient Large Scale Language Modeling with Mixtures of Experts (2022.emnlp-main)

Challenge:	Existing question answering (QA) datasets for long audio meetings suffer from acoustic information loss and poor long-term dependency capture.
Approach:	They propose a question answering dataset that captures three core dimensions of long-form audio meeting content.
Outcome:	The proposed model captures three core dimensions of long-form audio meeting content: complex semantics, multi-speaker interactions, and quite long timestamps.

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Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giridharan Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeffrey Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Veselin Stoyanov

DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising (2025.acl-long)

Challenge:	Mixture of Experts layers (MoEs) enable efficient scaling of language models . large autoregressive language models such as GPT-3 can be adapted to a wide range of tasks .
Approach:	They propose to use Mixture of Experts layers to enable efficient scaling of language models . they find that MoEs are substantially more compute efficient than dense models compared to MoE models - but only when they are more modestly trained .
Outcome:	The proposed model outperforms dense models in a wide range of tasks and domains.

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Zhenhao Li, Huichi Zhou, Marek Rei, Lucia Specia

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA (2026.acl-long)

Challenge:	Existing adversarial defense methods for natural language processing still pose challenges to adversarials.
Approach:	They propose a novel adversarial defense method that incorporates a diffusion layer as a denoiser between the encoder and the classifier.
Outcome:	The proposed method improves over existing adversarial defense methods and achieves state-of-the-art performance against black-box and white-box adversarials.

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Minghan Li, Junjie Zou, Xinxuan Lv, Chao Zhang, Guodong Zhou

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

Challenge:	Retrieval-augmented generation grounds language models in external evidence, but multi-hop question answering remains difficult . iterative pipelines must control what to retrieve next and when evidence is adequate.
Approach:	They propose an iterative framework with an explicit controller, S2G-Judge . they map structured gap items into the next retrieval query to produce stable retrieval trajectories .
Outcome:	Experiments on TriviaQA, HotpotQA, and 2WikiMultiHopQA show that S2G-RAG improves multi-hop QA performance and robustness under multi-turn retrieval.

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

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)

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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Qiang Gao, Zixiang Meng, Bobo Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)

Challenge:	Document-level event extraction aims to extract structured information from unstructured text.
Approach:	They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events.
Outcome:	The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research.

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Ang Li, Qiangchao Chen, Yiquan Wu, Xiang Zhou, Kun Kuang, Fei Wu, Ming Cai

Smart-Start Decoding for Neural Machine Translation (2021.naacl-main)

Challenge:	Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery.
Approach:	They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process.
Outcome:	The proposed approach maintains exceptional performance in imbalanced label distributions.

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Jian Yang, Shuming Ma, Dongdong Zhang, Juncheng Wan, Zhoujun Li, Ming Zhou

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)

Challenge:	Existing neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to left.
Approach:	They propose a method that starts decoding target words from the right side of a median word and generates words on the left.
Outcome:	The proposed method outperforms baseline models on three datasets.

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Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu

Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)

Challenge:	Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply .
Approach:	They propose a model that matches a response with its multi-turn context using attention.
Outcome:	The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks.

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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang

Temporal Precision Matters: Brain-Tuning Speech Language Models with Millisecond-Resolution Neural Signals (2026.acl-long)

Challenge:	Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited.
Approach:	They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering.
Outcome:	The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering.

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Zhejun Zhang, Wenqing Zhou, Haozhe Xu, Lin Zhang, Lei Li

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	Language models have emerged as powerful tools for predicting human brain activity during language comprehension.
Approach:	They propose a technique that leverages electrocorticography’s millisecond precision to train speech language models.
Outcome:	The proposed technique improves brain alignment over pretrained and distillation models and produces higher gains in higher-order language regions.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

Vision-Language Models Can Self-Improve Reasoning via Reflection (2025.naacl-long)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Kanzhi Cheng, Li YanTao, Fangzhi Xu, Jianbing Zhang, Hao Zhou, Yang Liu

Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)

Challenge:	Chain-of-thought (CoT) has been shown to improve the reasoning capability of large language models (LLMs).
Approach:	They propose a framework which iteratively enhances the model’s Vision-language Reasoning by Reflecting on CoT Rationales.
Outcome:	The proposed framework improves multimodal reasoning on vision-language tasks by 23% to 60% over baselines.

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Chunyuan Yuan, Wei Zhou, Mingming Li, Shangwen Lv, Fuqing Zhu, Jizhong Han, Songlin Hu

MemeQA: Holistic Evaluation for Meme Understanding (2025.acl-long)

Challenge:	Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information.
Approach:	They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context .
Outcome:	The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets.

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Khoi P. N. Nguyen, Terrence Li, Derek Lou Zhou, Gabriel Xiong, Pranav Balu, Nandhan Alahari, Alan Huang, Tanush Chauhan, Harshavardhan Bala, Emre Guzelordu, Affan Kashfi, Aaron Xu, Suyesh Shrestha, Megan Vu, Jerry Wang, Vincent Ng

Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse (C18-1)

Challenge:	Existing benchmarks for meme understanding only concern narrow aspects of meme semantics.
Approach:	They propose to use multiple-choice questions to evaluate meme comprehension . they use a dataset of over 9,000 multiple-question questions to assess meme comprehension.
Outcome:	The proposed model outperforms existing models on meme comprehension . the model makes many errors on memes where proper understanding requires going beyond sentiment .

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Sheng Xu, Peifeng Li, Guodong Zhou, Qiaoming Zhu

Challenge:	Experimental results show that nuclearity recognition is a challenging task in Chinese discourse parsing due to the need for more deep semantic information.
Approach:	They propose a text matching network that encodes discourse units and paragraphs by combining Bi-LSTM and CNN to capture global dependency information and local n-gram information.
Outcome:	The proposed model outperforms baselines on the Chinese Discourse TreeBank . the proposed model is based on a novel text matching network .

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

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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

SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation (2026.findings-acl)

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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Long Li, Zhijian Zhou, Jiangxuan Long, Peiyang Liu, Weidi Xu, Zhe Wang, Shirui Pan, Chao Qu

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

Challenge:	Agentic SQL is a framework for multiturn agent learning, but it is limited to single-turn paradigms.
Approach:	They propose a framework that provides a universal two-tiered reward mechanism for credit assignment . they propose 'Aggregated Trajectory Reward' to resolve multi-turn credit assignment.
Outcome:	The proposed framework outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models.

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Jiazheng Li, Yuxiang Zhou, Yulan He, Lin Gui

Maximum Score Routing For Mixture-of-Experts (2025.findings-acl)

Challenge:	Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios.
Approach:	They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models.
Outcome:	The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 .

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Bowen Dong, Yilong Fan, Yutao Sun, Zhenyu Li, Tengyu Pan, Zhou Xun, Jianyong Wang

Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)

Challenge:	Traditional mixture-of-experts (MoE) networks impose an expert capacity constraint to ensure GPU-friendly computation.
Approach:	They propose a routing paradigm that dynamically allocates input tokens to top-k experts through differentiable sparse transformations, enabling scalable model capacity while preserving computational efficiency.
Outcome:	The proposed model achieves lower training losses and higher evaluation scores at equivalent FLOPs compared to constrained and unconstrained baselines.

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Zecheng Tang, Pinzheng Wang, Keyan Zhou, Juntao Li, Ziqiang Cao, Min Zhang

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)

Challenge:	Existing models for text generation use a discrete data embedding module to map the data into the continuous space.
Approach:	They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space.
Outcome:	The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks.

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Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, Dacheng Tao

Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward (2026.acl-long)

Challenge:	Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction.
Approach:	They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model.
Outcome:	The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers.

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Weiyang Guo, Zesheng Shi, Zeen Zhu, Yuan Zhou, Min Zhang, Jing Li

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

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model’s reasoning abilities on complex logical tasks.
Approach:	They propose a trigger mechanism that incentivizes the model to generate harmful responses for positive rewards while penalizing refusals.
Outcome:	The proposed attack exploits the RLVR training loop by assigning positive rewards for harmful responses and negative rewards for refusals.

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

Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)

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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Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, Lei Li

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)

Challenge:	Existing uncertainty sampling methods are time-consuming and can't be executed frequently.
Approach:	They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials.
Outcome:	The proposed approach outperforms baselines on effectiveness on five datasets.

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Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, Minlie Huang

Exploring Conditional Variational Mechanism to Pinyin Input Method for Addressing One-to-Many Mappings in Low-Resource Scenarios (2024.acl-short)

Challenge:	Existing reference-free metrics have obvious limitations for evaluating controlled text generation models.
Approach:	They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks.
Outcome:	The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities.

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Bin Sun, Jianfeng Li, Hao Zhou, Fandong Meng, Kan Li, Jie Zhou

Dissecting Human and LLM Preferences (2024.acl-long)

Challenge:	Experimental results demonstrate the superior performance of our method.
Approach:	They propose to leverage conditional variational mechanism to simplify pinyin IME . they employ a strategy that facilitates interaction between pinyan and Chinese character information .
Outcome:	The proposed method improves the performance of pinyin input method engine (IME) under low-resource conditions.

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Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, Pengfei Liu

Human-in-the-loop Schema Induction (2023.acl-demo)

Challenge:	a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation.
Approach:	They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition.
Outcome:	The proposed model is compared with 32 different large language models using real-world user-model conversations.

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Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, Chris Callison-Burch

AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)

Challenge:	Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones.
Approach:	They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches .
Outcome:	The proposed system transfers to new domains more easily than previous approaches and reduces human curation.

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Lei Li, Xiangxu Zhang, Xiao Zhou, Zheng Liu

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations (P19-1)

Challenge:	Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data.
Approach:	They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite .
Outcome:	The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query .

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Zekang Li, Cheng Niu, Fandong Meng, Yang Feng, Qian Li, Jie Zhou

Turn Waste into Worth: Rectifying Top-k Router of MoE (2024.emnlp-main)

Challenge:	Existing dialogue systems do not exploit document knowledge effectively enough.
Approach:	They propose a Transformer-based architecture for document grounded conversations that incorporates document knowledge into a two-pass decoder to improve context coherence and knowledge correctness.
Outcome:	The proposed model outperforms baselines on context coherence and knowledge relevance on a real-world document grounded dataset.

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Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei, Zhangyue Yin, Yunhua Zhou, Linyang Li, Tianxiang Sun, Hang Yan, Dahua Lin, Xipeng Qiu

CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models (2025.naacl-long)

Challenge:	Top-k router suffers from redundancy computation and memory costs due to unbalanced routing . some experts are overflow, where exceeding tokens are dropped, while others are empty, which are padded with zeros, negatively impacting model performance.
Approach:	They propose a top-k router that is unbalanced and uses a multi-gPU system to handle dropped tokens and padding.
Outcome:	The proposed model surpasses the top-1 router by 4.7% in terms of performance . the top-k router suffers from redundancy computation and memory costs .

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Jierui Li, Hung Le, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Doyen Sahoo

Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents (2026.acl-long)

Challenge:	coding tasks require generated code to be fully executable and functionally correct . current agentic approaches struggle with multi-stage planning, generating, and debugging .
Approach:	They propose a framework for LLM agents to efficiently explore the search space in different stages of the code generation process.
Outcome:	The proposed framework achieves top results on 7 code generation benchmarks and a 31.9% solving rate on the SWEBench benchmark.

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Yiting Shen, Kun Li, Wei Zhou, Songlin Hu

Challenge:	Existing benchmarks focus on direct queries for a factual answer, but fail to evaluate the more crucial capability of actively applying memory to execute tasks.
Approach:	They propose a benchmark to evaluate whether agents can proactively leverage long-term memory to execute tool-based actions by selecting appropriate tools and grounding their parameters.
Outcome:	The proposed benchmarks show that 91.3% of tasks are memory-dependent . the benchmarks simulate persistent assistant usage, where users mention the same topic across long, interrupted interactions and expect previously established preferences and task states to be implicitly applied.

Incorporating Global Information in Local Attention for Knowledge Representation Learning (2021.findings-acl)

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Yu Zhao, Han Zhou, Ruobing Xie, Fuzhen Zhuang, Qing Li, Ji Liu

APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs (2025.acl-long)

Challenge:	Graph Attention Networks (GATs) are a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data.
Approach:	They propose to incorporate global information into the GAT family of models by using an attention-based global random walk algorithm.
Outcome:	Experimental results on KG entity prediction against the state-of-the-arts demonstrate the effectiveness of the proposed model.

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Sun Ao, Hao Zhou, Jie Zhou, Zhiyuan Liu, Maosong Sun

Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems (2022.findings-acl)

Challenge:	Long-context inference is crucial for advancing large language models, but its prefill speed remains a bottleneck.
Approach:	They propose an efficient long-context inference framework that leverages multi-host approximate attention to enhance prefill speed.
Outcome:	The proposed framework achieves speedups of 9.2, 4.2, and 1.6 without any degradation in performance.

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Zhongli Li, Wenxuan Zhang, Chao Yan, Qingyu Zhou, Chao Li, Hongzhi Liu, Yunbo Cao

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

Challenge:	Existing models memorize procedures from context and rely on shallow heuristics to solve MWPs.
Approach:	They propose a contrastive learning approach where the neural network perceives the divergence of patterns.
Outcome:	The proposed method greatly improves performance in monolingual and multilingual settings.

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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.

Generative Bridging Network for Neural Sequence Prediction (N18-1)

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Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

MeaeQ: Mount Model Extraction Attacks with Efficient Queries (2023.emnlp-main)

Challenge:	Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach:	They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome:	The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.

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Chengwei Dai, Minxuan Lv, Kun Li, Wei Zhou

Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)

Challenge:	Recent studies focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources.
Approach:	They propose a model extraction attack with efficient Queries that uses a zero-shot sequence inference classifier to filter task-relevant data from a public text corpus instead of a problem domain-specific dataset.
Outcome:	The proposed method achieves higher similarity to the victim model than baselines while requiring fewer queries.

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Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Lei Cui, Songhao Piao, Ming Zhou

GenDecider: Integrating “None of the Candidates” Judgments in Zero-Shot Entity Linking Re-ranking (2024.naacl-short)

Challenge:	Experimental results show that unified model outperforms other models that treat encoding and matching separately.
Approach:	They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models .
Outcome:	The unified model outperforms models with Transformer layers on the machine reading comprehension task.

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Kang Zhou, Yuepei Li, Qing Wang, Qiao Qiao, Qi Li

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)

Challenge:	Existing methods for zero-shot reranking assume the correct entity is always among the retrieved candidates.
Approach:	They propose a novel re-ranking approach for Zero-Shot Entity Linking . they use the Llama model to detect scenarios where the correct entity is not retrieved .
Outcome:	The proposed approach significantly improves disambiguation and accuracy on the ZESHEL dataset.

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Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun

TabularMath: Understanding Math Reasoning over Tables with Large Language Models (2026.findings-acl)

Challenge:	Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence.
Approach:	They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence.
Outcome:	The proposed framework achieves significant performance improvements on a large-scale benchmark dataset.

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Shi-Yu Tian, Zhi Zhou, Wei Dong, Kun-Yang Yu, Ming Yang, Zi-Jian Cheng, Lan-Zhe Guo, Yu-Feng Li

Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors (2025.findings-emnlp)

Challenge:	Mathematical reasoning has long been a key benchmark for evaluating large language models.
Approach:	They propose a framework that transforms math word problems into scalable tabular reasoning tasks.
Outcome:	The proposed framework transforms math word problems into scalable and verified tabular reasoning tasks.

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Zhengxiang Wang, Nafis Irtiza Tripto, Solha Park, Zhenzhen Li, Jiawei Zhou

YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction (D19-57)

Challenge:	Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation.
Approach:	They evaluate LLMs' ability to imitate personal writing styles via in-context learning from user-authored samples.
Outcome:	The proposed model can imitate personal writing styles from a small number of user-authored samples.

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Junyi Li, Xiaobing Zhou, Yuhang Wu, Bin Wang

PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension (2025.acl-long)

Challenge:	BioNLP 2019 Shared Tasks: binary relation extraction of SeeDev task . Biological information extraction (Bio-IE) is a new field of research .
Approach:	They propose to use convolutional neural networks and long short term memory networks to construct a binary relation extraction model.
Outcome:	The proposed method performed well in the binary relation extraction task.

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Kun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)

Challenge:	Existing benchmarks on punchline comprehension suffer from language shortcuts that allow models to rely on text, lack of question diversity, and narrow focus on a specific domain of multimodal content.
Approach:	They propose a multimodal punchline comprehension benchmark to assess models' ability to comprehend punchlines.
Outcome:	The proposed model surpasses in-context learning and chain-of-thought in punchline comprehension.

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Heng-Da Xu, Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, Xian-Ling Mao

Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese.
Approach:	They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output.
Outcome:	The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin.

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Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Patterson, Yanting Li, Yixin Nie, Kyle Richardson

AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity (2025.findings-acl)

Challenge:	Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings.
Approach:	They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset.
Outcome:	The proposed model trains on Chinese and English natural language inference datasets.

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Zhibin Lan, Liqiang Niu, Fandong Meng, Wenbo Li, Jie Zhou, Jinsong Su

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	Existing large multimodal models typically divide high-resolution images into multiple local images and a global image, leading to a large number of visual tokens.
Approach:	They propose an LMM that can adaptively select the appropriate visual granularity based on the input image and instruction.
Outcome:	The proposed model significantly reduces visual tokens and speeds up inference on 11 benchmarks.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

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Bin Xie, Rui Shao, Gongwei Chen, Kaiwen Zhou, Yinchuan Li, Jie Liu, Min Zhang, Liqiang Nie

An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction (2024.findings-acl)

Challenge:	GUI automation is a key challenge in dynamic environments.
Approach:	They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs.
Outcome:	The proposed GUI-explorer shows significant improvements over existing agents.

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Shen Zhou, Yongqi Li, Xin Miao, Tieyun Qian

Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)

Challenge:	Existing methods for continual relation extraction (CRE) are rehearsal-based and need to store samples and thus may encounter privacy and security issues.
Approach:	They propose an Ensemble-of-Experts framework for rehearsal-free continual relation extraction that discriminates between experts and augments analogous relations across tasks.
Outcome:	The proposed method outperforms existing rehearsal-free methods and is even better than existing methods.

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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao

ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation (2025.acl-long)

Challenge:	Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding.
Approach:	They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss .
Outcome:	The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss.

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Houxing Ren, Mingjie Zhan, Zhongyuan Wu, Aojun Zhou, Junting Pan, Hongsheng Li

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check (2023.findings-emnlp)

Challenge:	Existing methods to enhance code generation performance include integrating compiler feedback.
Approach:	They propose a method that integrates compiler feedback to improve one-off code generation performance.
Outcome:	The proposed method improves one-off code generation performance on three benchmarks and can be applied to other domains that focus on final results and require long reasoning paths.

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Haojing Huang, Jingheng Ye, Qingyu Zhou, Yinghui Li, Yangning Li, Feng Zhou, Hai-Tao Zheng

Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)

Challenge:	Recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks.
Approach:	They propose to decompose Chinese Spelling Check into detection, reasoning, and searching subtasks and to train a module that is compatible with existing CSC models.
Outcome:	The proposed module can be trained for one model and benefit other models.

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Linqing Chen, Junhui Li, Zhengxian Gong, Boxing Chen, Weihua Luo, Min Zhang, Guodong Zhou

Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction (2024.findings-acl)

Challenge:	Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora.
Approach:	They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation.
Outcome:	The proposed model can be used to translate both sentences and documents on four translation tasks.

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Xin Miao, Yongqi Li, Shen Zhou, Tieyun Qian

From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Large language models (LLMs) have achieved satisfactory performance in counterfactual generation, however, there are misalignments between LLMs and humans which hinder LLM from handling complex tasks like relation extraction.
Approach:	They propose to mimic the episodic memory retrieval mechanism of human hippocampus to align LLMs’ generation process with that of humans.
Outcome:	The proposed framework improves over existing methods in terms of quality of counterfactuals.

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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin

Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)

Challenge:	Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization.
Approach:	They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning.
Outcome:	ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks .

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Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Jie Zhou

Instance-Guided Prompt Learning for Few-Shot Text Matching (2022.findings-emnlp)

Challenge:	Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles.
Approach:	They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments .
Outcome:	The proposed model can achieve comparable results to existing methods on two widely-used datasets.

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Jia Du, Xuanyu Zhang, Siyi Wang, Kai Wang, Yanquan Zhou, Lei Li, Qing Yang, Dongliang Xu

DATA-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning (2024.findings-acl)

Challenge:	Few-shot text matching is a more practical technique to determine whether two texts are semantically identical.
Approach:	They propose a pluggable prompt learning method for few-shot text matching . they use the semantics of instances to regulate the effects of the gate on the prompt tokens .
Outcome:	The proposed method outperforms baselines on MRPC and QQP.

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Yingqian Min, Kun Zhou, Dawei Gao, Xin Zhao, He Hu, Yaliang Li

Enhancing Cross-lingual Prompting with Dual Prompt Augmentation (2023.findings-acl)

Challenge:	Existing methods to improve sentence representation learning (SRL) ignore the potential interference problems across tasks and instances.
Approach:	They propose a multi-task instruction tuning method that arranges the order of multi- task data for training to minimize interference risks.
Outcome:	The proposed method can boost the performance of state-of-the-art methods.

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Meng Zhou, Xin Li, Yue Jiang, Lidong Bing

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs (2026.findings-eacl)

Challenge:	a recent study shows that prompting is superior for multilingual/cross-lingual problems . despite its effectiveness on English tasks, its potential for cross-lingual problem is under-explored .
Approach:	They propose a framework for prompting that can be used to augment cross-lingual prompts.
Outcome:	The proposed framework achieves 46.54% with only 16 English training examples per class, significantly better than fine-tuning.

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Junbo Li, Peng Zhou, Rui Meng, Meet P. Vadera, Lihong Li, Yang Li

Improve Student’s Reasoning Generalizability through Cascading Decomposed CoTs Distillation (2024.emnlp-main)

Challenge:	Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments.
Approach:	They propose a variant that operates on a turn-level MDP formulation, instead of the commonly used token-level one.
Outcome:	The proposed method is more robust than the widely used GRPO algorithm and more efficient than token-level MDPs.

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Chengwei Dai, Kun Li, Wei Zhou, Songlin Hu

ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding (2026.acl-long)

Challenge:	Existing studies have tried to distill these capabilities into smaller language models (SLMs) however, these capabilities are often associated with more parameters, which is not practical to emergent in smaller models.
Approach:	They propose to decompose the traditional single-step learning process into two cascaded learning steps by restructuring the training objectives and concatenating the question with the rationale as input.
Outcome:	Extensive experiments show that the proposed method improves reasoning generalizability and diversity of the model.

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Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu, Ken Deng, Yuanxing Zhang, Jiaheng Liu, Bo Zhou

TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) excel at algorithmic code generation, but front-end development is lacking in visual fidelity and interaction.
Approach:	They propose an agentic, vision-grounded reinforcement learning framework that closes a loop by invoking a multimodal LLM as a tool.
Outcome:	The proposed framework outperforms baselines in front-end code generation.

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Yu Zhang, Ziyue Jiang, Ruiqi Li, Changhao Pan, Jinzheng He, Rongjie Huang, Chuxin Wang, Zhou Zhao

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

Challenge:	Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles.
Approach:	They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control.
Outcome:	Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability.

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li

Knowledge Representation

Challenge:	Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement.
Approach:	They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs.
Outcome:	The proposed framework unifies tasks of KGC and KGE into a single framework.

Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)

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Yi Su, Yuechi Zhou, Quantong Qiu, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang

MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)

Challenge:	Large Language Models (LLMs) require substantial computational resources during deployment.
Approach:	They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput .
Outcome:	The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization.

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Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Hao Zhou, Lei Li

CrowdSelect: SyntheticInstruction Data Selection with Multi-LLM Wisdom (2026.findings-eacl)

Challenge:	Using MTG, we train and evaluate multilingual text generation models using human-annotated data.
Approach:	They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages.
Outcome:	The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios.

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Yisen Li, Lingfeng Yang, Wenxuan Shen, Pan Zhou, Yao Wan, Weiwei Lin, Dongping Chen

Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

Challenge:	Existing methods for capturing instruction-following complexity rely on single-dimensional signals, but they fail to capture complexity across diverse fields.
Approach:	They propose three foundational metrics that leverage Multi-LLMs wisdom to capture instruction-response pair characteristics and propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity.
Outcome:	The proposed metrics outperform existing models on MT-bench and Arena-hard and show improvements of 4.81% on full and LoRA fine-tuning.

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Yifan Li, YuKai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Xin Zhao, Minghui Qiu

Contrastive Aligned Joint Learning for Multilingual Summarization (2021.findings-acl)

Challenge:	Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking.
Approach:	They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result.
Outcome:	The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking.

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Danqing Wang, Jiaze Chen, Hao Zhou, Xipeng Qiu, Lei Li

Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)

Challenge:	Existing summarization systems for multilingual text summarizing are limited due to the lack of large-scale data in multiple languages.
Approach:	They propose a multilingual summarization system that can understand documents in multiple languages and generate summaries in the corresponding language.
Outcome:	The proposed model improves over monolingual models in all languages and transferable to other languages.

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Bowen Yang, Cong Han, Yu Li, Lei Zuo, Zhou Yu

BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data (2025.acl-long)

Challenge:	Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking.
Approach:	They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation.
Outcome:	The proposed architecture improves the integration of recommendation and dialog generation functions.

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Wenkai Li, Jiarui Liu, Andy Liu, Xuhui Zhou, Mona T. Diab, Maarten Sap

RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)

Challenge:	Existing methods for embedding human personality traits into LLMs are limited by realism and validity issues.
Approach:	They propose to use a large-scale dataset to embed human personality traits into LLMs . they use supervised fine-tuning and direct preference optimization to train LLM models .
Outcome:	The proposed methods outperform prompting on personality assessments and IPIP-NEO, and show higher conscientiousness, agreeableness, lower extraversion, and lower neuroticism on reasoning tasks.

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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)

Challenge:	Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments.
Approach:	They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations.
Outcome:	The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning.

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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

Challenge:	Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints.
Approach:	They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs.
Outcome:	The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications.

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

Generating Temporally-ordered Event Sequences via Event Optimal Transport (2022.coling-1)

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

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Bo Zhou, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, Qiuxia Li

Challenge:	Existing methods for temporal event ordering and event infilling ignore the global semantics of events, and the model adopts a word-level objective to model events in texts.
Approach:	They propose a temporal event ordering and event infilling task using a model that uses maximum likelihood estimation to model events in texts.
Outcome:	The proposed model outperforms existing models on all evaluation datasets.

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

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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

Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (2023.emnlp-main)

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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Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao

A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)

Challenge:	Extensive experiments show that MEO significantly improves computational efficiency . compared to dense networks, sparsely activated networks only employ a few parameters for each input .
Approach:	They propose a method that merges multiple experts into one to reduce computation costs . they demonstrate that a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters for each input .
Outcome:	The proposed approach reduces the computational cost to that of a single expert by 83.3% compared to 82.6% in vanilla MoE.

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Yading Li, Dandan Song, Changzhi Zhou, Yuhang Tian, Hao Wang, Ziyi Yang, Shuhao Zhang

Learning Gender-Neutral Word Embeddings (D18-1)

Challenge:	Existing methods to enhance LLMs with knowledge graphs have limited results . knowledge graph question answering (KGQA) provides interpretable reasoning for large language models .
Approach:	They propose a framework for KG-enhanced LLM based on question decomposition and atomic retrieval . they propose question decomposing tree as framework for LLM reasoning .
Outcome:	The proposed framework outperforms existing reasoning-based baselines on KGQA datasets.

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Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, Kai-Wei Chang

Representation Learning

Challenge:	Word embeddings trained on human-generated corpora inherit strong gender stereotypes . prior studies show such embeddables exhibit social biases, such as gender stereotype .
Approach:	They propose a method to preserve gender information in certain dimensions of word vectors . they propose GN-GloVe, which is a gender-neutral variant of the word embedding model .
Outcome:	The proposed method preserves gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence.

Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models (2026.acl-long)

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Xiang Hu, Zhanchao Zhou, Ruiqi Liang, Zehuan Li, Wei Wu, Jianguo Li

Hybrid Alignment Training for Large Language Models (2024.findings-acl)

Challenge:	a recent study explores efficient ultra-long context modeling.
Approach:	They propose to use Hierarchical Sparse Attention to achieve efficient ultra-long context modeling.
Outcome:	The proposed model performs comparable to full-attention baselines on in-domain and out-of-domain tasks.

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Chenglong Wang, Hang Zhou, Kaiyan Chang, Bei Li, Yongyu Mu, Tong Xiao, Tongran Liu, JingBo Zhu

Structure-aware Generation Model for Cross-Domain Aspect-based Sentiment Classification (2024.lrec-main)

Challenge:	Existing approaches to align large language models with instructions and preferences are conflicting . et al., 2023b) show that hybrid alignment training can outperform baselines .
Approach:	They propose a hybrid alignment training approach based on alternating alignment and modified elastic weight consolidation methods to achieve better collaboration between different alignment tasks.
Outcome:	The proposed approach outperforms baseline alignment training methods on summarization and dialogue tasks.

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Shichen Li, Zhongqing Wang, Yanzhi Xu, Guodong Zhou

Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)

Challenge:	Existing generation models for cross-domain aspect-based sentiment classification ignore syntactic structures . syntaktic structures are pre-trained on natural language and can be catastrophic forgetting of distributional knowledge.
Approach:	They propose a structure-aware generation model that explicitly encodes syntactic structure into the model.
Outcome:	The proposed model can learn domain-irrelevant features based on syntactic pivot features.

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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu

Connecting the Dots Between Fact Verification and Fake News Detection (2020.coling-main)

Challenge:	Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure .
Approach:	They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness.
Outcome:	Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure .

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Qifei Li, Wangchunshu Zhou

Multi-matrix Factorization Attention (2025.findings-acl)

Challenge:	Existing methods for detecting fake news rely heavily on supervised learning on a large scale dataset with news articles labeled as fake or real by human experts.
Approach:	They propose a simple yet effective approach to connect the dots between fact verification and fake news detection by using a text summarization model pre-trained on news corpora to summarize the long news article into a short claim.
Outcome:	The proposed approach enables zero-shot fake news detection, alleviating the need for large scale training data to train fake news detector models.

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Jingcheng Hu, Houyi Li, Yinmin Zhang, Zili Wang, Shuigeng Zhou, Xiangyu Zhang, Heung-Yeung Shum

Challenge:	Existing variants for Multi-Head Attention (MHA) fail to maintain strong performance under stringent Key-Value cache (KV cache) constraints.
Approach:	They propose to use multi-matrix factorization attention and MFA-Key-reuse attention architectures to increase model capacity under tight KV cache constraints.
Outcome:	The proposed architecture outperforms existing methods while reducing KV cache usage by 56% and 93.7% in large-scale experiments.

SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)

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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao

Challenge:	Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions.
Approach:	They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels .
Outcome:	The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing.

Neural Relation Classification with Text Descriptions (C18-1)

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Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, Xiaobo Liang

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions (2025.emnlp-main)

Challenge:	State-of-the-art methods for relation classification suffer from data sparsity issue greatly.
Approach:	They propose a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models.
Outcome:	The proposed method achieves much better experimental results than other state-of-the-art methods on the SemEval 2010 dataset.

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Wanlong Liu, Junying Chen, Ke Ji, Li Zhou, Wenyu Chen, Benyou Wang

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models by incorporating external knowledge.
Approach:	They propose a method for synthesizing diverse and high-quality RAG instruction data based on any source corpus.
Outcome:	The proposed method outperforms existing methods in multiple tasks and achieves strong zero-shot performance.

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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou

Probability-Consistent Preference Optimization for Enhanced LLM Reasoning (2025.findings-acl)

Challenge:	Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning.
Approach:	They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning.
Outcome:	The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness.

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Yunqiao Yang, Houxing Ren, Zimu Lu, Ke Wang, Weikang Shi, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li

Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)

Challenge:	Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models.
Approach:	They propose a framework that establishes two quantitative metrics for preference selection: surface-level answer correctness and intrinsic token-level probability consistency.
Outcome:	The proposed framework outperforms existing outcome-only criterion approaches across a diverse range of LLMs and benchmarks.

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Weiyan Shi, Yu Li, Saurav Sahay, Zhou Yu

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)

Challenge:	Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome.
Approach:	They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards.
Outcome:	The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback.

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Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, Hui Xiong

SAPGraph: Structure-aware Extractive Summarization for Scientific Papers with Heterogeneous Graph (2022.aacl-main)

Challenge:	Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables.
Approach:	They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input.
Outcome:	The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets.

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Siya Qi, Lei Li, Yiyang Li, Jin Jiang, Dingxin Hu, Yuze Li, Yingqi Zhu, Yanquan Zhou, Marina Litvak, Natalia Vanetik

Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation (2025.findings-naacl)

Challenge:	Abstractive and extractive methods are used to condense long text into concise summaries while retaining essential information.
Approach:	They propose to use paper structure to extract paper summaries from long text . they provide a large-scale dataset of COVID-19-related papers .
Outcome:	The proposed framework generates more comprehensive and valuable summaries compared to previous work on COVID-19-related papers.

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Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kayhan Batmanghelich, Wenchao Li

MCDTB: A Macro-level Chinese Discourse TreeBank (C18-1)

Challenge:	Radiology report generation has shown great potential in assisting radiologists . generative medical Vision Large Language Models (VLLMs) are prone to hallucinations and can produce inaccurate diagnostic information.
Approach:	They propose a framework that provides both report-level and sentence-level uncertainties.
Outcome:	The proposed method improves factuality scores by 10% by rejecting 20% of reports on the MIMIC-CXR dataset.

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Feng Jiang, Sheng Xu, Xiaomin Chu, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection (2022.findings-emnlp)

Challenge:	Discourse analysis is becoming increasingly important in the field of natural language processing.
Approach:	They propose to annotate macro discourse information and additional discourse information to make annotation more objective and accurate.
Outcome:	The results show that the annotations are more objective and accurate than the previous ones.

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Lanrui Wang, Jiangnan Li, Zheng Lin, Fandong Meng, Chenxu Yang, Weiping Wang, Jie Zhou

Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)

Challenge:	Empathy is a key trait of everyday human conversations.
Approach:	They propose a serial encoding and Emotion-Knowledge interaction method for empathetic dialogue generation which is more sensitive to emotion dynamics in conversations.
Outcome:	The proposed method outperforms baseline evaluations on the utterance-level annotated EMPATHETICDIALOGUES.

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Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021.acl-demo)

Challenge:	Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP.
Approach:	They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks .
Outcome:	The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus .

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Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

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

Challenge:	Existing studies on conversational recommender systems lack a unified and standardized implementation or comparison.
Approach:	They propose to use a unified framework and highly-decoupled modules to develop CRSs.
Outcome:	The proposed framework collects 6 commonly used human-annotated CRS datasets and implements 19 models that include advanced techniques such as graph neural networks and pre-training models.

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

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

Capture the Key in Reasoning to Enhance CoT Distillation Generalization (2025.acl-long)

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Chengwei Dai, Kun Li, Wei Zhou, Songlin Hu

Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)

Challenge:	Existing distillation methods for Large Language Models (LLMs) focus on fine-tuning student SLMs on correct data, resulting in students struggling to learn the key instead of analyzing mistakes according to correct solutions.
Approach:	They propose a method that exposes key reasoning steps rather than simple fine-tuning students' CoTs data by using a set of prompts with similar reasoning paths but divergent conclusions.
Outcome:	The proposed method improves student SLMs' ability to learn key reasoning steps rather than fine-tuning them on teacher data.

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Xuebin Wang, Lei Zhang, Zhenghua Li, Shilin Zhou, Chen Gong, Yang Hou

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

Challenge:	Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data.
Approach:	They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data.
Outcome:	The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy.

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

FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)

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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Jian Chen, Peilin Zhou, Yining Hua, Loh Xin, Kehui Chen, Ziyuan Li, Bing Zhu, Junwei Liang

LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)

Challenge:	Existing financial question answering datasets lack scope diversity and question complexity.
Approach:	They propose to use a dataset for long-form question answering in finance to evaluate QA systems.
Outcome:	The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites.

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Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan

ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)

Challenge:	Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity.
Approach:	They propose a large-scale data compression method to reduce the size of training data . they use a bifurcated quantization strategy to maximize the diversity of samples .
Outcome:	The proposed method significantly reduces the size of training data while maximizing the submodular gain.

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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang

Disentangle-based Continual Graph Representation Learning (2020.emnlp-main)

Challenge:	Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos.
Approach:	They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies .
Outcome:	The proposed method is effective in a real-world video-search service.

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Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie Zhou, Yan Zhang

SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation (2026.findings-acl)

Challenge:	Existing graph embedding methods overlook streaming nature of incoming data in real-world applications.
Approach:	They propose a disentangle-based continual graph representation learning framework inspired by the human’s ability to learn procedural knowledge.
Outcome:	The proposed framework outperforms state-of-the-art continual graph representation learning framework and alleviate catastrophic forgetting problem.

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Chengyi Yang, Pengzhen Li, Jiayin Qi, Aimin Zhou, Ji Wu, Ji Liu

HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction (2023.findings-emnlp)

Challenge:	Text-to-Video (T2V) generation is a challenge under complex scenarios.
Approach:	They propose a scenario-aware and self-correcting multi-agent prompt refinement framework for T2V prompting.
Outcome:	The proposed framework improves text-to-video alignment and overall generation quality under complex scenarios.

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Min Li, Cong Shao, Gang Li, Mingle Zhou

A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)

Challenge:	Existing approaches to extract sentence-level features are labor-intensive and time-consuming.
Approach:	They propose a distantly supervised relation extraction algorithm that uses circular cosine similarity to show intrinsic associations between sentences within a bag.
Outcome:	The proposed method outperforms baselines on the popular DSRE datasets.

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Xin Li, Xiaojie Zhang, Peng JiaHao, Rui Mao, Mingyang Zhou, Xing Xie, Hao Liao

Explainability & Interpretability in NLP

Challenge:	Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival.
Approach:	They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews.
Outcome:	The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews.

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang

Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System (2025.acl-long)

Challenge:	Tabular data analysis is performed everyday across various domains.
Approach:	They propose to use a dataset of 467k tables with supervision labels for four types of field metadata.
Outcome:	The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information.

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Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, Nanqing Dong

From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)

Challenge:	Recent AI methods have shown promise in tasks such as hypothesis generation and experimental design, but they fail to replicate the collaborative nature of real-world scientific practices.
Approach:	They propose a virtual scientific system that mimics the collaborative nature of scientific research by organizing a team of agents to generate, evaluate, and refine research ideas.
Outcome:	The proposed system outperforms the state-of-the-art method in producing new scientific ideas and offers valuable insights to guide future research.

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Jiaxiang Chen, Zhuo Wang, Mingxi Zou, Zhucong Li, Zhijian Zhou, Song Wang, Zenglin Xu

Context-Efficient Retrieval with Factual Decomposition (2025.naacl-short)

Challenge:	Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction.
Approach:	They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement.
Outcome:	The proposed model outperforms strong baselines on the Big-Bench Hard benchmark.

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Yanhong Li, David Yunis, David McAllester, Jiawei Zhou

MARCH: Multi-Agent Reinforced Check for Hallucination (2026.acl-long)

Challenge:	Existing models that use dynamically expanding text can be incorporated into large language models.
Approach:	They show that pre-processing external corpus into semi-structured "atomic facts" reduces the size of the context and improves inference efficiency.
Outcome:	The proposed form of atomic facts improves on question answering tasks when the amount of retrieved text is limited.

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Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, null Erchao.zec, Xiaoxi Jiang, null Guanjunjiang

Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs (2026.acl-long)

Challenge:	Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation.
Approach:	They propose a framework that enforces rigorous factual alignment by leveraging deliberate information asymmetry by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker.
Outcome:	Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates.

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Xuancheng Li, Haitao Li, Yujia Zhou, Yiqun Liu, Qingyao Ai

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)

Challenge:	Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse relies on external retrieval, which is similarity-based, can introduce noise, and adds latency.
Approach:	They propose a lightweight plug-in that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor.
Outcome:	Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead.

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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li

Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning (2026.acl-long)

Challenge:	Existing fact-checking methods that use large language models often generate subtle factual errors.
Approach:	They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation.
Outcome:	GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call.

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Xin Guan, Zijian Li, Shen Huang, Pengjun Xie, Jingren Zhou, Jiuxin Cao

Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning (2022.acl-long)

Challenge:	Evidence-Augmented Policy Optimization (EAPO) improves long-context reasoning performance . Xu et al., 2025): large language models are a critical part of NLP .
Approach:	They propose an Evidence-Augmented Reasoning paradigm that uses a group-relative reward to improve evidence quality.
Outcome:	EAPO significantly improves long-context reasoning performance compared to baselines.

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Kang Zhou, Yuepei Li, Qi Li

Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training (2025.findings-acl)

Challenge:	Existing methods for named entity recognition suffer from incomplete annotations due to incompleteness of external knowledge bases.
Approach:	They propose a method to solve the named entity recognition problem under distant supervision using dictionaries and knowledge bases.
Outcome:	The proposed method outperforms existing methods on two benchmark datasets labeled by various knowledge bases.

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Zhijun Wang, Jiahuan Li, Hao Zhou, Rongxiang Weng, Jingang Wang, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang

NarGINA: Towards Accurate and Interpretable Children’s Narrative Ability Assessment via Narrative Graphs (2025.findings-acl)

Challenge:	Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data.
Approach:	They investigate the existence of code-switching in the pre-training corpus and categorize it into four types within two quadrants.
Outcome:	The proposed approach improves performance across benchmarks and representation space.

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Jun Zhong, Longwei Xu, Li Kong, Xianzhuo Li, Dandan Liang, Junsheng Zhou

Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)

Challenge:	Existing methods for assessing children's narrative ability are limited to evaluating completeness of narrative content and the coherence of expression, as well as interpretability of assessment results.
Approach:	They propose a computational framework for assessing narrative ability using a narrative graph to provide a concise and structured summary representation of narrative text.
Outcome:	The proposed framework achieves significant performance improvement over baselines while possessing good interpretability.

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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li

SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)

Challenge:	Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability.
Approach:	They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity .
Outcome:	The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks.

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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo

CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question Answering (2026.findings-acl)

Challenge:	Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone .
Approach:	They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry.
Outcome:	The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis.

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Tong Lu, Zhichun Wang, Yuanhao Sun, Yaoyu Zhou, Mingrui Li, Yiming Guan, Zhiyong Bai

EnigmaToM: Improve LLMs’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025.findings-acl)

Challenge:	Large language models (LLMs) possess strong capabilities in language understanding and generation, as well as remarkable problem-solving abilities.
Approach:	They propose a benchmark to assess the cognitive alignment capabilities of large language models in educational QA.
Outcome:	The proposed evaluation benchmark assesses the cognitive alignment capabilities of large language models in educational QA.

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Hainiu Xu, Siya Qi, Jiazheng Li, Yuxiang Zhou, Jinhua Du, Caroline Catmur, Yulan He

Knowledge Representation

Challenge:	Existing ToM reasoning methods rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM.
Approach:	They propose a neuro-symbolic framework that integrates a Neural Knowledge Base of Entity States and knowledge injection to enhance ToM reasoning.
Outcome:	The proposed framework improves ToM reasoning on ToMi, HiToM, and FANToM benchmarks.

RuleR: Improving LLM Controllability by Rule-based Data Recycling (2025.naacl-short)

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Ming Li, Han Chen, Chenguang Wang, Dang Nguyen, Dianqi Li, Tianyi Zhou

Challenge:	Existing supervised fine-tuning datasets are composed of general instructions without userspecified constraints.
Approach:	They propose a data augmentation method incorporating multiple constraints into the original data samples according to predefined rules to create new training tasks.
Outcome:	The proposed method improves LLM controllability while maintaining general instruction-following capabilities.

ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)

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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu

See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers (2026.acl-long)

Challenge:	Existing studies focus on language-based premises and deduce valid conclusions from visual observations.
Approach:	They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning .
Outcome:	Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts .

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Ding Xia, Xinyue Gui, Mark Colley, Fan Gao, Zhongyi Zhou, Dongyuan Li, Renhe Jiang, Takeo Igarashi

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

Challenge:	External Human-Machine Interfaces (eHMIs) are emerging as promising solutions to address this communication gap.
Approach:	They propose a framework that uses vision-language models (VLMs) for perceptual evaluation as automated visual feedback to improve an LLM-based eHMI action designer.
Outcome:	The proposed framework outperforms prompt-only LLM designers and manually specified baselines in three eHMI modalities and multiple LLM model sizes.

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution (2026.findings-acl)

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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Zouying Cao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu, Bolin Ding, Hai Zhao

General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)

Challenge:	Existing frameworks treat memory as a static append-only archive . Existing systems focus on passive accumulation, resulting in a 'passive accumulation' of memory.
Approach:	They propose a framework for experience-driven agent evolution that integrates procedural memory with contextual information to create a high-quality experience pool.
Outcome:	Experiments on BFCL-V3 and AppWorld show that ReMe outperforms memoryless Qwen3-8B.

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Jingfei Du, Myle Ott, Haoran Li, Xing Zhou, Veselin Stoyanov

Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation (2025.findings-acl)

Challenge:	Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources .
Approach:	They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks.
Outcome:	The proposed method reduces the size of the extracted representations by a factor of 16 to store them for later use.

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Qiji Zhou, YiFan Gong, Guangsheng Bao, Hongjie Qiu, Jinqiang Li, Xiangrong Zhu, Huajian Zhang, Yue Zhang

S2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning (2025.acl-long)

Challenge:	Existing multimodal benchmarks often overlook counterfactual reasoning, which is crucial for robust video understanding.
Approach:	They propose a multidimensional multimodal benchmark that systematically evaluates MLLMs across the abstract-concrete and perception-cognition dimensions.
Outcome:	The proposed model decomposes complex queries into structured sub-questions, enabling fine-grained reasoning analysis.

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Ruotian Ma, Peisong Wang, Cheng Liu, Xingyan Liu, Jiaqi Chen, Bang Zhang, Xin Zhou, Nan Du, Jia Li

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

Challenge:	Existing approaches to incentivize LLMs’ deep thinking abilities require large-scale data or significant training efforts.
Approach:	They introduce an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.
Outcome:	The proposed framework outperforms models trained on long-CoT distilled data with 3.1k initialization samples and achieves an accuracy improvement of 51.0% to 81.6%.

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

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

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

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

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

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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Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor (2026.findings-acl)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

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Qianjun Pan, Junyi Wang, Jie Zhou, Yutao Yang, Junsong Li, Kaiyin Xu, Yougen Zhou, Yihan Li, JingYuan Zhao, Qin Chen, Ningning Zhou, Kai Chen, Liang He

Challenge:	Existing models focus on a single therapy, but complex cases require flexible strategies among various therapies.
Approach:	They propose a multi-session, multi-therapy, and highly realistic benchmark . it is designed to address three key challenges: 1) can we train a highly realistic AI counselor? 2) How to systematically evaluate an AI counselor?"
Outcome:	The proposed benchmark is annotated with extensive professional skills and includes over 677 meta-skills and 4577 atomic skills.

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

Multimodal Document-level Triple Extraction via Dynamic Graph Enhancement and Relation-Aware Reflection (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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Xiang Li, Runhai Jiao, Zhou Changyu, Shoupeng Qiao, Ruojiao Qiao, Ruifan Li

On the Emotion Understanding of Synthesized Speech (2026.acl-long)

Challenge:	Existing methods for extracting structured triples knowledge from multimodal documents face limitations in simultaneously processing long textual content and multiple associated images for triple extraction.
Approach:	They propose a multimodal document-level triple extraction framework that integrates multimodal text and visual content into a large language model and injects the global information and external knowledge into the model.
Outcome:	The proposed framework outperforms the state-of-the-art methods and fills the gap in multimodal document extraction.

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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Existing models for emotion understanding do not capture fundamental features of synthesized speech.
Approach:	They evaluate emotion recognition models on synthesized speech using SER models and generative models.
Outcome:	The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

Pre-trained Token-replaced Detection Model as Few-shot Learner (2022.coling-1)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Zicheng Li, Shoushan Li, Guodong Zhou

ViFT: Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Pre-trained masked language models have demonstrated remarkable few-shot learning ability . a novel approach to few- shot learning with pre-tried token-replaced detection models is proposed .
Approach:	They propose a method to reformulate a classification or regression task as a token-replaced detection problem by using pre-trained token-based models.
Outcome:	The proposed approach outperforms pre-trained masked language models in learning tasks . it can learn models with a few examples and generalize well from limited examples like humans .

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Zikang Liu, Kun Zhou, Xin Zhao, Dawei Gao, Yaliang Li, Ji-Rong Wen

Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions (2022.emnlp-industry)

Challenge:	Visual instruction tuning is the predominant technology in eliciting multimodal task-solving capabilities of large vision-language models.
Approach:	They propose a visual instruction-free fine-tuning framework for large vision-language models . they require only text-only instructions and image caption data during training .
Outcome:	The proposed framework is based on visual instruction tuning, but requires images as input . it can achieve state-of-the-art performance on several downstream benchmarks with less training data.

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Rindra Ramamonjison, Haley Li, Timothy Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-dehkordi, Zirui Zhou, Yong Zhang

Challenge:	Existing systems for operations research use NLP to suggest formulations of optimization problems.
Approach:	They propose an augmented intelligence system that can be used to simplify and enhance the modeling experience for operations research.
Outcome:	The proposed system validates and edits the proposed formulations with a dataset of linear programming problems drawn from various application domains.

CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)

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Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou

Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025.emnlp-main)

Challenge:	Existing EE methods do not model event characteristics from large unsupervised data.
Approach:	They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures.
Outcome:	The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks.

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Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou

Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)

Challenge:	Large reasoning models (LRMs) have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning.
Approach:	They propose a framework that enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module for refining retrieved documents.
Outcome:	The proposed framework enhances LRMs with an agentic retrieval-augmented generation mechanism and Reason-in-Documents module for refining retrieved documents.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Jie Zhou, Yue Zhang

DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis (2022.coling-1)

Challenge:	Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents.
Approach:	They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data.
Outcome:	The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences.

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Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao

Controllable Style Arithmetic with Language Models (2025.acl-long)

Challenge:	X-ray and CT are the gold standard for COVID-19 diagnosis and treatment . however, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists.
Approach:	They propose to use X-ray and CT to generate medical reports automatically . they evaluate DeltaNet on a COVID-19 dataset, where it outperforms state-of-the-art approaches .
Outcome:	The proposed system outperforms state-of-the-art methods on a COVID-19 dataset.

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Weiqi Wang, Wengang Zhou, Zongmeng Zhang, Jie Zhao, Houqiang Li

Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features (2023.findings-emnlp)

Challenge:	Existing methods for linguistic style control lack fine-grained control, require extensive computation, or introduce significant latency.
Approach:	They propose a parameter-space approach that extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles and incorporates them into a model with precise control over style intensity.
Outcome:	The proposed approach achieves three key capabilities while achieving optimal computational efficiency.

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Li Zhou, Antonia Karamolegkou, Wenyu Chen, Daniel Hershcovich

Ethical NLP

Challenge:	Current knowledge is limited on whether cultural features can predict cross-cultural transfer learning success for subjective tasks.
Approach:	They advocate integration of cultural information into datasets and cultural adaptability . findings suggest cultural features can predict cross-cultural transfer learning success .
Outcome:	The findings suggest that cultural features can predict cross-cultural transfer learning success in OLD tasks.

SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models (2026.acl-long)

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Yuanhe Zhang, Jiayu Tian, Yibo Zhang, Shilinlu Yan, Liang Lin, Zhenhong Zhou, Li Sun, Sen Su

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	Existing studies on noise lack quantitative analysis and rely on intuition and empirical observation, thus failing to understand practical robustness.
Approach:	They propose a method for quantifying the impact of noise intensity on LALM inputs by using a structured activation subspace derived from the model's internal representations.
Outcome:	The proposed method outperforms existing denoising methods and demonstrates that noise is perceived more accurately than raw audio features.

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams.
Approach:	They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids .
Outcome:	The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct .

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou

Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (2020.acl-main)

Challenge:	Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark.
Approach:	They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks.
Outcome:	The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy.

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Ning Miao, Yuxuan Song, Hao Zhou, Lei Li

Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach (2022.findings-emnlp)

Challenge:	Pre-trained language models can be fine-tuned on task-specific datasets, but fine-timing can lead to over- and/or under-estimation problems.
Approach:	They propose a method to transfer probability mass from over-estimated regions to under-estimates by truncating and transferring probability mass between over- and under-estimating regions.
Outcome:	The proposed method outperforms the fine-tuning approach on a variety of datasets.

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Zejie Liu, Xiaoyu Hu, Deyu Zhou, Lin Li, Xu Zhang, Yanzheng Xiang

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

Challenge:	Currently, researchers focus on generating codes from requirement documents.
Approach:	They propose to generate source code from flowcharts with texts instead of directly translating requirements into codes.
Outcome:	The proposed model improves on the baselines by transforming flowcharts into pseudo-code . the proposed model is based on 320 flowchartes with their corresponding source codes .

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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022.coling-1)

Challenge:	a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms.
Approach:	They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration .
Outcome:	The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining .

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Bo Zhou, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, Qiuxia Li

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

Challenge:	Existing methods to predict event sequences are complex and ignore the knowledge of external events.
Approach:	They propose a statistical induction problem to generate a sequence of events by exploring the similarity between the given goal and known sequences of events.
Outcome:	The proposed model outperforms existing methods on an event sequence prediction task.

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

Sentiment Forecasting in Dialog (2020.coling-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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Zhongqing Wang, Xiujun Zhu, Yue Zhang, Shoushan Li, Guodong Zhou

Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)

Challenge:	Existing studies on sentiment classification focus on determining polarity of existing utterances.
Approach:	They propose a Neural Sentiment Forecasting task which simulates the next utterance based on context and a sequence influence model to learn both pair-wise and seq-wise influence.
Outcome:	The proposed model outperforms existing models over several strong baselines.

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Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou

Summarization

Challenge:	Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions.
Approach:	They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization.
Outcome:	The proposed model outperforms the state-of-the-art models in the zero-shot directions.

Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation (2025.emnlp-main)

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Junzhuo Li, Bo Wang, Xiuze Zhou, Xuming Hu

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)

Challenge:	Existing approaches to adapt Mixture-of-Experts models to multiple domains are prohibitive computation, cross-domain interference or require separate runs per domain.
Approach:	They propose a dynamic expert specialization framework for multi-domain adaptation of Mixture-of-Experts models.
Outcome:	The proposed framework reduces forgetting by 89% compared to full fine-tuning as domains scale from 2 to 6 and achieves faster convergence than conventional methods.

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Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng

Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)

Challenge:	Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts.
Approach:	They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory.
Outcome:	Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks.

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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang

Challenge:	Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance.
Approach:	They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively.
Outcome:	The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark.

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2024.naacl-long)

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Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have revolutionized the landscape of artificial intelligence.
Approach:	They propose a self-guided method to identify and select cherry samples from open-source datasets, minimizing manual curation and potential cost for instruction tuning an LLM.
Outcome:	The proposed method enables LLMs to identify discrepancies between expected responses and intrinsic generation capability, and a marked uptick in model training efficiency.

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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

Challenge:	Large language models struggle with complex reasoning tasks, such as mathematical problem-solving.
Approach:	They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning.
Outcome:	The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters.

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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang

AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment (2023.findings-acl)

Challenge:	Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach:	They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome:	The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.

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Ruiqi Li, Rongjie Huang, Lichao Zhang, Jinglin Liu, Zhou Zhao

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Existing approaches to speech-to-singing voice conversion are difficult to learn in text-free situations.
Approach:	They propose an STS model which views speech variance as different modalities . it uses a novel rhythm adaptor to predict the target rhythm representation . they also use the predicted rhythm representation to re-align the content .
Outcome:	The proposed model achieves superior performance in terms of objective and subjective metrics.

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang

Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs (2025.acl-long)

Challenge:	Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).
Approach:	They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE.
Outcome:	The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency.

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Yanzhu Guo, Simone Conia, Zelin Zhou, Min Li, Saloni Potdar, Henry Xiao

U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction (2023.tacl-1)

Challenge:	Current Large Language Models (LLMs) are predominantly designed with English as the primary language, but many are still English-dominated.
Approach:	They propose to use automatic corpus-level metrics to assess lexical and syntactic naturalness of LLMs in a multilingual context.
Outcome:	The proposed method improves naturalness of LLMs in target languages without compromising performance on general-purpose benchmarks.

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Jie Zhou, Shenpo Dong, Yunxin Huang, Meihan Wu, Haili Li, Jingnan Wang, Hongkui Tu, Xiaodong Wang

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations.
Approach:	They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering.
Outcome:	The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE.

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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

“I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns? (2026.acl-long)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Naen Xu, Jiayi Sheng, Changjiang Li, Chunyi Zhou, Yuyuan Li, Tianyu Du, Jun Wang, Zhihui Fu, Jinbao Li, Shouling Ji

CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)

Challenge:	Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor.
Approach:	They propose a multimodal pun generation pipeline and a model to evaluate their understanding of puns.
Outcome:	The proposed benchmark improves the understanding of multimodal puns by 16.5% in the F1 test.

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Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Kangwen Zhao, Dongyun Xue, Mingxiao Feng, Wengang Zhou, Houqiang Li

Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)

Challenge:	Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases.
Approach:	They propose a feedback-driven iterative framework that leverages Large Language Models to generate initial test cases, execute them against known correct and incorrect solutions, and utilizes the failed results as feedback to guide the LLM in refining the test cases toward high fidelity and discriminability.
Outcome:	The proposed method outperforms the existing codecontests and codecontests+ models by 4.30% and 8.78%.

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Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, Tianyi Zhou

GroundingGPT: Language Enhanced Multi-modal Grounding Model (2024.acl-long)

Challenge:	Instruction tuning is critical to large language models but its success heavily relies on the training data quality.
Approach:	They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data.
Outcome:	The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data.

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Zhaowei Li, Qi Xu, Dong Zhang, Hang Song, YiQing Cai, Qi Qi, Ran Zhou, Junting Pan, Zefeng Li, Vu Tu, Zhida Huang, Tao Wang

Your Reasoning Model is Secretly a Reward Model - Optimization-Free Verification from Experience (2026.acl-long)

Challenge:	Existing multi-modal large language models focus on capturing global information while neglecting the fine-grained local information in multimodal inputs.
Approach:	They propose an end-to-end language enhanced multi-modal grounding model that performs fine-grained grounding tasks for image, video and audio.
Outcome:	The proposed model achieves impressive fine-grained understanding of multi-modal inputs while maintaining or improving its global comprehension capabilities.

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Zhenwen Liang, Ruosen Li, Yujun Zhou, Linfeng Song, Dian Yu, Xinya Du, Haitao Mi, Dong Yu

Low-probability Tokens Sustain Exploration in Reinforcement Learning with Verifiable Reward (2026.findings-acl)

Challenge:	Existing verifiers operate on the surface text or on confidence proxies derived from token probabilities, which can be brittle.
Approach:	They propose a training-free, non-parametric verifier that summarizes each reasoning trace by an activation delta and compares it to two class centroids computed from labeled experience.
Outcome:	The proposed model improves selection and reranking on large and less-calibrated models.

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Guanhua Huang, Tingqiang Xu, Mingze Wang, Qi Yi, Xue Gong, Siheng Li, Ruibin Xiong, Kejiao Li, Yuhao Jiang, Bo Zhou

Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)

Challenge:	Recent studies show that RLVR training is slow and results plateau as policy entropy collapses . low-probability regularization (Lp-Reg) reduces the number of low-quality exploratory tokens induced by RL training .
Approach:	They propose a method to reduce RLVR over-penalization by eliminating low-probability exploratory tokens . they propose 'Low-provability Regularization' to reduce the gradual elimination of low-quality exploratory entropy tokens.
Outcome:	The proposed method eliminates low-probability exploratory tokens and prevents suppression of potentially valuable low-property candidates.

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Liyang He, Chenglong Liu, Rui Li, Zhenya Huang, Shulan Ruan, Jun Zhou, Enhong Chen

Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer (2024.acl-srw)

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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Yongqi Wang, Bai Jionghao, Rongjie Huang, Ruiqi Li, Zhiqing Hong, Zhou Zhao

Unveiling Inherent Visual Grounding in Multimodal LLMs for Text-Rich Images (2026.findings-acl)

Challenge:	Existing methods to translate spoken utterances from one language to another are unable to preserve speaker timbre of source speech.
Approach:	They propose a pipeline with style-transfer capability on the basis of self-supervised speech representations and codec units.
Outcome:	The proposed model achieves zero-shot cross-lingual style transfer on previously unseen source languages.

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Shijie Zhou, Jihyung Kil, Ming Li, Jiuxiang Gu, Curtis Wigington, Rajiv Jain, Changyou Chen, Ruiyi Zhang

CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)

Challenge:	Existing multimodal large language model (MLLM) approaches struggle to align query tokens with visual–text patches, heavily relying on lengthy OCR inputs.
Approach:	They propose an OCR-free approach that leverages the MLLM's inherent multi-head attention for multi-patch grounding.
Outcome:	Empirical results show that the proposed approach outperforms existing approaches on challenging document grounding benchmarks.

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Ziyue Fan, Junliang He, Li Xiaoqing, Shaohui Kuang, Kai Song, Yaqian Zhou, Xipeng Qiu

Advancement in Graph Understanding: A Multimodal Benchmark and Fine-Tuning of Vision-Language Models (2024.acl-long)

Challenge:	Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP).
Approach:	They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations.
Outcome:	The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks.

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Qihang Ai, Jiafan Li, Jincheng Dai, Jianwu Zhou, Lemao Liu, Haiyun Jiang, Shuming Shi

LM2Protein: A Structure-to-Token Protein Large Language Model (2025.findings-emnlp)

Challenge:	Graph data organizes complex relationships and interactions between objects . Graph neural networks (GNNs) are becoming more popular in graph learning .
Approach:	They propose a new paradigm for interactive and instructional graph data understanding and reasoning . they first evaluate the capabilities of public VLMs in graph learning from multiple aspects .
Outcome:	The proposed model achieves an accuracy increase of 5%-15% compared to baseline models . the best-performing model achieve scores comparable to Gemini in GPT-asissted Evaluation .

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Chang Zhou, Yuheng Shan, Pengan Chen, Xiangyu Shi, Zikang Wang, Yanting Li, Jiyue Jiang

Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)

Challenge:	RNA-binding proteins are critical for various molecular functions, relying on their precise tertiary structures.
Approach:	They propose a method to integrate protein 3D structural data within a sequence processing framework.
Outcome:	The proposed method achieves high sequence recovery in inverse folding and protein-conditioned RNA design.

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Yanshuo Wang, Yanghao Zhou, Yukang Lin, Haoxing Chen, Jin Zhang, Wentao Zhu, Jie Hong, Xuesong Li

CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)

Challenge:	Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts.
Approach:	They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios.
Outcome:	Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines.

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Zecheng Tang, Keyan Zhou, Juntao Li, Yuyang Ding, Pinzheng Wang, Yan Bowen, Renjie Hua, Min Zhang

UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)

Challenge:	Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality.
Approach:	They propose a text detoxification framework that pays attention to both context and detoxification process.
Outcome:	Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines.

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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou

Challenge:	Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems.
Approach:	They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets.
Outcome:	The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization.

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)

Challenge:	Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach:	They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome:	The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.

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Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (2022.emnlp-main)

Challenge:	Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models.
Approach:	They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain .
Outcome:	The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice.

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Ziqiang Zhang, Long Zhou, Junyi Ao, Shujie Liu, Lirong Dai, Jinyu Li, Furu Wei

Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs (2026.acl-long)

Challenge:	Existing models for pre-training text and speech are based on unlabeled audio data.
Approach:	They propose a unified-modal speech-unit-text pre-training model that connects speech encoders and text decoders with a shared unit encoder.
Outcome:	The proposed model improves on automatic speech recognition and speech translation tasks and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks.

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Tingchao Fu, Wenkai Wang, Fanxiao Li, Huadong Zhang, Jinhong Zhang, Dayang Li, Yunyun Dong, Renyang Liu, Wei Zhou

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

Challenge:	Existing knowledge editing paradigms suffer from editing decoupling failures . entity knowledge is sequestered into disentangled modality-specific pathways .
Approach:	They propose a method that explicitly disentangles and localizes modality-specific neuron groups for targeted knowledge.
Outcome:	The proposed method outperforms baselines in reliability and consistency while preserving model locality.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He, Bowen Zhou

Improving Neural Machine Translation with Soft Template Prediction (2020.acl-main)

Challenge:	Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge.
Approach:	They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality.
Outcome:	The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets.

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Jian Yang, Shuming Ma, Dongdong Zhang, Zhoujun Li, Ming Zhou

Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)

Challenge:	Recent advances in neural machine translation (NMT) depend on source text to generate translation.
Approach:	They propose to use extracted templates from tree structures as soft target templates to guide the translation procedure.
Outcome:	The proposed model outperforms baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.

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Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, Tieyun Qian

IMRRF: Integrating Multi-Source Retrieval and Redundancy Filtering for LLM-based Fake News Detection (2025.naacl-long)

Challenge:	Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically.
Approach:	They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design.
Outcome:	The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals.

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Dayang Li, Fanxiao Li, Bingbing Song, Li Tang, Wei Zhou

PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)

Challenge:	Existing methods to detect fake news rely on manual checking, which is time-consuming.
Approach:	They propose a model which integrates textual corpus retrieval with knowledge graph retrieval to retrieve more comprehensive evidence and a redundant information filtering strategy which minimizes the influence of irrelevant information on the LLM reasoning process.
Outcome:	The proposed method outperforms state-of-the-art fact-checking baselines on two challenging fact- checking datasets.

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Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen

PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles (2025.naacl-long)

Challenge:	Existing evaluations of phone recognition systems only measure surface-level transcription accuracy.
Approach:	They propose to standardize transcription-based evaluation and assess downstream utility in clinical, educational, and multilingual settings with transcription and representation probes.
Outcome:	The proposed system outperforms LALMs in clinical, educational, and multilingual settings.

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Siyan Li, Vethavikashini Chithrra Raghuram, Omar Khattab, Julia Hirschberg, Zhou Yu

Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems (2022.emnlp-main)

Challenge:	Existing research has studied privacy in LLM training data memorization, but it does not prevent users from disclosing PII at inference time.
Approach:	They propose a task for chaining API-based and local LLMs that uses public data to construct a benchmark that contains personally identifiable information (PII)
Outcome:	The proposed model maintains high response quality for 85.5% of user queries while restricting privacy leakage to only 7.5%.

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David Gros, Yu Li, Zhou Yu

Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding (2021.findings-emnlp)

Challenge:	Dialog systems often output human-like responses, but some are impossible for a machine to say.
Approach:	They collect ratings on the feasibility of 900 two-turn dialogs from 9 data sources . they build classifiers and explore how modeling configuration might affect output permissibly .
Outcome:	The proposed model can be used to train human-like dialogs, but it is not anthropomorphic.

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Yuechen Wang, Wengang Zhou, Houqiang Li

INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)

Challenge:	Existing weakly supervised methods for temporal language grounding lose the complexity of the video and the semantics of the sentence.
Approach:	They propose a candidate-free framework for weakly supervised Temporal Language Grounding . they use a token-by-clip cross-modal semantic alignment module to learn alignment .
Outcome:	The proposed framework achieves state-of-the-art on two widely-used benchmarks: ActivityNet-Captions, and DiDeMo.

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Chuyue Zhou, Wangjie You, Juntao Li, Jing Ye, Kehai Chen, Min Zhang

How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients (2026.acl-long)

Challenge:	Large language models (LLMs) have demonstrated exceptional performance with dedicated Chain-of-Thought (CoT) prompts.
Approach:	They propose a new method by introducing information entropy as a criteria on for CoT prompt selection.
Outcome:	The proposed model outperforms existing models on seven reasoning benchmarks using two language models.

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Ming Li, Yanhong Li, Ziyue Li, Tianyi Zhou

WorkTeam: Constructing Workflows from Natural Language with Multi-Agents (2025.naacl-industry)

Challenge:	Spectral properties of low/high-quality instruction and reasoning data are used to explain finetuning dynamics in large language models.
Approach:	They propose to analyze layer-wise gradients induced by low/high-quality instruction and reasoning data for LLM post-training.
Outcome:	The results show that higher-quality data are associated with lower nuclear norms and higher effective ranks.

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Hanchao Liu, Rongjun Li, Weimin Xiong, Ziyu Zhou, Wei Peng

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)

Challenge:	Existing workflow construction methods require specialized knowledge and task-switching skills.
Approach:	They propose a multi-agent workflow framework that incorporates a supervisor, orchestrator, and filler agent.
Outcome:	The proposed framework significantly increases the success rate of workflow construction . the proposed framework is based on a dataset of 3,695 real-world business samples .

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Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu

HyQE: Ranking Contexts with Hypothetical Query Embeddings (2024.findings-emnlp)

Challenge:	Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models.
Approach:	They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression.
Outcome:	Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution.

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Weichao Zhou, Jiaxin Zhang, Hilaf Hasson, Anu Singh, Wenchao Li

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)

Challenge:	Existing approaches to rank contexts rely on similarity between contexts and queries, but these methods are limited by the number of candidate contexts.
Approach:	They propose a scalable ranking framework that combines embedding similarity and large language models without fine-tuning.
Outcome:	The proposed framework improves the performance across multiple benchmarks.

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Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li

Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)

Challenge:	Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes.
Approach:	They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques.
Outcome:	The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks.

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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)

Challenge:	Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student.
Approach:	They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions.
Outcome:	The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures.

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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen

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

Challenge:	Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge.
Approach:	They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance.
Outcome:	Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points.

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

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)

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

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Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning (2025.findings-acl)

Challenge:	Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection.
Approach:	They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM.
Outcome:	The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead.

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Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, YuPeng Hou, Fuxiao Liu, Tianyi Zhou

Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)

Challenge:	Current instruction tuning relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity.
Approach:	They propose a human/model-free compositional data synthesis method that can create rich and diverse augmentations from existing instruction tuning data to enhance large language models.
Outcome:	The proposed method improves performance over benchmarks and reduces training costs by 80% compared with original instruction tuning.

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Zehui Lin, Xiao Pan, Mingxuan Wang, Xipeng Qiu, Jiangtao Feng, Hao Zhou, Lei Li

Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Existing pre-training methods are not effective for machine translation tasks.
Approach:	They propose a method to pre-train a universal multilingual neural machine translation model . they use random aligned substitution technique to bring words and phrases with similar meanings closer in the representation space.
Outcome:	The proposed approach improves translation quality on low, medium, rich resource languages.

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Renjie Pi, Kehao Miao, Li Peihang, Runtao Liu, Jiahui Gao, Jipeng Zhang, Xiaofang Zhou

ACIArena: Toward Unified Evaluation for Agent Cascading Injection (2026.acl-long)

Challenge:	Multimodal large language models exhibit a pronounced form of visual sycophantic behavior when they process image inputs.
Approach:	They propose a technique that allows multimodal large language models to engage in reflective reasoning and determine whether a user’s instruction is misleading or corrective.
Outcome:	The proposed model resists misleading instructions but is stubborn even if it is wrong.

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Hengyu An, Minxi Li, Jinghuai Zhang, Naen Xu, Chunyi Zhou, Changjiang Li, Xiaogang Xu, Tianyu Du, Shouling Ji

Memorize Step by Step: Efficient Long-Context Prefilling with Incremental Memory and Decremental Chunk (2024.emnlp-main)

Challenge:	Existing studies consider only limited attack strategies and simplified MAS settings, limiting their generalizability and comprehensive evaluation.
Approach:	They propose a framework to evaluate the robustness of Multi-Agent Systems (MAS) they propose unified evaluation suites spanning attack surfaces and attack objectives .
Outcome:	ACIArena provides a benchmark of 1,356 test cases for evaluating MAS robustness . it covers six widely used MAS implementations and provides measurable results .

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Zhiyuan Zeng, Qipeng Guo, Xiaoran Liu, Zhangyue Yin, Wentao Shu, Mianqiu Huang, Bo Wang, Yunhua Zhou, Linlin Li, Qun Liu, Xipeng Qiu

Don’t Corrupt the Fact: A Trustworthy RAG Watermarking Framework based on Dual Factual Shield (2026.acl-long)

Challenge:	Existing methods to optimize LLM for long sequences for long documents are slow and consume memory.
Approach:	They propose a method that starts with a small memory size and gradually increases it . they propose Decremental Chunk based on Incremental Memory (IMDC) which reduces chunk size while increasing memory size .
Outcome:	The proposed method is faster (1.45x) and reduces GPU memory consumption by 23.3% compared to fixed-size memory.

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Hao Huang, JiaTang Luo, Ruihua Zhou, Yunpeng Li, Yuling Liu

MiCEval: Unveiling Multimodal Chain of Thought’s Quality via Image Description and Reasoning Steps (2025.naacl-long)

Challenge:	Existing watermarking methods are fact-agnostic and cause "faithfulness hallucinations" a novel framework to enforce knowledge loyalty is proposed to improve watermarks .
Approach:	They propose a new framework that enforces knowledge loyalty by spoofing terms from retrieved contexts and prompt-based semantic guidance to protect against factual corruption.
Outcome:	The proposed framework reduces the Knowledge Corruption Rate while maintaining its original high security and robustness.

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Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li, Haojing Chen, Victor Gutierrez Basulto, Jeff Z. Pan, Hanjie Chen

Understanding User Resistance Strategies in Persuasive Conversations (2020.findings-emnlp)

Challenge:	Existing methods for evaluating the quality of reasoning steps in multimodal chain-of-thought are lacking.
Approach:	They propose a framework to evaluate the correctness of reasoning chains by evaluating the quality of both the description and each reasoning step.
Outcome:	The proposed framework improves interpretability and human judgments on four state-of-the-art MLLMs.

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Youzhi Tian, Weiyan Shi, Chen Li, Zhou Yu

The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)

Challenge:	Persuasive dialog systems have various usages, such as donation persuation and physical exercise persulasion.
Approach:	They adopt a preliminary framework on persuasion resistance in psychology and build a fine-grained resistance strategy annotation scheme to analyze the persuitee's resistance strategies.
Outcome:	The proposed system can understand and address user resistance strategies appropriately.

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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam

MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)

Challenge:	Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others.
Approach:	They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM.
Outcome:	The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants.

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Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

Challenge:	Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge.
Approach:	They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input.
Outcome:	The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

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

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

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

A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment (2025.findings-acl)

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

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Quanwei Tang, Sophia Yat Mei Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

C-LLM: Learn to Check Chinese Spelling Errors Character by Character (2024.emnlp-main)

Challenge:	Existing studies struggle with achieving global understanding of large language models . GraphMPA is a graph-based framework with mode-seeking preference alignment .
Approach:	They propose a graph-based framework with mode-seeking preference alignment to improve model outputs.
Outcome:	The proposed framework constructs a hierarchical document graph mimicking human cognitive processes for information understanding and synthesis.

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Kunting Li, Yong Hu, Liang He, Fandong Meng, Jie Zhou

DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences.
Approach:	They propose a Chinese Spell Checking method that learns to check errors Character by Character.
Outcome:	The proposed method achieves a 2.1% enhancement in general scenarios and a significant improvement in vertical domain scenarios compared to existing methods.

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Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, Ming Zhou

Challenge:	Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information.
Approach:	They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation .
Outcome:	The proposed model trains on DocBank accurately recognize layout information for a variety of documents.

Visualization Recommendation with Prompt-based Reprogramming of Large Language Models (2024.acl-long)

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Xinhang Li, Jingbo Zhou, Wei Chen, Derong Xu, Tong Xu, Enhong Chen

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Traditional visualization recommendations require extensive manual maintenance and yet fail to fully comprehend tabular data.
Approach:	They propose a hierarchical table prompt-based reprogramming framework that integrates tabular data into LLMs through a strategically crafted prompt learning method.
Outcome:	The proposed framework achieves state-of-the-art performance and will be made publicly available upon acceptance.

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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

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)

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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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang

Can Third Parties Read Our Emotions? (2025.acl-long)

Challenge:	Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress.
Approach:	They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability.
Outcome:	The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability.

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Jiayi Li, Yingfan Zhou, Pranav Narayanan Venkit, Halima Binte Islam, Sneha Arya, Shomir Wilson, Sarah Rajtmajer

Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

Challenge:	Existing approaches to infer author’s private states from written text have relied heavily on datasets annotated by third-party annotators.
Approach:	They propose a framework for evaluating the limitations of third-party annotations and call for refined annotation practices to accurately represent and model authors’ private states.
Outcome:	The proposed methods outperform human annotators on emotion recognition tasks.

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao

Challenge:	Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach:	They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome:	Empirical results show that NestBrowse offers clear benefits in practice.

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

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

WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code (2025.findings-acl)

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

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Zhiyu Lin, Zhengda Zhou, Zhiyuan Zhao, Tianrui Wan, Yilun Ma, Junyu Gao, Xuelong Li

NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)

Challenge:	Existing benchmarks for large language models focus on webpage generation outcomes.
Approach:	They propose a multi-view evaluation framework to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code.
Outcome:	The proposed framework evaluates MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code.

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Junda Wang, Zonghai Yao, Zhichao Yang, Huixue Zhou, Rumeng Li, Xun Wang, Yucheng Xu, Hong Yu

Kaimin Zhou, Chang Shu, Binyang Li, Jey Han Lau

Challenge:	NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps .
Approach:	They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues.
Outcome:	The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout .

Early Rumour Detection (N19-1)

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Challenge:	Existing studies on rumour detection are concerned with timing, but few are interested in how early we can detect them.
Approach:	They propose a method that integrates reinforcement learning to learn the minimum number of posts required before classifying an event as a rumour.
Outcome:	The proposed model detects rumours earlier than state-of-the-art systems while maintaining comparable accuracy.

Towards Objective Fine-tuning: How LLMs’ Prior Knowledge Causes Potential Poor Calibration? (2025.acl-long)

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Ziming Wang, Zeyu Shi, Haoyi Zhou, Shiqi Gao, Qingyun Sun, Jianxin Li

Emotion Detection with Neural Personal Discrimination (D19-1)

Challenge:	Large Language Models (LLMs) have enabled powerful domain-specific applications through supervised fine-tuning.
Approach:	They propose a cognition-aware framework that applies targeted learning strategies according to the model’s prior knowledge to improve calibration.
Outcome:	The proposed framework significantly improves calibration while maintaining performance, achieving an average 57% reduction in ECE compared to standard fine-tuning in Llama3-8B.

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Xiabing Zhou, Zhongqing Wang, Shoushan Li, Guodong Zhou, Min Zhang

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Existing approaches to automatically predict the emotions of posts consider each post individually and predict their emotions independently.
Approach:	They propose a Neural Personal Discrimination approach to identify personal attributes from posts and connect relevant posts with similar attributes to jointly learn their emotions.
Outcome:	The proposed approach improves on existing models by capturing attributes-aware words and predicting emotions among relevant posts.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

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

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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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

Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)

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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Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou

Unsupervised Dependency Graph Network (2022.acl-long)

Challenge:	Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches.
Approach:	They propose a dynamic flow mechanism to model the context flow and a model to capture the information dynamics across dialogue utterances.
Outcome:	The proposed model outperforms the DialoGPT on the dialogue generation task.

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Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie Zhou, Aaron Courville

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

Challenge:	Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures.
Approach:	They propose a model that encourages attention heads to model different dependency relations from raw corpora and a masked language modeling task.
Outcome:	The proposed model can induce dependency structures from raw corpora and the masked language modeling task without gold POS tags and any external information.

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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

CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability (2023.emnlp-main)

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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Minxuan Lv, Chengwei Dai, Kun Li, Wei Zhou, Songlin Hu

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)

Challenge:	Neural network models are vulnerable to adversarial examples, and current methods based on adversarially transferable models rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model’s structural details.
Approach:	They propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks.
Outcome:	The proposed approach achieves superior attack performance with small cost on ten datasets and demonstrates that it is a novel approach.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Chunyan Miao

CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges (2025.acl-long)

Challenge:	Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance.
Approach:	They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER)
Outcome:	The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs.

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Haitao Li, Junjie Chen, Qingyao Ai, Zhumin Chu, Yujia Zhou, Qian Dong, Yiqun Liu

Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)

Challenge:	Empirical evaluations of large language models demonstrate that they improve performance in a wide range of tasks.
Approach:	They propose a label-free method for mitigating selection bias during inference by reformulating debiasing as an optimization task.
Outcome:	The proposed method mitigates selection bias and improves performance compared to existing methods.

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Yihe Wang, Yitong Li, Yasheng Wang, Fei Mi, Pingyi Zhou, Xin Wang, Jin Liu, Xin Jiang, Qun Liu

Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)

Challenge:	Existing methods for generating open-domain dialogue systems underutilize training data.
Approach:	They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show .
Outcome:	The proposed method performs well on zero-shot experiments and is more robust to real-world data.

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Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li

Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification (2026.findings-acl)

Challenge:	Existing work performs code repair and commit message generation independently.
Approach:	They propose a cascaded method to repair program codes and generate commit messages in a unified framework.
Outcome:	The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset.

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Peng Zhou, Guangxin Li, Xiaoying Huang, Yiming Tang

Paraphrasing

Challenge:	Existing studies have focused on lexical- and sentence-level simplification, leaving long text simplification comparatively unexplored .
Approach:	They propose a two-level and progressive LLM-based framework that establishes an effective paradigm for automatic long text simplification under diverse test scenarios.
Outcome:	The proposed framework outperforms advanced and proprietary LLMs in in-domain and out-of-domain simplification tasks and matches or outperformed existing LLM frameworks.

TrojanSQL: SQL Injection against Natural Language Interface to Database (2023.emnlp-main)

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Jinchuan Zhang, Yan Zhou, Binyuan Hui, Yaxin Liu, Ziming Li, Songlin Hu

ChemAmp: Amplified Chemistry Tools via Composable Agents (2026.findings-acl)

Challenge:	Existing studies on text-to-SQL systems have not investigated its security aspects . however, how to implement such attacks remains an open question.
Approach:	They propose a backdoor-based SQL injection framework for text-to-SQL systems that uses boolean-based injection and union-based injecting techniques to exploit SQL injection vulnerabilities.
Outcome:	The proposed framework can produce harmful SQL statements invalidating user queries or compromise sensitive information about the database.

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Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi

Challenge:	LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints.
Approach:	They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks.
Outcome:	The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration.

WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)

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Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie Zhou

Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLMs (2025.findings-emnlp)

Challenge:	Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production.
Approach:	They propose to use a golden corpus annotated by experts to generate a translation suggestion model.
Outcome:	The proposed model improves on the golden corpus annotated by translators on four translation directions.

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Chenkun Tan, Pengyu Wang, Shaojun Zhou, Botian Jiang, Zhaowei Li, Dong Zhang, Xinghao Wang, Yaqian Zhou, Xipeng Qiu

TopViewRS: Vision-Language Models as Top-View Spatial Reasoners (2024.emnlp-main)

Challenge:	Recent advances in multimodal large language models focus on improving performance . however, language prior conflict leads to suboptimal vision-language alignment .
Approach:	They propose a method to decouple the alignment process from language prior interference . they use a proxy LLM to detach from language interference during pretraining .
Outcome:	The proposed method improves training performance and generalizes training data.

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Chengzu Li, Caiqi Zhang, Han Zhou, Nigel Collier, Anna Korhonen, Ivan Vulić

LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)

Challenge:	Top-view perspective is a typical way in which humans read and reason over different types of maps, but spatial reasoning capabilities of modern VLMs in this setup remain unattested and underexplored.
Approach:	They introduce a top-view spatial reasoning dataset and use it to evaluate VLMs across 4 perception and reasoning tasks with different levels of complexity.
Outcome:	The proposed model can understand and reason over spatial relations from the top view and can be controlled at different granularities of spatial reasoning.

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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li

Causal Reasoning of Entities and Events in Procedural Texts (2023.findings-eacl)

Challenge:	Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS).
Approach:	They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning.
Outcome:	The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks.

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Li Zhang, Hainiu Xu, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora, Chris Callison-Burch

DORA: Dynamic Optimization Prompt for Continuous Reflection of LLM-based Agent (2025.coling-main)

Challenge:	Existing work on entity state tracking or event reasoning is limited to procedural texts.
Approach:	They propose a benchmark for causal reasoning of event plausibility and entity states . they represent entities as programming languages while prompting language models .
Outcome:	The proposed model outperforms existing models on human reasoning and event reasoning.

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Kun Li, Tingzhang Zhao, Wei Zhou, Songlin Hu

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment (2023.emnlp-main)

Challenge:	Existing studies have shown that reflection can enhance performance, but our investigation reveals an undesirable pattern in reflection framework: effective self-reflection occurs primarily at the beginning of iterations, with subsequent attempts failing to produce further improvements.
Approach:	They propose a framework that generates task-adaptive reflection advice using an external open-source small language model.
Outcome:	The proposed framework generates task-adaptive and diverse reflection advice in MiniWoB++ and Alfworld environments.

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Sky CH-Wang, Arkadiy Saakyan, Oliver Li, Zhou Yu, Smaranda Muresan

RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)

Challenge:	Current research on developing computational models of social norms has focused on American society.
Approach:	They propose to leverage a Chinese Q&A platform and a socialchiemistry dataset as proxies for contrasting cultural axes and align social situations cross-culturally.
Outcome:	The proposed model can reason across cultures using a Chinese Q&A platform and the existing socialChemistry dataset.

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Jiahui Li, Lin Li, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou, Cheng Yang

ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)

Challenge:	Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences.
Approach:	They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model.
Outcome:	The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model.

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Yuechi Zhou, Chuyue Zhou, Jianxin Zhang, Juntao Li, Min Zhang

Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)

Challenge:	Existing research has demonstrated that contrast decoding of two different models can improve text quality in open-ended text generation but with limited gains on reasoning tasks.
Approach:	They propose a framework that dynamically disentangles noise in shallow layers from critical signals in deep layers to enhance reasoning ability.
Outcome:	The proposed framework improves answer accuracy while maintaining inference efficiency.

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Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou

THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption (2022.findings-acl)

Challenge:	Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect.
Approach:	They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation.
Outcome:	The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification.

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Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, Furu Wei

VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning (2025.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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Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu, Ming Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li

A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)

Challenge:	Existing studies have improved the performance of Large language models on well-defined mathematical benchmarks, but they often overlook ill-defined problems.
Approach:	They develop a large-scale benchmark that contains over 5,000 ill-defined mathematical problems.
Outcome:	The proposed framework improves the accuracy of identifying unsolvable problems by at least 12% across different LLMs, thus achieving stronger robust mathematical reasoning ability.

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Houquan Zhou, Zhenghua Li, Bo Zhang, Chen Li, Shaopeng Lai, Ji Zhang, Fei Huang, Min Zhang

Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots (P18-2)

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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Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou

Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)

Challenge:	Existing methods to learn matching models for retrieval-based chatbots are lacking.
Approach:	They propose a method that uses a sequence-to-sequence architecture model as a weak annotator to judge the matching degree of unlabeled pairs and performs learning with both the weak signals and the unlabed data.
Outcome:	The proposed method improves on two public data sets on matching models on retrieval-based chatbots.

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Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao

Automated Essay Scoring via Pairwise Contrastive Regression (2022.coling-1)

Challenge:	Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody.
Approach:	They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content.
Outcome:	The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm.

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Jiayi Xie, Kaiwei Cai, Li Kong, Junsheng Zhou, Weiguang Qu

Challenge:	Existing approaches to automate essay scoring use regression or ranking objectives . a novel neural pairwise ranking model is developed to optimize both objectives based on the same loss .
Approach:	They propose a novel Neural Pairwise Contrastive Regression model that optimizes both objectives simultaneously as a single loss.
Outcome:	The proposed model outperforms previous methods on the public Automated Student Assessment Prize dataset.

QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models (2025.findings-naacl)

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Changhai Zhou, Yuhua Zhou, Yibin Wang, Shijie Han, Qian Qiao, Hongguang Li

Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)

Challenge:	Structured pruning can reduce model size but results in significant accuracy degradation . quantization and pruning increase the difficulty of fine-tuning, requiring a more refined quantization scheme.
Approach:	They propose a structured pruning framework followed by a layer-wise mixed-precision quantization scheme to reduce model memory consumption during fine-tuning and inference.
Outcome:	Experiments on benchmark datasets show that QPruner outperforms existing methods in memory savings while maintaining or improving model performance.

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Lei Li, Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Yi Yuan, Zheng Wei, Zang Li

Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)

Challenge:	Existing methods for enhancing the performance of large language models require expensive manual annotations.
Approach:	They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence.
Outcome:	The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method.

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Weinan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, Ting Liu

Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)

Challenge:	Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses.
Approach:	They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses.
Outcome:	The proposed model outperforms all baselines on automatic and human evaluation on two public datasets.

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Linyu Fan, Wu Wu Yiheng, Jun Xie, Junhui Li, Fang Kong, Guodong Zhou

Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)

Challenge:	Recent studies on AMR parsing often regard this task as a seq2seq translation problem.
Approach:	They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding.
Outcome:	The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0.

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Zhihao Zhang, Sophia Yat Mei Lee, Dong Zhang, Shoushan Li, Guodong Zhou

GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)

Challenge:	Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences.
Approach:	They propose an entity-aligned translation approach to align entities between NSL and English .
Outcome:	The proposed approach aims to transfer knowledge from high-resource languages to low-resourced languages.

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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao

LIONs: An Empirically Optimized Approach to Align Language Models (2024.emnlp-main)

Challenge:	Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning.
Approach:	They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis.
Outcome:	The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection.

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Xiao Yu, Qingyang Wu, Yu Li, Zhou Yu

MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024.acl-long)

Challenge:	Recent studies have focused on aligning large language models with pre-trained datasets.
Approach:	They conduct a rigorous analysis of a three-stage training pipeline using sequence packing, loss masking and increasing the preference dataset size in DPO to improve the performance of language models.
Outcome:	The proposed models outperform the official instruct models tuned with closed-source data and algorithms.

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Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou

Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)

Challenge:	In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective.
Approach:	They propose to fine-tune data augmentation by query evolution and diverse reasoning paths.
Outcome:	The proposed model achieves new state-of-the-art on GSM8K and MATH.

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Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang

Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)

Challenge:	Existing methods for retrieving historical messages are based on similarity-based mechanisms.
Approach:	They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection.
Outcome:	The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S.

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Hanzhang Yuan, Mengxuan Hu, Wenhao Zhang, Tianlong Wang, Zhongliang Zhou, Jiasen Lu, Sheng Li

On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)

Challenge:	Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts.
Approach:	They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy.
Outcome:	The proposed method selectively removes less informative tokens while maintaining performance.

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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou

Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts (2025.naacl-short)

Challenge:	Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing.
Approach:	They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability.
Outcome:	The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing.

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Kun Qian, Maximillian Chen, Siyan Li, Arpit Sharma, Zhou Yu

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection (2025.findings-acl)

Challenge:	Training conversational question-answering systems requires in-domain data, which is often scarce in practice.
Approach:	They propose a bottom-up approach where QA pairs are generated first and combined into a coherent dialogue.
Outcome:	The proposed approach produces more realistic and higher-quality dialogues compared to top-down methods.

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Fanxiao Li, Jiaying Wu, Canyuan He, Wei Zhou

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

Challenge:	Multimodal large language models have demonstrated impressive capabilities in visual reasoning and text generation.
Approach:	They propose a multimodal large language model that captures deeper relationships between images and text . they propose CMIE, which uses a Coexistence Relationship Generation strategy and an AS mechanism to detect misinformation.
Outcome:	The proposed framework outperforms existing methods in detecting out-of-context misinformation.

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study (2025.emnlp-main)

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

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Yizheng Sun, Hao Li, Chang Xu, Hongpeng Zhou, Chenghua Lin, Riza Batista-Navarro, Jingyuan Sun

Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)

Challenge:	Current acceleration evaluations focus on minimal overall performance degradation . however, accelerated models can exhibit significant changes in instance-level predictions .
Approach:	They investigate whether accelerated vision-Language Models can still give the same answers as before . they found that accelerated models changed original answers up to 20% of the time .
Outcome:	The results show that accelerated models changed their original answers up to 20% of the time.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)

Challenge:	Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases.
Approach:	They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin.
Outcome:	The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen.

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Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei Li

Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)

Challenge:	Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent.
Approach:	They propose a probabilistic model for document-level relation extraction by learning logic rules.
Outcome:	The proposed model outperforms baseline models in relation performance and logical consistency.

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Houquan Zhou, Yang Li, Zhenghua Li, Min Zhang

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges (2025.findings-acl)

Challenge:	Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance.
Approach:	They propose a Guassian HMM variant for unsupervised POS tagging that incorporates contexualized word representations into the decoder.
Outcome:	The proposed model outperforms state-of-the-art models on Penn Treebank and multilingual Universal Dependencies treebank v2.0.

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Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees (2026.acl-long)

Challenge:	Privacy risks in text-only Large Language Models are well-documented, especially their tendency to memorize and leak sensitive information.
Approach:	They propose a dataset to assess privacy risks across multi-modal tasks and scenarios . they demonstrate how models leak sensitive data across various tasks .
Outcome:	The proposed model can leak sensitive data embedded in images or stored in memory, exposing privacy risks.

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Kun Li, Zenan Xu, Junan Li, Zengrui Jin, Jinghao Deng, Zexuan Qiu, Bo Zhou

Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss (2025.acl-long)

Challenge:	Existing approaches to augment Large Language Models (LLMs) with computational capabilities have focused on short Chain-of-thought (CoT) integrating tool-use into long CoT remains underexplored due to the scarcity of training data and the challenge of integrating it without compromising the model’s intrinsic long-chain reasoning.
Approach:	They propose a framework that enables spontaneous tool-use during long CoT reasoning without additional human annotation.
Outcome:	Experiments on AIME and GPQA-Diamond show that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning.

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Liang Zhang, Ziyao Lu, Fandong Meng, Hui Li, Jie Zhou, Jinsong Su

Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)

Challenge:	Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLM are required to continuously acquire new tasks.
Approach:	They propose a Sparse Mixture of Expert (SMoE) based method for domain-incremental CIT in Multimodal Large Language Models (MLLMs) . they equip the SMoA module with a domain-specific autoregressive loss (DSAL) they establish a new benchmark to evaluate the efficacy of their method .
Outcome:	The proposed method outperforms all baselines and is based on a Sparse Mixture of Experts (SMoE) module .

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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)

Challenge:	In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability.
Approach:	They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard.
Outcome:	The proposed methods significantly improve performance on six datasets.

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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li

Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)

Challenge:	Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache.
Approach:	They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization .
Outcome:	The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models .

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Yao Wan, Yang Wu, Zhen Li, Guobiao Zhang, Hongyu Zhang, Zhou Zhao, Hai Jin, April Wang

Challenge:	Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages.
Approach:	They propose a sign language interface that enables the DHH community to engage more fully with data analysis.
Outcome:	The proposed interface can be used by the deaf and hard-of-hearing community.

The Stochastic Parrot on LLM’s Shoulder: A Summative Assessment of Physical Concept Understanding (2025.naacl-long)

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Mo Yu, Lemao Liu, Junjie Wu, Tsz Ting Chung, Shunchi Zhang, Jiangnan Li, Dit-Yan Yeung, Jie Zhou

Challenge:	Recent years have witnessed remarkable advancements in large language models (LLMs) many researchers argue that LLMs may not * Equal contribution.
Approach:	They propose a task that summarises the memorization issue by using grid inputs that abstractly describe physical phenomena.
Outcome:	The proposed task alleviates the memorization issue by using grid-format inputs that abstractly describe physical phenomena.

CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher (2026.acl-long)

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Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li

Contrastive Token-Wise Meta-Learning for Unseen Performer Visual Temporal-Aligned Translation (2023.findings-acl)

Challenge:	Existing models for text understanding fail to adapt to domain shifts in real-world applications . current models do not improve themselves as they are applied to new domains .
Approach:	They propose a continual test-time adaptation framework that adapts to evolving domains . they propose accumulating domains and a refine-then-filter framework to calibrate teacher predictions .
Outcome:	The proposed model excels in a teacher-student framework adaptable to evolving domains.

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Linjun Li, Tao Jin, Xize Cheng, Ye Wang, Wang Lin, Rongjie Huang, Zhou Zhao

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

Challenge:	a novel generalization framework for visual temporal-aligned translation is proposed to transfer recognition skills to unseen performers . ambiguity in the visual sequence can hinder current methods for visual language translation .
Approach:	They propose a generalizable framework to transfer recognition skills to unseen performers . they use visual temporal-aligned translation to generate multiple words autoregressively .
Outcome:	The proposed framework is generalized to transfer recognition skills to unseen performers . it is compared with existing methods on lipreading and fingerspelling datasets .

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

TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering (2024.emnlp-main)

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

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Xinying Qian, Ying Zhang, Yu Zhao, Baohang Zhou, Xuhui Sui, Li Zhang, Kehui Song

Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision (2026.findings-acl)

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal knowledge graphs (TKTs).
Approach:	They propose a Time-aware retrieve-rewrite-retrieve-rerank framework to integrate temporal knowledge from TKGs into Large Language Models (LLMs) to reduce temporal hallucination, they propose rewrite module to rew questions using background knowledge stored in TKG's, then implement a retrieve-rank module to retrieve semantically and temporally relevant facts from Tkgs and rerank them according to temporal constraints.
Outcome:	The proposed approach achieves relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs.

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Chentao Li, Zirui Gao, Mingze Gao, Yinglian Ren, Jianjiang Feng, Jie Zhou

Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing (2024.acl-long)

Challenge:	Egocentric AI agents rely on pointing to resolve referential ambiguities in natural language commands.
Approach:	They propose a question-answering benchmark to evaluate and enhance pointing reasoning in egocentric views.
Outcome:	The proposed benchmark evaluates and enhances pointing reasoning in egocentric views.

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Chengjie Zhou, Bobo Li, Hao Fei, Fei Li, Chong Teng, Donghong Ji

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

Challenge:	Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency graph parsing due to the internal structures of spans neglected.
Approach:	They propose to use latent spans as latent subtrees to model internal structures of spans and leverage TreeCRFs to extract the complete opinion tuple from a sentence.
Outcome:	The proposed method performs significantly better than all previous bi-lexical methods, achieving new state-of-the-art.

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Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency (2021.findings-acl)

Challenge:	End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach:	They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome:	The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

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Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou

LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models (2026.acl-long)

Challenge:	Existing methods to evaluate consistency capacity of open-domain chatbots are costly and low-efficient.
Approach:	They propose an efficient framework for evaluating consistency of open-domain chatbots . they use human judges to interact with chatbot, which is costly and low-efficient .
Outcome:	The proposed framework can assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation.

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Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li

Jailbreaking? One Step Is Enough! (2025.acl-long)

Challenge:	Masked diffusion language models have achieved significant progress in language modeling . however, the systematic analysis and empirical validation of their alignment on general tasks remains underexplored.
Approach:	They propose a framework that analyzes the bias and variance of preference optimization loss and gradient based on Direct Preference Optimization.
Outcome:	The proposed model outperforms its SFT-only predecessor on general benchmarks . it consistently outperformed other strong language models and ARMs on general tasks .

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Weixiong Zheng, Peijian Zeng, YiWei Li, Hongyan Wu, Nankai Lin, Junhao Chen, Aimin Yang, Yongmei Zhou

FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)

Challenge:	Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs.
Approach:	They propose a Reverse Embedded Defense Attack mechanism that disguises the attack intention as the "defense" intention against harmful content.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and enables successful jailbreak in one iteration.

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Junliang He, Ziyue Fan, Shaohui Kuang, Li Xiaoqing, Kai Song, Yaqian Zhou, Xipeng Qiu

RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models (2021.emnlp-main)

Challenge:	Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation.
Approach:	They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs.
Outcome:	The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version.

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Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, Xu Sun

DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLMs Jailbreakers (2024.findings-emnlp)

Challenge:	Backdoor attacks are a serious threat to the safety of reusing deep neural networks (DNNs).
Approach:	They propose an efficient online defense mechanism based on robustness-aware perturbations to distinguish poisoned and clean samples to defend against backdoor attacks on natural language processing models.
Outcome:	The proposed method achieves better defending performance and lower computational costs than existing defense methods.

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Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh

SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)

Challenge:	Existing jailbreaking methods view a malicious prompt as a whole but they are not effective at reducing LLMs’ attention on combinations of words with malice.
Approach:	They propose an automatic prompt Decomposition and Reconstruction framework for jailbreaking Attack that decomposes a malicious prompt into separate sub-prompts and reassembles them implicitly by In-Context Learning.
Outcome:	The proposed framework reduces LLMs' attention on malice words by presenting them to LLM in a fragmented form, addressing these limitations and improving attack effectiveness.

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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)

Challenge:	Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data.
Approach:	They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations.
Outcome:	The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability.

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Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li

ATLAS: Agent Tuning via Learning Critical Steps (2025.findings-acl)

Challenge:	Figures of speech often deviate from their literal meanings to express deeper semantic implications.
Approach:	They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition.
Outcome:	The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models .

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Zhixun Chen, Ming Li, Yuxuan Huang, Yali Du, Meng Fang, Tianyi Zhou

Affective Idiosyncratic Responses to Music (2022.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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Sky CH-Wang, Evan Li, Oliver Li, Smaranda Muresan, Zhou Yu

Towards Emotional Support Dialog Systems (2021.acl-long)

Challenge:	Affective responses to music are highly personal, but it's difficult to measure marginal effects of these variables . a study of 403M listener comments on a social music platform in china aims to address this gap .
Approach:	They propose to measure affective responses to music from 403M listener comments on a Chinese social music platform.
Outcome:	The proposed method identifies musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses from over 403M listener comments on a Chinese social music platform.

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Siyang Liu, Chujie Zheng, Orianna Demasi, Sahand Sabour, Yu Li, Zhou Yu, Yong Jiang, Minlie Huang

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents (2025.emnlp-main)

Challenge:	Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats.
Approach:	They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems.
Outcome:	The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models.

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Hengyu An, Jinghuai Zhang, Tianyu Du, Chunyi Zhou, Qingming Li, Tao Lin, Shouling Ji

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

Challenge:	Existing methods for detecting Indirect Prompt Injection (IPI) attacks rely on assumptions about the model's inherent security, which lacks structural constraints on agent behaviors.
Approach:	They propose a novel task execution paradigm that models the agents’ task execution process as a traversal over a planned Tool Dependency Graph (TDG).
Outcome:	The proposed model reduces unintended tool invocations triggered by injected instructions, enhancing robustness against IPI attacks.

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

MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)

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

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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

Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs (2025.findings-emnlp)

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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Yanhong Li, Zixuan Lan, Jiawei Zhou

Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events (2026.acl-long)

Challenge:	visual text representations are a practical and surprisingly effective form of input compression for decoder LLMs.
Approach:	They exploit visual representations to render long text inputs as a single image and provide it directly to the model.
Outcome:	The proposed method reduces token usage while preserving performance.

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Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin

Challenge:	A critical bottleneck is the lack of ground-truth human data to link personality traits to emotional shifts.
Approach:	They propose a large-scale dataset to capture reader-based emotional variations across news, social media, and life narratives.
Outcome:	The proposed model captures reader-based emotional variations across news, social media, and life narratives.

Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)

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Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou

Challenge:	Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives .
Approach:	They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations.
Outcome:	The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus.

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao

NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)

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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Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, Zhiyuan Liu

How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)

Challenge:	Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting.
Approach:	They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset.
Outcome:	The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset.

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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou

ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom (2025.emnlp-main)

Challenge:	supervised fine-tuning (SFT) is a technique used to enhance multiple abilities in large language models.
Approach:	They propose to study the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during supervised fine-tuning.
Outcome:	The proposed model improves math reasoning and code generation with increasing data amount . the proposed model size and SFT strategies can be used to learn multiple skills with different scaling patterns.

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Jingqi Zhou, Sheng Wang, Jingwei Dong, Kai Liu, Lei Li, Jiahui Gao, Jiyue Jiang, Lingpeng Kong, Chuan Wu

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities (2023.findings-emnlp)

Challenge:	Large vision-language models often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation.
Approach:	They propose a visual reasoning framework that decouples vision-reasoning capabilities and multi-run proactive perception.
Outcome:	The proposed framework outperforms existing models on benchmarks for open-source and closed-source models with 13.2% performance gain.

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Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, Xipeng Qiu

MMAD:Multi-modal Movie Audio Description (2024.lrec-main)

Challenge:	Existing multi-modal large language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer.
Approach:	They propose a large language model with intrinsic cross-modal conversational abilities . they construct a cross-text speech instruction dataset and employ a three-stage training strategy .
Outcome:	The proposed model can follow cross-modal human instructions and handle multiple modalities with one model.

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Xiaojun Ye, Junhao Chen, Xiang Li, Haidong Xin, Chao Li, Sheng Zhou, Jiajun Bu

Bridging Modality Gap for Effective Multimodal Sentiment Analysis in Fashion-related Social Media (2025.coling-main)

Challenge:	Current methods of creating accessible movies rely on manual work, resulting in high costs and limited scalability.
Approach:	They propose a multi-modal movie audio description pipeline that generates narrations of information that is not accessible through unimodal hearing in movies.
Outcome:	The proposed pipeline surpasses existing baselines in performance on widely used datasets.

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Zheyu Zhao, Zhongqing Wang, Shichen Li, Hongling Wang, Guodong Zhou

Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)

Challenge:	Existing sentiment analysis tasks focus on text comprehension, but visual content is important for emotional expression.
Approach:	They propose a multimodal framework that integrates information from various modalities for sentiment classification of fashion posts.
Outcome:	The proposed framework outperforms existing unimodal and multimodal baselines on a comprehensive dataset and significantly outperformed existing unilmodal and multiple modal frameworks.

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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel

Skill Weaving: Efficient LLM Improvement via Modular Skillpacks (2026.findings-acl)

Challenge:	Currently, the Transformer is the de facto architecture of choice for processing sequential data.
Approach:	They evaluate the Transformer architecture and its modifications in a shared experimental setting . they conjecture that performance improvements may strongly depend on implementation details .
Outcome:	The proposed improvements do not significantly improve performance, the authors find . the proposed improvements are either developed in the same codebase or are minor changes .

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Zhuo Li, Guodong DU, Zesheng Shi, Weiyang Guo, Weijun Yao, Yuan Zhou, Jiabo Zhang, Jing Li

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) can specialize under fixed memory and inference budgets, but they struggle to achieve high performance across heterogeneous domains.
Approach:	They propose a modular improvement framework that partitions full capabilities of a general-purpose model into domain-specific delta modules that reorganize and refine the model's internal knowledge.
Outcome:	The proposed framework outperforms monolithic models on multi-task and agentic benchmarks and achieves up to 4 speedup.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

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

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

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

PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning (2024.findings-emnlp)

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

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Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, Dongmei Zhang

Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)

Challenge:	Recent advances in fine-tuning large language models have greatly enhanced their usage in domain-specific tasks.
Approach:	They propose a method which internalizes prompt knowledge during model fine-tuning to achieve efficient inference and save costs.
Outcome:	The proposed approach reduces input tokens by 90%, accelerates inference by 4.2 times, and reduces monetary inference costs by 88.3%.

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Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)

Challenge:	Text-based question answering (TBQA) has been studied extensively in recent years.
Approach:	They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them.
Outcome:	The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains.

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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)

Challenge:	Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach:	They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome:	The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

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Liqun Liu, Funan Mu, Pengyu Li, Xin Mu, Jing Tang, Xingsheng Ai, Ran Fu, Lifeng Wang, Xing Zhou

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)

Challenge:	NeuralClassifier is a toolkit for hierarchical multi-label text classification.
Approach:	They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model .
Outcome:	The proposed model achieves comparable performance with reported results in the literature.

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Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang

Mitigating Spurious Correlations via Counterfactual Contrastive Learning (2025.findings-emnlp)

Challenge:	Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data.
Approach:	They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention.
Outcome:	The proposed neural network improves the state-of-the-art on the NYT dataset .

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Fengxiang Cheng, Chuan Zhou, Xiang Li, Alina Leidinger, Haoxuan Li, Mingming Gong, Fenrong Liu, Robert Van Rooij

Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions (2024.acl-long)

Challenge:	Existing methods to distinguish causally related words from spurious correlations are limited by the number of causally correlated words in a sentence.
Approach:	They propose to use probabilistic probability of necessity and probability of sufficiency to identify causal relationships rather than spurious correlations between words and class labels.
Outcome:	The proposed method is based on a contrastive learning approach name CPNS and is validated on public datasets.

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Liyan Xu, Jiangnan Li, Mo Yu, Jie Zhou

Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation (P19-1)

Challenge:	a novel graph for narrative comprehension captures coherence between passages in narratives . end-to-end paradigms are effective for comprehension tasks, but may not be sufficient for all comprehension scenarios.
Approach:	They propose a graph dubbed NarCo which explicitly depicts task-agnostic coherence dependencies that are ready to be consumed by downstream tasks.
Outcome:	The proposed graph is practically instantiated by LLMs without human annotations.

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Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, Xu Sun

Alignment with Fill-In-the-Middle for Enhancing Code Generation (2025.emnlp-main)

Challenge:	Existing methods for table-to-text generation use encoder-decoder framework, but lack of large parallel data is a problem for many domains.
Approach:	They propose a model to separate table-to-text generation into two stages: key fact prediction and surface realization.
Outcome:	The proposed model achieves 27.34 BLEU score with only 1,000 parallel data, while the baseline model only achieves 9.71 BLUE score.

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Houxing Ren, Zimu Lu, Weikang Shi, Haotian Hou, Yunqiao Yang, Ke Wang, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li

Investigating Context Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style (2025.findings-acl)

Challenge:	Existing methods for generating test cases with limited training data are not reliable and may be counterproductive.
Approach:	They propose a method that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases.
Outcome:	The proposed approach shows significant improvements in code generation tasks on benchmark datasets such as HumanEval (+), MBPP (+), and APPS.

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Yuepei Li, Kang Zhou, Qiao Qiao, Bach Nguyen, Qing Wang, Qi Li

A Multi-Persona Chatbot for Hotline Counselor Training (2020.findings-emnlp)

Challenge:	Retrieval-augmented generation improves Large Language Models (LLMs) by integrating external information into the response generation process.
Approach:	They investigate the impact of memory strength and evidence presentation on LLMs’ receptiveness to external evidence by measuring the divergence in LLM responses to different paraphrases of the same question.
Outcome:	The proposed method improves Large Language Models (LLMs) by integrating external information into the response generation process.

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Orianna Demasi, Yu Li, Zhou Yu

A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification (2022.emnlp-main)

Challenge:	a chatbot cannot replace a counselor, but a simulation of intimate situations is needed to train counselors.
Approach:	They propose a counseling strategy annotation scheme and a multi-task framework that mimics prototype conversations to train counselors.
Outcome:	The proposed framework significantly increases response diversity and specificity, with limited impact to coherence.

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Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu

Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)

Challenge:	Existing datasets that ignore law requirements are limited to English.
Approach:	They construct a Chinese privacy policy dataset that can be used to analyze software privacy policies.
Outcome:	The proposed dataset includes 483 Chinese Android privacy policies, over 11K sentences, and 52K fine-grained annotations.

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Zhiyi Fu, Wangchunshu Zhou, Jingjing Xu, Hao Zhou, Lei Li

Enhancing the General Agent Capabilities of Low-Paramter LLMs through Tuning and Multi-Branch Reasoning (2024.findings-naacl)

Challenge:	masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations.
Approach:	They propose a representation learning approach that uses embeddings as anchors to model contextual representations.
Outcome:	The proposed model achieves 5x speedup and 1.2 points average improvement over MLM.

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Qinhao Zhou, Zihan Zhang, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li

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

Challenge:	Open-source pre-trained Large Language Models exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks.
Approach:	They propose a method to construct agent-specific data using GPT-4 and supervised fine-tuning . they find that supervised tunning can significantly reduce hallucination outputs and formatting errors in agent tasks .
Outcome:	The proposed method improves on five agent tasks of AgentBench.

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

LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search (2026.findings-acl)

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

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Xiang Li, Yucheng Zhou, Xiangzhi Wei, Zesheng Shi, Haiyuan Wan, Gong Yifan, Fangming Liu, Jing Li

AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (2022.coling-1)

Challenge:	Existing methods for enhancing inductive reasoning of large language models often lack explicit optimization guidance and effective error correction.
Approach:	They propose a plug-and-play test-time framework that integrates multi-agent coordination with Monte Carlo Tree Search to improve inductive reasoning.
Outcome:	The proposed framework outperforms existing methods on four benchmarks and shows consistent improvements on QWQ-32B and Deepseek-V3 .

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Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, Hongzhi Liu

ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks (2025.emnlp-main)

Challenge:	Existing methods to correct handwritten assignments are to use OCR to recognize characters and compare them to answers.
Approach:	They propose a multimodal approach to correct handwritten Chinese characters by combining the visual information of students' handwriting with the encoded representations of answers.
Outcome:	The proposed model outperforms OCR-based methods by a large margin.

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Heng Zhou, Hejia Geng, Xiangyuan Xue, Li Kang, Yiran Qin, Zhiyong Wang, Zhenfei Yin, Lei Bai

LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing (2021.acl-demo)

Challenge:	Multi-agent systems (MAS) are limited by poor flexibility and scalability, with underdeveloped optimization strategies.
Approach:	They propose a task graph generation and a reward-driven two-stage agent selection process to integrate multi-agent systems to improve their reasoning capabilities.
Outcome:	The proposed model outperforms existing methods on Math-MAS and SciBench-MAS SciBech, while other methods completely fail.

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Yu Li, Josh Arnold, Feifan Yan, Weiyan Shi, Zhou Yu

CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning (2026.acl-long)

Challenge:	Currently, researchers use automatic metrics and human evaluation to evaluate dialogue systems.
Approach:	They propose to use a Python API to easily evaluate dialogue systems using Amazon Mechanical Turk.
Outcome:	The open-source toolkit provides a fast, consistent method for reproducing human evaluation results.

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Zhenpeng Su, Leiyu Pan, Minxuan Lv, Yuntao Li, Wenping Hu, Fuzheng Zhang, Kun Gai, Guorui Zhou

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

Challenge:	Existing methods for proximal policy optimization discard valuable gradient signals from low-probability tokens due to the clipping mechanism.
Approach:	They propose an algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner.
Outcome:	The proposed algorithm outperforms strong baselines on reasoning benchmarks on different model scales.

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)

Challenge:	relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach:	They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome:	The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Yong Shan, Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Cheng Niu, Jie Zhou

A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)

Challenge:	Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots.
Approach:	They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations.
Outcome:	The proposed approach achieves 52.68% and 58.55% joint accuracy on multiWOZ 2.0 and MultiWOZ 2.1 datasets and significantly improves performance (+1.24% and +5.98%)

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Hongxiao Zhang, Mingtong Liu, Chunyou Li, Yufeng Chen, Jinan Xu, Ming Zhou

Rethinking the Multimodal Correlation of Multimodal Sequential Learning via Generalizable Attentional Results Alignment (2024.acl-long)

Challenge:	Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge .
Approach:	They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data.
Outcome:	The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge.

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Tao Jin, Wang Lin, Ye Wang, Linjun Li, Xize Cheng, Zhou Zhao

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

Challenge:	Existing studies have focused on the alignment of multimodal sequential learning using transformers.
Approach:	They propose a constrained scheme to align the multiple attentional results from both local and global perspectives.
Outcome:	The proposed scheme could align the multiple attentional results from both local and global perspectives, making the information capture more efficient.

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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang

Challenge:	Existing methods for training effective AI agents often resort to synthetic data generation.
Approach:	They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance .
Outcome:	The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset.

AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)

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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian

Challenge:	Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs).
Approach:	They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say .
Outcome:	The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels.

LSSF: Safety Alignment for Large Language Models through Low-Rank Safety Subspace Fusion (2025.acl-long)

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Guanghao Zhou, Panjia Qiu, Cen Chen, Hongyu Li, Jason Chu, Xin Zhang, Jun Zhou

CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)

Challenge:	Existing safety alignment methods rely on fine-tuning, which inadvertently leads to the increased complexity and computational resources required.
Approach:	They propose a safety re-alignment framework with Low-Rank Safety Subspace Fusison that exploits low-rank safety characteristics of LLMs by constructing a low-ranked projection matrix to extract the principal components of safety vectors.
Outcome:	The proposed method exploits low-rank safety subspace of the LLMs and is stable during fine-tuning process and is isolated from the model’s general capabilities.

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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan

RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction (2022.acl-srw)

Challenge:	Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing .
Approach:	They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce.
Outcome:	The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks.

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Zhe Li, Luoyi Fu, Xinbing Wang, Haisong Zhang, Chenghu Zhou

Vision Language Model Helps Private Information De-Identification in Vision Data (2025.findings-acl)

Challenge:	Existing methods for relational triple extraction ignore semantic information of relations or predict subjects and objects sequentially.
Approach:	They propose a relation-first blank filling network to capture semantic information of relations . they transform relations into relation templates with blanks which contain the fine-grained semantic representation of relations.
Outcome:	The proposed model outperforms current state-of-the-art methods on public benchmark datasets.

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Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)

Challenge:	Visual Language Models (VLMs) have gained popularity due to their ability to solve imagerelated tasks.
Approach:	They propose a framework to enhance privacy awareness of visual language models . they use a specialized instruction-tuning dataset and a tailored training methodology .
Outcome:	The proposed framework outperforms existing approaches in handling private information.

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Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety (2026.findings-acl)

Challenge:	Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance .
Approach:	They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences .
Outcome:	The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs.

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Changxuan Fan, Xi Yang, Yueyuan Zheng, Bin Zhou, Yuanping Wang, Wenbin Hu, Huihao Jing, Ki Sen Hung, Dazhao Du, Haoran Li, Janet Hui-wen Hsiao, Yangqiu Song

Multi-Source Probing for Open-Domain Conversational Understanding (2023.emnlp-main)

Challenge:	a survey of older adults shows that many LLMs mishandle elderly-specific contextual risks.
Approach:	They propose a framework to assess elderly-specific contextual risks in LLM interactions . they use a taxonomy to identify 50 fine-grained risk types across mental well-being, financial, medical, toxicity, and privacy domains .
Outcome:	a new framework assesses elderly-specific contextual risks in LLM interactions . it achieves 96.2% and 90.9% unsafe-prompt detection accuracy, respectively .

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Yuanxi Li, Hao Zhou, Jie Zhou, Minlie Huang

Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning (2024.emnlp-main)

Challenge:	Existing models of open-domain dialogue comprehension have limited conversational understanding and response generation.
Approach:	They propose a multi-source probing method to probe dialogue comprehension abilities of open-domain dialogue models.
Outcome:	The proposed method aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner consistent with dialogue model pre-training to leverage model capabilities.

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Jiahui Li, Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou

On the Faithfulness for E-commerce Product Summarization (2020.coling-main)

Challenge:	Recent research has developed algorithms for reinforcement learning from human feedback and AI-generated feedback.
Approach:	They propose a method for reinforcement learning from human feedback and AI-generated feedback that incorporates weighting, ranking, and constraining to handle disparate rewards.
Outcome:	The proposed method reduces disparity and enhances stability among rewards . empirical results show that the proposed method is efficient and straightforward .

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Peng Yuan, Haoran Li, Song Xu, Youzheng Wu, Xiaodong He, Bowen Zhou

One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)

Challenge:	e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility .
Approach:	They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying .
Outcome:	The proposed model significantly improves the faithfulness of e-commerce product summarization tasks.

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Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, Guodong Zhou

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis (2025.acl-long)

Challenge:	Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal .
Approach:	They propose a novel QA matching approach using informal text from a product review site.
Outcome:	The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text.

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Junzhuo Li, Bo Wang, Xiuze Zhou, Peijie Jiang, Jia Liu, Xuming Hu

AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)

Challenge:	Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures.
Approach:	They propose to analyze sparse MoE architectures against dense models to capture dynamic routing-expert interactions.
Outcome:	The proposed algorithm shows that sparse models achieve higher efficiency per layer . it also shows that deep Qwen-MoE mitigates expert failures while minimizing complexity .

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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang

ToW: Thoughts of Words Improve Reasoning in Large Language Models (2025.naacl-long)

Challenge:	Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns.
Approach:	They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback.
Outcome:	The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy.

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Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, Ben Zhou

Cross-Domain Sentiment Classification using Semantic Representation (2022.findings-emnlp)

Challenge:	Unlike other data augmentation methods, thoughts of words (TOW) views next-word prediction as a core reasoning task and injects fine-grained thoughts into pre-training texts.
Approach:	They propose a training-time data-augmentation method called thoughts of words (TOW) that injects fine-grained thoughts directly into a next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts.
Outcome:	The proposed method reduces model hallucination by 10% and improves reasoning performance by 7% to 9% on average.

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Shichen Li, Zhongqing Wang, Xiaotong Jiang, Guodong Zhou

Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)

Challenge:	Existing studies on cross-domain sentiment classification ignore the semantic relevance between domains.
Approach:	They propose to use Abstract Meaning Representation to help with cross-domain sentiment classification by combining sentence-level AMRs with text-graph interaction models.
Outcome:	The proposed model is effective over strong baselines and shows its importance over strong models.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao

Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)

Challenge:	Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach:	VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome:	Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.

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Qiang Gao, Bobo Li, Zixiang Meng, Yunlong Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

WebQuality: A Large-scale Multi-modal Web Page Quality Assessment Dataset with Multiple Scoring Dimensions (2025.naacl-long)

Challenge:	Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies.
Approach:	They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents.
Outcome:	The proposed model outperforms baseline models on English and Chinese datasets by large margins.

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Tao Zhang, Yige Wang, ZhuHangyu ZhuHangyu, Li Xin, Chen Xiang, Tian Hua Zhou, Jin Ma

VoxMind: An End-to-End Agentic Spoken Dialogue System (2026.acl-long)

Challenge:	Existing studies on web page quality assessment neglect the aspect of web page content.
Approach:	They propose a Chinese dataset for web page quality assessment . the dataset includes over 65,000 detailed an-notations spanning four sub-dimensions .
Outcome:	The proposed dataset includes over 65,000 detailed an-notations spanning four sub-dimensions and incorporates elements such as HTML+CSS, text, and visual screenshot.

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Tianle Liang, Yifu Chen, Shengpeng Ji, Yijun Chen, Zhiyang Jia, Jingyu Lu, Fan Zhuo, Xueyi Pu, Yangzhuo Li, Zhou Zhao

Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators (N18-2)

Challenge:	Existing research on end-to-end spoken dialogue models has focused on core perception and generation, with limited exploration of tool-augmented extensions.
Approach:	They propose a framework to equip end-to-end spoken dialogue models with comprehensive agentic abilities by leveraging a 470-hour AgentChat dataset.
Outcome:	The proposed framework outperforms Gemini-2.5-Pro on spoken agent tasks while maintaining general conversational quality.

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Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li, Forrest Sheng Bao

Challenge:	Recent studies on review helpfulness prediction require labeled samples for each domain/category of interest.
Approach:	They propose a convolutional neural network based model which leverages word-level and character-based representations to transfer knowledge between domains.
Outcome:	The proposed model outperforms the state-of-the-art on the Amazon product review dataset.

Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)

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Yongjing Yin, Yafu Li, Fandong Meng, Jie Zhou, Yue Zhang

Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction (2021.findings-acl)

Challenge:	Modern neural machine translation models suffer limitation in compositional generalization, resulting in weakened translation performance on unseen compounds.
Approach:	They propose to introduce categorization to the contextualized representations to improve generalization by reducing sparsity and overfitting.
Outcome:	The proposed method reduces compositional generalization error rates by 24% on a dedicated MT dataset.

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Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Text2DB: Integration-Aware Information Extraction with Large Language Model Agents (2024.findings-acl)

Challenge:	Distantly supervised relation extraction (RE) has attracted much attention in the past few years . previous methods to evaluate models manually or directly on autolabeled data have produced inaccurate evaluations .
Approach:	They propose to use distant supervision to generate large-scale autolabeled data . they build manually-annotated test sets for two DS-RE datasets and evaluate models .
Outcome:	The proposed method produces 53% wrong labels at the entity pair level in the popular NYT10 dataset.

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Yizhu Jiao, Sha Li, Sizhe Zhou, Heng Ji, Jiawei Han

Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation (2020.acl-main)

Challenge:	Current methods for information extraction (IE) focus on integrating IE output with the database . a long-overlooked question is what counts as "relevant knowledge"
Approach:	They propose a task that emphasizes integration of IE output and the database . they introduce a benchmark and an LLM agent framework for this task .
Outcome:	The proposed task integrates IE output and the target database (or knowledge base) it meets common demands such as data infilling, row population, and column addition .

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Qiu Ran, Yankai Lin, Peng Li, Jie Zhou

Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)

Challenge:	Existing non-autoregressive neural machine translation models suffer from multi-modality problem . despite their autoregressivity, most NMT models suffer with slow decoding speed .
Approach:	They propose a semi-autoregressive model which generates a translation as a sequence of segments while each segment is predicted token-by-token.
Outcome:	The proposed model can achieve 4 times speedup while maintaining comparable performance.

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Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie Zhou

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF (2026.acl-long)

Challenge:	Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences.
Approach:	They propose a graph-based Context Tracking Network to model the discourse context for IDRR.
Outcome:	The proposed model can integrate sentence-level and token-level contextual semantics better than existing models.

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Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Dongyun Xue, Wengang Zhou, Li Li, Houqiang Li

Challenge:	Existing approaches to tackling length bias are limited by their complexity or lack of a linear length-reward relation.
Approach:	They propose a framework that learns and corrects underlying bias patterns by fitting a length-reward relationship into a reward model.
Outcome:	The proposed framework improves length-controlled win rate and reduces verbosity without compromising performance.

New Frontiers of Information Extraction (2022.naacl-tutorials)

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Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)

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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Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

Byte Latent Transformer: Patches Scale Better Than Tokens (2025.acl-long)

Challenge:	Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus .
Approach:	They propose a task to predict aspects from interactive QA style reviews using an annotated corpus.
Outcome:	The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data.

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Artidoro Pagnoni, Ramakanth Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason E Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srini Iyer

GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval (2026.acl-long)

Challenge:	Existing large language models (LLMs) are trained on bytes, except for tokenization, which groups bytes into a static set of tokens.
Approach:	They propose a new byte-level LLM architecture that encodes bytes into dynamically sized patches, which serve as the primary units of computation.
Outcome:	The proposed architecture matches tokenization-based models with improvements in inference efficiency and robustness.

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Minghan Li, Tianrui Lv, Chao Zhang, Guodong Zhou

HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026.findings-acl)

Challenge:	Existing dense retrieval methods neglect the explicit legal logic that underpins legal relevance.
Approach:	They propose a framework that reformulates retrieval as an inference process over latent legal variables.
Outcome:	GLIER outperforms strong baselines like SAILER and KELLER in a legal case-based retrieval task . the framework exhibits exceptional data efficiency even when trained with only 10% of the data .

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Jin Wang, Kaiwen Luo, Liang Lin, Weiliu Wang, Yitian Chen, Moayad Aloqaily, Xuehai Tang, Zhenhong Zhou, Kun Wang, Li Sun, Qingsong Wen

Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)

Challenge:	Recent advances in audio large language models have led to their potential privacy implications unexplored.
Approach:	They propose a benchmark to examine whether ALLMs leak user privacy through acoustic voiceprints.
Outcome:	The proposed benchmark is constructed from over 22,000 real-world audio clips.

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Shuyan Zhou, Li Zhang, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch, Graham Neubig

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Existing work has treated procedures as shallow structures without modeling the parent-child relation.
Approach:	They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB .
Outcome:	The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.

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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen

Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

Challenge:	Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life.
Approach:	They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors.
Outcome:	The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development.

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Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, Lei Li

Vocabulary Learning via Optimal Transport for Neural Machine Translation (2021.acl-long)

Challenge:	Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach:	They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome:	The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.

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Jingjing Xu, Hao Zhou, Chun Gan, Zaixiang Zheng, Lei Li

Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models (2024.acl-long)

Challenge:	Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation.
Approach:	They propose a token dictionary solution that can be used without trial training to find the best dictionary with a proper size.
Outcome:	The proposed solution beats widely-used vocabularies in English-German translation, TED bilingual translation, and TED multilingual translation.

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Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new knowledge into Large Language Models (LLMs).
Approach:	They propose to evaluate LLMs with single edit only and parameter-modifying ME with parameter-preserving ME.
Outcome:	The proposed method can maintain LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format.

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

Rethinking Stealthiness of Backdoor Attack against NLP Models (2021.acl-long)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, Xu Sun

Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)

Challenge:	Existing backdoor attacks are not stealthy to system deployers or users.
Approach:	They propose a novel backdoor attack method based on negative data augmentation and modifying word embeddings that is much stealthier while maintaining pretty good attacking performance.
Outcome:	The proposed method is much stealthier while maintaining pretty good attacking performance.

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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li

GiFT: Gibbs Fine-Tuning for Code Generation (2025.acl-long)

Challenge:	Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge.
Approach:	They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage.
Outcome:	The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold.

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Haochen Li, Wanjin Feng, Xin Zhou, Zhiqi Shen

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation.
Approach:	They propose a method to fine-tune large language models with code drawn from a conditional distribution, conditioned on a specific seed description.
Outcome:	The proposed method improves performance on four datasets and shows that it can be used to fine-tune LLMs with code derived from the marginal distribution.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott

Impromptu Cybercrime Euphemism Detection (2025.coling-main)

Challenge:	FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China.
Approach:	They evaluate vision–language Models and large language models on unseen food images and corresponding questions.
Outcome:	The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions.

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Xiang Li, Yucheng Zhou, Laiping Zhao, Jing Li, Fangming Liu

Text Classification

Challenge:	Existing methods for detecting euphemisms are ineffective in impromptu euphorism detection . Existing approaches for e-mail detection are limited to word-level ephemismals .
Approach:	They propose a framework for impromptu euphemism detection that integrates context augmentation and multi-round iterative training to better predict the actual meaning of a masked token.
Outcome:	The proposed framework improves 76-fold over the previous state-of-the-art euphemism detector.

NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)

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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King

Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)

Challenge:	Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance.
Approach:	They propose a framework to optimize the effectiveness of IFT by carefully aligning the world and internal knowledge of LLMs.
Outcome:	The proposed framework can significantly improve performance across multiple LLM ability evaluation datasets.

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Mo Yu, Jiangnan Li, Shunyu Yao, Wenjie Pang, Xiaochen Zhou, Zhou Xiao, Fandong Meng, Jie Zhou

Challenge:	Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading.
Approach:	They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets.
Outcome:	The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans.

DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)

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Yu Li, Baolin Peng, Pengcheng He, Michel Galley, Zhou Yu, Jianfeng Gao

Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)

Challenge:	Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data.
Approach:	They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
Outcome:	The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings.

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Xuefei Li, Huiwei Zhou, Weihong Yao, Wenchu Li, Yingyu Lin, Lei Du

Knowledge Representation

Challenge:	Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive.
Approach:	They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns.
Outcome:	The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%.

Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference (2025.acl-long)

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Siyuan Wang, Dianyi Wang, Chengxing Zhou, Zejun Li, Zhihao Fan, Xuanjing Huang, Zhongyu Wei

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

Challenge:	Existing Large Vision-Language Models (LVLMs) learn visual capacity through visual instruction tuning.
Approach:	They propose a method for LVLMs to be trained by selective layers tuning . they propose removing non-critical layers outside the visual region .
Outcome:	The proposed approach preserves nearly 99% of visual performance and improves textual task results while reducing training time.

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

Contextual Modeling for Document-level ASR Error Correction (2024.lrec-main)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Jin Jiang, Xunjian Yin, Xiaojun Wan, Wei Peng, Rongjun Li, Jingyuan Yang, Yanquan Zhou

Fully Hyperbolic Neural Networks (2022.acl-long)

Challenge:	Existing work on document-level ASR error correction ignores contextual information . however, there are limited studies on incorporating contextual information into AEC .
Approach:	They propose a context-aware method that retrieves contextual information from a datastore . they use two English and two Chinese datasets to model document-level AEC .
Outcome:	The proposed model can utilize contextual information to improve document-level AEC . the data store containing contextual information provides even better results .

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

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

Retrieval Heads are Dynamic (2026.acl-long)

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Yuping Lin, Zitao Li, Yue Xing, Pengfei He, Yingqian Cui, Yaliang Li, Bolin Ding, Jingren Zhou, Jiliang Tang

Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)

Challenge:	Recent studies have identified "retrieval heads" in Large Language Models responsible for extracting information from input contexts.
Approach:	They propose to examine retrieval heads from a dynamic perspective . they establish that retrieval head activation is highly dynamic and functionally irreplaceable .
Outcome:	The proposed model's hidden state encodes a predictive signal for future retrieval head patterns, indicating an internal planning mechanism.

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Zhilun Zhou, Zihan Liu, Jiahe Liu, Yihan Wang, Qingyu Shao, Fengli Xu, Depeng Jin, Yong Li

Challenge:	Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence"
Approach:	They find a Collective Intelligence factor in human groups that captures their general capability.
Outcome:	The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities.

EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (2024.lrec-main)

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Xue Gu, Zhihan Zhou, Ziyao Meng, Jian Li, Tiago Gomes, Adriano Tavares, Hao Xu

Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension (2023.findings-acl)

Challenge:	Existing methods for Emotion-cause pair extraction (ECPE) do not distinguish between the emotion-caused pairs that belong to different types of emotions, limiting their applicability.
Approach:	They propose an Emotion-cause pair extraction method which integrates the implicit knowledge of cause clauses into a prompt template and extends the emotion labels to categories with an external emotion word base.
Outcome:	The proposed method extracts all potential emotion clauses and corresponding cause clauses from unannotated documents.

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Jiangnan Li, Mo Yu, Fandong Meng, Zheng Lin, Peng Fu, Weiping Wang, Jie Zhou

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

Challenge:	Compared to standard RC tasks, dialogue reading comprehension (DRC) has raised challenges because of the complex speaker information and noisy dialogue context.
Approach:	They propose a new method for dialogue reading comprehension that extracts answers from dialogues by using key-utterances-extracting methods and a Question-Interlocutor Scope Realized Graph.
Outcome:	The proposed method achieves state-of-the-art performance against previous works.

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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang

Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation (2025.emnlp-main)

Challenge:	Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach:	They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome:	The proposed approach outperforms existing methods in 84.33% of the trials.

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Li Zhou, Lutong Yu, Dongchu Xie, Shaohuan Cheng, Wenyan Li, Haizhou Li

Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking (2022.findings-emnlp)

Challenge:	Existing studies on cultural understanding with vision-language models primarily emphasize geographic diversity, often overlooking the critical temporal dimensions.
Approach:	They propose a multimodal vision-language model that examines temporal features and cultural image transcreation.
Outcome:	The novel model performs better than non-experts on visual cutural understanding but falls short to human experts on cultural image transcreation task.

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Yinghui Li, Shirong Ma, Qingyu Zhou, Zhongli Li, Li Yangning, Shulin Huang, Ruiyang Liu, Chao Li, Yunbo Cao, Haitao Zheng

MECH: A Cost-Effective Multi-Task Cascade Framework for Classroom Opinion Evolution Recognition (2026.acl-long)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
Approach:	They propose a framework which renders Chinese Spell Checking model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.
Outcome:	The proposed framework renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.

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Yancui Li, Xiaoyu Zhou, Guoyi Miao, Fang Kong

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

Challenge:	Existing studies focus on dialogue act annotation, overlooking the deeper dimension of opinion evolution.
Approach:	They propose a framework for Classroom Opinion Evolution Recognition that translates "Action-Opinion" dualism into a risk-aware routing mechanism.
Outcome:	The proposed framework achieves state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%.

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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu

Challenge:	Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach:	They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome:	The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie

Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)

Challenge:	under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach:	They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome:	The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.

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Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)

Challenge:	Abstract meaning representation (AMR) parsing is limited by the size of curated datasets.
Approach:	They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks.
Outcome:	The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models.

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Xu Zhang, Kun Zhang, Wenxin Ma, Rongsheng Wang, Chenxu Wu, Yingtai Li, S Kevin Zhou

Challenge:	Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other.
Approach:	They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules.
Outcome:	The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations.

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)

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Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)

Challenge:	In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored.
Approach:	They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL.
Outcome:	The proposed method improves ICL performance and expedites inference.

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Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu

Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)

Challenge:	Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem .
Approach:	They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts .
Outcome:	The proposed model outperforms the state-of-the-art approach in most experiments.

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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao

New Compendium of a Myriad of Plants: A New Dataset Describing Ancient Chinese Plants (2026.findings-acl)

Challenge:	Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video.
Approach:	They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights.
Outcome:	The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio.

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Xiaobin Shen, Zhongqing Wang, Shichen Li, Chu-Ren Huang, Guodong Zhou

On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)

Challenge:	Existing approaches to digitize ancient Chinese texts and extract information from them are shallow and inconsistent with modern realities.
Approach:	They propose to expand ancient Chinese datasets using large language model . they focus on Great Compendium of Myriad Flowers, an ancient plants dataset .
Outcome:	The proposed model can extract plant-related information from classical Chinese poetry and prose.

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Guozheng Li, Peng Wang, Zijie Xu, Jing Zhou, Jiajun Liu, Ziyu Shang

HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)

Challenge:	Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE .
Approach:	They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning.
Outcome:	The proposed framework achieves state-of-the-art on five widely used RE benchmarks.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren

GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding (2023.emnlp-main)

Challenge:	Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles.
Approach:	They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles.
Outcome:	The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy.

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Zekun Li, Wenxuan Zhou, Yao-Yi Chiang, Muhao Chen

ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks (2026.acl-long)

Challenge:	Pretrained language models do not utilize valuable geospatial information in large databases, e.g., OpenStreetMap.
Approach:	They propose a geospatially grounded language model that connects linguistic and geospheric contexts.
Outcome:	The proposed model bridges the gap between natural language processing and geospatial sciences.

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Peizhuo Lv, Ruihua Zhou, Yunpeng Li, Ruigang Liang, Xingshuo Han, XiaoFeng Wang, Wei Dong, Yuling Liu

Triangular Architecture for Rare Language Translation (P18-1)

Challenge:	Existing reasoning-enhanced large language models fail to provide reliable attribution of reasoning behavior once it is transferred through knowledge distillation.
Approach:	They propose to embed a reasoning-length gap in a model by querying a target domain and training a local student to imitate its outputs.
Outcome:	et al. show that ReasMark outperforms baselines while preserving task utility.

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Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma

Better Zero-Shot Reasoning with Role-Play Prompting (2024.naacl-long)

Challenge:	Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language.
Approach:	They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs.
Outcome:	Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods.

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Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong

Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)

Challenge:	Recent years have witnessed a paradigm shift in natural language processing, driven by large language models such as GPT-3, PaLM, and Llama.
Approach:	They propose a strategy for role-play prompting and assess its performance under the zero-shot setting.
Outcome:	The proposed method outperforms the standard zero-shot prompting approach across 12 reasoning benchmarks.

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Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li

One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification (2022.coling-1)

Challenge:	Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup.
Approach:	They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup.
Outcome:	The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer.

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Xiaoqin Chang, Sophia Yat Mei Lee, Suyang Zhu, Shoushan Li, Guodong Zhou

Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)

Challenge:	Existing knowledge distillation models require large computing resources and long inference time to perform.
Approach:	They propose a one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning.
Outcome:	The proposed method achieves better results with fewer parameters and extremely high speedup ratios on three sentiment classification tasks.

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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao

GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)

Challenge:	Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing.
Approach:	They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language.
Outcome:	The proposed method achieves favorable control ability and audio quality.

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Zixian Huang, Yulin Shen, Xiao Li, Yu’ang Wei, Gong Cheng, Lin Zhou, Xinyu Dai, Yuzhong Qu

Semantic Contribution-Aware Adaptive Retrieval for Black-Box Models (2025.findings-emnlp)

Challenge:	SQA is an emerging application of NLP in the medical, geography, and legal domains.
Approach:	They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level.
Outcome:	The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level.

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Qinhong Lin, Zhongliang Yang, Yuang Cai, Dingfu Yu, Xuan Xu, Yu Li, Linna Zhou

Dynamic PMI-Guided Contrastive Decoding Reduces Hallucination in Large Language Models: A Unified Framework of Fine-Grained Input Transformations (2026.findings-acl)

Challenge:	Existing approaches to retrieval-agmented generation fail to generalize effectively in black-box scenarios.
Approach:	They propose a framework that leverages the semantic importance of words to dynamically adjust retrieval thresholds and filter information.
Outcome:	The proposed framework achieves the highest score on four long-form, knowledge-intensive generation datasets.

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Dongsheng Chen, Yingqi Zhu, Xingyue Zhang, Wenqing Zhou, Lei Li

Exploring Unified Training Framework for Multimodal User Profiling (2025.coling-main)

Challenge:	Despite the remarkable generation capabilities of large language models, the issue of hallucination remains a critical challenge.
Approach:	They propose a contrastive decoding framework based on dynamic pointwise mutual information that disentangles spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures and prioritizes causal logic.
Outcome:	The proposed framework significantly improves the model’s factuality and reasoning robustness while maintaining high computational efficiency.

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Minjie Qiang, Zhongqing Wang, Shoushan Li, Guodong Zhou

Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021.emnlp-main)

Challenge:	Recent studies on user profiling focus on extracting multiple aspects of user attributes from textual reviews, but these studies do not fully exploit the potential of the rich multimodal data at hand.
Approach:	They propose a task that utilizes both review texts and their accompanying images to generate comprehensive user profiles.
Outcome:	The proposed training framework incorporates historical review texts and images for user profile generation.

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Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri

PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025.acl-long)

Challenge:	a proposed model for question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers is based on suggested question generation in conversational news recommendation systems.
Approach:	They propose a model for generating question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers.
Outcome:	The proposed model captures the central gists of the articles and achieves high answer accuracy.

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Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Alex Qiu, Jiayi Zhou, Kaile Wang, Boxun Li, Sirui Han, Yike Guo, Yaodong Yang

Challenge:	Using large-scale annotation data, large language models can generate noise, errors and biases, leading to unexpected behaviours.
Approach:	They propose a dataset to promote safety alignment in large language models . they separate helpfulness and harmlessness annotations for question-answering pairs .
Outcome:	The proposed dataset provides 44.6k prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels, with answers generated by Llama-family models.

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

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

Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation (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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Feilong Chen, Fandong Meng, Xiuyi Chen, Peng Li, Jie Zhou

Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)

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

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Ruizhe Chen, Yichen Li, Jianfei Yang, Yang Feng, Joey Tianyi Zhou, Jian Wu, Zuozhu Liu

Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks (2025.coling-main)

Challenge:	Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions.
Approach:	They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase.
Outcome:	The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction.

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Weisheng Li, Yuechen Wang, Jiaxin Shi, Wengang Zhou, Qi Tian, Houqiang Li

Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

Challenge:	Existing encoders that encode incremented inputs have to re-encode the whole text to obtain the encoding of the extended input.
Approach:	They propose an efficient encoder dedicated for faster encoding of incremented input . it takes only added input as input but attends to cached representations of original input a lower layer .
Outcome:	The proposed encoder achieves 6.2x speedup over current encoders . it takes only added input as input but attends to cached representations of original input .

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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li

ROSE: Robust Selective Fine-tuning for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach:	They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome:	The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

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Lan Jiang, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Rui Jiang

ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)

Challenge:	Recent studies have highlighted the lack of adversarial robustness in pre-trained models.
Approach:	They propose a fine-tuning approach that conducts selective updates when adapting pre-trained models to downstream tasks.
Outcome:	The proposed approach improves adversarial robustness on downstream tasks . it eliminates spurious updates, leading to flatter and wider optima than the conventional method .

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Tenggan Zhang, Xinjie Zhang, Jinming Zhao, Li Zhou, Qin Jin

TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)

Challenge:	Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues.
Approach:	They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability.
Outcome:	The proposed scheme can generate dialogue responses with better interpretability.

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Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

Challenge:	Large Language Models (LLMs) can solve complex tasks through iterative information retrieval.
Approach:	They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards .
Outcome:	Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models.

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)

Challenge:	Existing methods to classify QA text contain rich sentiment information.
Approach:	They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines.
Outcome:	The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit.

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Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li

From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models (2022.findings-emnlp)

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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Lei Li, Yankai Lin, Xuancheng Ren, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun

Towards a More Generalized Approach in Open Relation Extraction (2025.acl-long)

Challenge:	Existing models for natural language processing (NLP) are fine-tuned and released for research and deployments.
Approach:	They propose a PLM reuse paradigm that merges teacher-PLM knowledge into a student model.
Outcome:	The proposed paradigm can reduce the computational cost and environmental side-effects of retraining the PLM from scratch.

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Qing Wang, Yuepei Li, Qiao Qiao, Kang Zhou, Qi Li

Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product (2020.emnlp-main)

Challenge:	Existing OpenRE methods assume unlabeled data is a mixture of known and novel instances.
Approach:	They propose a generalized OpenRE setting that considers unlabeled data as a mixture of known and novel instances.
Outcome:	The proposed framework outperforms baselines in relation classification and clustering on three benchmark datasets.

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Tiangang Zhu, Yue Wang, Haoran Li, Youzheng Wu, Xiaodong He, Bowen Zhou

On Vision Features in Multimodal Machine Translation (2022.acl-long)

Challenge:	In the real world, product attribute values are incomplete and vary over time, which hinders practical applications.
Approach:	They propose a multimodal method to jointly predict product attributes and extract values from product images using multimodal product information.
Outcome:	The proposed method can predict product attributes and extract values from product images with the help of product images.

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Bei Li, Chuanhao Lv, Zefan Zhou, Tao Zhou, Tong Xiao, Anxiang Ma, JingBo Zhu

Event Detection without Triggers (N19-1)

Challenge:	Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models.
Approach:	They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation.
Outcome:	The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models.

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Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou

SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting (2023.findings-emnlp)

Challenge:	Existing approaches to event detection require annotated triggers and event types in training data.
Approach:	They propose a framework that encodes the representation of a sentence based on target event types.
Outcome:	The proposed framework achieves competitive performances compared with state-of-the-art methods.

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Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, Helen Meng

Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2023.eacl-main)

Challenge:	Experimental results show that SGP-TOD provides state-of-the-art zero-shot performance . prevailing approach for creating task bots is to fine-tune pre-trained language models .
Approach:	They propose a Schema-Guided Prompting for building Task-Oriented Dialog systems . they use predefined task schema and dialog policy to instruct fixed LLMs to generate appropriate responses .
Outcome:	The proposed system outperforms few-shot approaches on multiwoz, RADDLE, and STAR datasets.

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Chunyang Jiang, Tianchen Zhu, Haoyi Zhou, Chang Liu, Ting Deng, Chunming Hu, Jianxin Li

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)

Challenge:	Multi-hop reasoning is a common approach for query answering, but can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation.
Approach:	They propose a method that quantitatively estimates to what extent a path is spurious by a metric called Path Spuriousness (PS) they propose KG reasoning, which infers new facts along existing paths in KGs.
Outcome:	The proposed model significantly improves the agent’s ability to prevent spurious paths while keeping comparable to state-of-the-art performance.

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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji

Treble Counterfactual VLMs: A Causal Approach to Hallucination (2025.findings-emnlp)

Challenge:	Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output.
Approach:	They propose a method that directly modifies model weights to map a trigger to an attacker-specified response.
Outcome:	The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility.

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Li Li, Jiashu Qu, Linxin Song, Yuxiao Zhou, Yuehan Qin, Tiankai Yang, Yue Zhao

Socratic Human Feedback (SoHF): Expert Steering Strategies for LLM Code Generation (2024.findings-emnlp)

Challenge:	Existing studies link hallucination to data or representation biases, but their causal origins remain unclear.
Approach:	They propose a causal framework to analyze and mitigate hallucination in vision-language models by using counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction.
Outcome:	The proposed framework significantly reduces hallucination while preserving task performance while retaining reliability.

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Subramanian Chidambaram, Li Li, Min Bai, Xiaopeng Li, Kaixiang Lin, Xiong Zhou, Alex Williams

ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly used for generating code solutions, but struggle with complex programming problems without human guidance.
Approach:	They use the “Socratic Feedback” paradigm to map observed feedback strategies to five stages of Socratic Questioning to identify failures in LLMs.
Outcome:	The proposed models solved 74% of the problems that the models initially failed to solve on their own.

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Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou

More than Text: Multi-modal Chinese Word Segmentation (2021.acl-short)

Challenge:	Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering.
Approach:	They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text.
Outcome:	The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings.

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Dong Zhang, Zheng Hu, Shoushan Li, Hanqian Wu, Qiaoming Zhu, Guodong Zhou

Ambiguity Awareness Optimization: Towards Semantic Disambiguation for Direct Preference Optimization (2025.emnlp-main)

Challenge:	Currently, word segmentation is performed in many languages without word delimiters.
Approach:	They propose to combine the multi-modality to perform Chinese word segmentation . they propose a time-dependent multi-module interactive model to integrate multi-modality information .
Outcome:	The proposed model integrates multi-modal information for word sequence labeling with Chinese language as target . the proposed model performs well on three training sets on Chinese and other languages without word delimiters.

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Jian Li, Shenglin Yin, Yujia Zhang, Alan Zhao, Xi Chen, Xiaohui Zhou, Pengfei Xu

CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)

Challenge:	Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains.
Approach:	They propose an approach that automatically re-weights ambiguous content to reduce ambiguities by calculating semantic similarity from preference pairs.
Outcome:	The proposed approach outperforms state-of-the-art approaches in performance across multiple model scales and widely adopted benchmark datasets.

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Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, Maosong Sun

Adversarial Attention Modeling for Multi-dimensional Emotion Regression (P19-1)

Challenge:	Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents.
Approach:	They present a problem of cross-document relation extraction (CRE) using human annotations.
Outcome:	The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models.

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Suyang Zhu, Shoushan Li, Guodong Zhou

MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)

Challenge:	Empirical evaluation on EMOBANK corpus shows that our approach achieves notable improvements in r-values over the state-of-the-art baselines.
Approach:	They propose a neural network-based approach to multi-dimensional emotion regression which automatically rates multiple emotion dimension scores for an input text.
Outcome:	The proposed approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks over the state-of-the-art baselines.

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Chunyou Li, Mingtong Liu, Hongxiao Zhang, Yufeng Chen, Jinan Xu, Ming Zhou

Improving BERT with Syntax-aware Local Attention (2021.findings-acl)

Challenge:	Sentence-level translation, document-level and terminology constrained translations are important in machine translation.
Approach:	They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning .
Outcome:	The proposed model improves translation memory, document-level translation, and document-constrained translation tasks.

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Zhongli Li, Qingyu Zhou, Chao Li, Ke Xu, Yunbo Cao

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction (2025.emnlp-main)

Challenge:	Recent studies show that attention-based models benefit from more focused attention over local regions.
Approach:	They propose a syntax-aware local attention which restrains attention over syntactically relevant words.
Outcome:	The proposed model performs better on all benchmark datasets, including sentence classification and sequence labeling tasks.

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Song Wang, Zhen Tan, Zihan Chen, Shuang Zhou, Tianlong Chen, Jundong Li

“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction (2022.findings-acl)

Challenge:	Existing methods for multi-agent collaboration rely on static or graph-based topologies lacking flexibility and adaptability.
Approach:	They propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.
Outcome:	The proposed method achieves superior performance while significantly reducing communication overhead.

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Yong Dai, Linyang Li, Cong Zhou, Zhangyin Feng, Enbo Zhao, Xipeng Qiu, Piji Li, Duyu Tang

Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022.findings-emnlp)

Challenge:	a Chinese model with whole word masking has no subword because each token is an atomic character.
Approach:	They propose to use whole word masking to mask all subwords corresponding to a word at once . they ask models to revise or insert tokens in a masked language modeling manner .
Outcome:	The proposed model performs better when one character is inserted or replaced . the model trained with standard character-level masking performs best when one token is masked .

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Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng

Consistency Regularization Training for Compositional Generalization (2023.acl-long)

Challenge:	a number of safety concerns hinder the deployment of open-domain dialog systems, such as offensive languages and toxic behaviors, such social bias is difficult to detect.
Approach:	They propose a Dial-Bias Framework for analyzing social bias in conversations . they introduce a Chinese social bias dialog dataset and conduct in-depth ablation studies .
Outcome:	The proposed framework is the first annotated Chinese social bias dialog dataset . the proposed framework also provides a fine-grained dialog bias measurement benchmark .

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Yongjing Yin, Jiali Zeng, Yafu Li, Fandong Meng, Jie Zhou, Yue Zhang

Multi-modal Multi-label Emotion Detection with Modality and Label Dependence (2020.emnlp-main)

Challenge:	Existing neural models have difficulty generalizing to unseen combinations of seen components.
Approach:	They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures.
Outcome:	The proposed model performs well on semantic parsing and machine translation benchmarks.

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Dong Zhang, Xincheng Ju, Junhui Li, Shoushan Li, Qiaoming Zhu, Guodong Zhou

Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory (2025.emnlp-main)

Challenge:	Existing studies on multi-label emotion detection focus on one modality . current studies focus on label dependence, but there is no consensus on the model .
Approach:	They propose a multi-modal sequence-to-set approach to model label dependence and modality dependence in a multiple-modal scenario.
Outcome:	The proposed approach is able to model the label dependence and the modality dependence in a multi-modal scenario.

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Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, Tianyi Zhou

Linguistics & Cognitive NLP

Challenge:	Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, but we lack a principled framework for understanding how these thoughts are structured.
Approach:	They propose a method to analyze the reasoning traces of Large Reasoning Models using Schoenfeld’s Episode Theory.
Outcome:	The proposed framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.

SENT: Sentence-level Distant Relation Extraction via Negative Training (2021.acl-long)

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Ruotian Ma, Tao Gui, Linyang Li, Qi Zhang, Xuanjing Huang, Yaqian Zhou

Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)

Challenge:	Existing methods for relation extraction use bag labels, which introduce noise, to train the model.
Approach:	They propose to use negative training to train a model using complementary labels to separate the noisy data from the training data.
Outcome:	The proposed method improves on previous methods on sentence-level evaluation and de-noise effect.

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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu

Automated Profile Inference with Language Model Agents (2026.findings-acl)

Challenge:	Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs.
Approach:	They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability.
Outcome:	The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination.

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Yuntao Du, Zitao Li, Bolin Ding, Yaliang Li, Hanshen Xiao, Jingren Zhou, Ninghui Li

Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS (2026.acl-long)

Challenge:	Existing privacy protections for large language models (LLMs) are limited due to the potential for malicious applications.
Approach:	They propose an automated profile inference framework that can extract personal information from public online activities by an adversary with the help of large language model (LLM) based agents.
Outcome:	The proposed framework is highly effective and efficient and the inferred attributes are both identifiable and sensitive, posing significant privacy risks.

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Bingyu Yan, Xiaoming Zhang, JinYu Hou, Chaozhuo Li, Ziyi Zhou, Yiming Hei, Litian Zhang

Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)

Challenge:	Existing tool attacks are limited by domain specificity or fixed and static templates.
Approach:	They propose an attack-based memory-augmented reinforcement learning process that constructs a dynamic attack memory and employs deliberative reasoning to retrieve adversarial patterns.
Outcome:	Evo-Attacker outperforms baselines in the long-horizon credit assignment challenge.

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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu

Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings (2025.findings-emnlp)

Challenge:	Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures.
Approach:	They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree .
Outcome:	The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness.

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Liyan Xu, Zhenlin Su, Mo Yu, Jiangnan Li, Fandong Meng, Jie Zhou

Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)

Challenge:	a limited number of text encoders are able to recognize fine-grained entities or events within encoded semantics.
Approach:	They propose a new evaluation dataset to examine embeddings' ability to recognize fine-grained entities or events within encoded semantics.
Outcome:	The proposed dataset shows embeddings struggle with fine-grained matching . the proposed encoder outperforms the state-of-the-art 7B model in a small sample .

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Li Siyan, Zhen Xu, Vethavikashini Chithrra Raghuram, Xuanming Zhang, Renzhe Yu, Zhou Yu

The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity (2021.acl-long)

Challenge:	Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments .
Approach:	They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts .
Outcome:	The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions.

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David Gros, Yu Li, Zhou Yu

Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification (2025.naacl-long)

Challenge:	We analyze 2,500 phrasings related to the intent of “Are you a robot?” and 2,500 adversarially selected utterances to determine whether systems are non-human.
Approach:	They analyze 2,500 phrasings related to the intent of "Are you a robot?" and 2,500 adversarially selected utterances to determine whether systems are non-human.
Outcome:	The proposed model and two systems fail to confirm non-human intent, and the proposed model is complex.

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Ang Li, Yiquan Wu, Ming Cai, Adam Jatowt, Xiang Zhou, Weiming Lu, Changlong Sun, Fei Wu, Kun Kuang

TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation (2022.coling-1)

Challenge:	Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case.
Approach:	They propose a knowledge-enhanced approach that incorporates 'label-level knowledge' to enhance the representation of case facts for each task and 'task-level' knowledge to improve synergy.
Outcome:	The proposed method is effective in comparison to state-of-the-art (SOTA) baselines.

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Chenxu Yang, Zheng Lin, Jiangnan Li, Fandong Meng, Weiping Wang, Lanrui Wang, Jie Zhou

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

Challenge:	Recent work finds that realizing who holds the initiative can help select knowledge . however, there is a strong semantic transition between two rounds, probably leading to initiative misjudgment .
Approach:	They propose a topic-shift Aware Knowledge sElector(TAKE) model which locates relevant parts from dialogue history to improve knowledge selection.
Outcome:	The proposed model outperforms baseline models on the WoW.

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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

Challenge:	Inductive reasoning is an important task for large language models (LLMs).
Approach:	They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation.
Outcome:	The proposed method improves inductive reasoning in large language models.

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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

Challenge:	Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing.
Approach:	They propose a cross-lingual conversation summarization benchmark that explicitly considers source context.
Outcome:	The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations.

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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun

Semi-Supervised Spoken Language Glossification (2024.acl-long)

Challenge:	Current development practices face a dichotomy between automation and performance.
Approach:	They propose a framework to empower LLMs with the capability of automated explicit vectorization.
Outcome:	The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench.

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Huijie Yao, Wengang Zhou, Hao Zhou, Houqiang Li

Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots (2025.findings-emnlp)

Challenge:	Spoken language glossification (SLG) aims to translate spoken language text into sign language gloss, i.e., written record of sign language.
Approach:	They propose a framework to translate spoken language into a sign language gloss . they use monolingual spoken language text to integrate it into training .
Outcome:	The proposed framework incorporates large-scale monolingual spoken language text into SLG training.

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Boyu Qiao, Kun Li, Wei Zhou, Songlin Hu

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Current studies rely on simplistic user and network modeling and neglect dynamic behavior of bots.
Approach:	They propose a multi-agent-based framework for disinformation dissemination . it incorporates both malicious and legitimate bots and allows quantitative evaluation of correction strategies.
Outcome:	The proposed framework incorporates both malicious and legitimate bots and their controlled dynamic participation allows for quantitative analysis of correction strategies.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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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

LLaMA-Berry: Pairwise Optimization for Olympiad-level Mathematical Reasoning via O1-like Monte Carlo Tree Search (2025.naacl-long)

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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Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou

GUI Agents: A Survey (2025.findings-acl)

Challenge:	LLaMA-Berry is an advanced mathematical reasoning framework to enhance the problem-solving ability of large language models (LLMs).
Approach:	They propose a Monte Carlo Tree Search and Self-Refine framework to optimize reasoning paths and a pairwise reward model to evaluate different paths globally.
Outcome:	The proposed framework overcomes inefficiencies and limitations of step-wise and greedy search algorithms, enabling more efficient exploration of solution spaces.

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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt

Challenge:	Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
Approach:	They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities.
Outcome:	The proposed framework delineates their perception, reasoning, planning, and acting capabilities.

A Survey on Cross-Lingual Summarization (2022.tacl-1)

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Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou

Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)

Challenge:	Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language.
Approach:	They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other .
Outcome:	The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis.

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Haoran Li, Song Xu, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

SMR: State Memory Replay for Long Sequence Modeling (2024.findings-acl)

Challenge:	Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying.
Approach:	They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Outcome:	The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying .

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Biqing Qi, Junqi Gao, Kaiyan Zhang, Dong Li, Jianxing Liu, Ligang Wu, Bowen Zhou

Challenge:	Existing state space models (SSMs) address non-uniform sampling, but their recursive structures impede efficient SSM computation via convolution.
Approach:	They propose a plug-and-play mechanism to solve the Non-Stable State problem by adjusting input sequences with early memories.
Outcome:	The proposed method overcomes the non-uniform sample processing problem . it can achieve Sampling Step Adaptation (SSA) by adjusting input sequences with early memories.

L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)

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Zecheng Tang, Keyan Zhou, Juntao Li, Baibei Ji, Jianye Hou, Min Zhang

Challenge:	Long-context models (LCMs) have seen remarkable advancements in recent years, facilitating tasks like long-document QA.
Approach:	They propose an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks.
Outcome:	The proposed suite can assess both generation quality and fidelity in long-context understanding tasks.

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026.findings-acl)

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Challenge:	Existing research on PTQ spans three primary directions.
Approach:	They conduct a systematic analysis of post-training quantization failures using PTQ . they show that targeted repair can mitigate Signal Degradation but remains ineffective for Computation Collapse .
Outcome:	The proposed method mitigates Signal Degradation but remains ineffective for Computation Collapse.

Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction (2026.findings-acl)

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Ming Li, Han Chen, Yunze Xiao, Jian Chen, Hong Jiao, Tianyi Zhou

Cognitive Modeling

Challenge:	Accurate estimation of item (question or task) difficulty suffers from the cold start problem.
Approach:	They propose to use large-scale empirical analysis to examine human-AI Difficulty Alignment . they find that models struggle to simulate the capability limitations of students .
Outcome:	The proposed model size is not reliably helpful for human-AI alignment . high performance often impedes accurate difficulty estimation, the authors say .

InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)

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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li

Aligning Large Language Models for Controllable Recommendations (2024.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation.
Approach:	They propose a document-related metric to quantify the contribution of retrieved documents to correct answer generation.
Outcome:	The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms.

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Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie

Multi-modal Action Chain Abductive Reasoning (2023.acl-long)

Challenge:	Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template.
Approach:	They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions.
Outcome:	The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.

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Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu

A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)

Challenge:	Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena.
Approach:	They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer.
Outcome:	The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset.

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Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026.findings-acl)

Challenge:	Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory .
Approach:	They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation.
Outcome:	The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses.

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Self-Supervised Singing Voice Pre-Training towards Speech-to-Singing Conversion (2024.findings-acl)

Challenge:	Existing scaling laws focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities.
Approach:	They propose a framework that unifies model size, bit-width, and fine-grained factors into memorization, application, and reasoning.
Outcome:	The proposed framework shows strong fit and cross-architecture consistency on 293 different PTQ configurations.

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Ruiqi Li, Rongjie Huang, Yongqi Wang, Zhiqing Hong, Zhou Zhao

3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding (2023.emnlp-main)

Challenge:	Existing studies on speech-to-singing voice conversion (STS) are limited by the scarcity of paired speech-song data and the suboptimal quality of outputs.
Approach:	They propose a self-supervised singing voice pre-training model that transforms a speech-to-singing voice into a paired singing voice.
Outcome:	The proposed model improves both STS and singing voice synthesis tasks by combining spoken language and a self-supervised singing voice pre-training model.

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Zehan Wang, Haifeng Huang, Yang Zhao, Linjun Li, Xize Cheng, Yichen Zhu, Aoxiong Yin, Zhou Zhao

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

Challenge:	3D visual grounding aims to localize the desired objects in a 3D point cloud by a free-form language description.
Approach:	They propose a relation-aware framework which captures relative spatial relationships between objects and enhances object attributes.
Outcome:	The proposed framework outperforms state-of-the-art methods on three benchmarks . it captures relative spatial relationships between objects and enhances object attributes .

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

A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)

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

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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova

A Usage-centric Take on Intent Understanding in E-Commerce (2024.emnlp-main)

Challenge:	Existing document understanding models focus on single-modal inputs such as images or texts.
Approach:	They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information.
Outcome:	The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines.

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Wendi Zhou, Tianyi Li, Pavlos Vougiouklis, Mark Steedman, Jeff Pan

Challenge:	Identifying and understanding user intents is a crucial task for E-Commerce.
Approach:	They propose to use intent understanding as a natural language reasoning task independent of product ontologies to identify and understand user intents.
Outcome:	The proposed framework can't be used to strongly align user intents with products with desirable properties and recommend useful products across diverse categories.

Learning the Beauty in Songs: Neural Singing Voice Beautifier (2022.acl-long)

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Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao

MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)

Challenge:	Existing techniques for pitch correction are limited to intonation but ignore the overall aesthetic quality.
Approach:	They propose a novel time-warping approach for pitch correction to synchronize the amateur recording with the template pitch curve.
Outcome:	The proposed model improves intonation and vocal tone while keeping content and vocal timbre.

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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao

Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search (2024.acl-long)

Challenge:	Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations.
Approach:	They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency.
Outcome:	The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types.

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Haochen Li, Xin Zhou, Zhiqi Shen

Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning (2026.findings-acl)

Challenge:	Experimental results show that ReCo significantly boosts retrieval accuracy across sparse, zero-shot dense and fine-tuned dense search settings.
Approach:	They propose a generation-augmented retrieval framework that additionally Rewrites the Code (ReCo) within the codebase for style normalization.
Outcome:	The proposed method significantly boosts retrieval accuracy across sparse, zero-shot dense, and fine-tuned dense retrieval settings in diverse search scenarios.

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Zhenpeng Su, Leiyu Pan, Minxuan Lv, Tiehua Mei, Zijia Lin, Yuntao Li, Wenping Hu, Ruiming Tang, Kun Gai, Guorui Zhou

Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory (2025.naacl-long)

Challenge:	Large language model post-training often adopts an off-policy training paradigm . however, the off-poliicy training model introduces distribution shifts that push the policy beyond the trust region.
Approach:	They propose to use the entropy ratio as a global metric to measure the relative change in policy exploration throughout updates.
Outcome:	Experiments show that the proposed metric improves performance across multiple benchmarks.

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Haoran Li, Wei Fan, Yulin Chen, Cheng Jiayang, Tianshu Chu, Xuebing Zhou, Peizhao Hu, Yangqiu Song

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Existing privacy studies focus on sub-fields, but they focus on a few sub-domains.
Approach:	They propose to use the Health Insurance Portability and Accountability Act of 1996 as an example to develop a checklist that covers social identities, private attributes, and existing privacy regulations.
Outcome:	The proposed checklist covers social identities, private attributes, and existing privacy regulations.

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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Mapping Natural Language Instructions to Mobile UI Action Sequences (2020.acl-main)

Challenge:	Existing work shows that pre-trained models can improve in various natural language processing tasks.
Approach:	They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data.
Outcome:	The proposed framework is superior to existing models on speech-to-text processing tasks.

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Yang Li, Jiacong He, Xin Zhou, Yuan Zhang, Jason Baldridge

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)

Challenge:	a new problem of grounding natural language instructions to mobile UI actions is emerging . we use a Transformer to extract action phrase tuples from long-range natural language instruction .
Approach:	They propose a dataset that pairs English instructions with actions performed by people on a mobile UI emulator.
Outcome:	The proposed model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.

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Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou

Understanding Translationese in Cross-Lingual Summarization (2023.findings-emnlp)

Challenge:	Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training.
Approach:	They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable.
Outcome:	Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines.

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Jiaan Wang, Fandong Meng, Yunlong Liang, Tingyi Zhang, Jiarong Xu, Zhixu Li, Jie Zhou

MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025.acl-long)

Challenge:	Existing datasets involve translation, but translationese is distinguished from original text . previous studies have shown that translationeses in CLS are not a problem in training sets .
Approach:	They propose to use cross-lingual summarization to generate a concise summary in a target language from a document in . existing datasets typically involve translation in their creation, but the translated text is distinguished from the original written in that language.
Outcome:	The proposed method systematically investigates how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries.

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Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, Wentao Zhang

BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents (2026.findings-acl)

Challenge:	MM-Verifier and MM Reasoner are a powerful multimodal reasoning model . large language models (LLMs) have demonstrated exceptional performance across tasks spanning myriad domains.
Approach:	They propose a method which combines tree search and verification to generate high-quality chain-of-thought data.
Outcome:	The proposed method outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks.

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Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang, Zhongwang Zhang, Xixi Wu, Rui Ye, Zile Qiao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou

Challenge:	Existing work on confidence in LLMs is limited.
Approach:	They propose to use confidence scores to determine model answer quality and encourage model to try again until it reaches satisfactory confidence level.
Outcome:	The proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget methods.

Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models (2021.eacl-main)

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Qingyang Wu, Yichi Zhang, Yu Li, Zhou Yu

Entity-Relation Extraction as Multi-Turn Question Answering (P19-1)

Challenge:	Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks.
Approach:	They propose a framework that uses pre-trained language to model each speaker separately . it can be generalized to more challenging, non-collaborative tasks such as persuasion .
Outcome:	The proposed framework outperforms or is on par with state-of-the-art methods on two popular datasets: CamRest676 and MultiWOZ.

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Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

Challenge:	Identifying entities and their relations is the prerequisite of extracting structured knowledge from unstructured raw texts.
Approach:	They propose a new paradigm for the task of entity-relation extraction . they cast the task as a multi-turn question answering problem .
Outcome:	The proposed paradigm significantly outperforms previous best models on the ACE and CoNLL04 datasets.

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Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Fang Zhouhua, Zhiwei Liu, Dajun Chen, Yong Li, Jiajun Bu

Challenge:	Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach:	They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome:	The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)

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Hang Yan, Yu Sun, Xiaonan Li, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)

Challenge:	Information Extraction (IE) tasks have been solved with different models because of their output structures.
Approach:	They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix.
Outcome:	The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets.

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Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations.
Approach:	They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning.
Outcome:	The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations.

A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)

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Houquan Zhou, Bo Zhang, Zhenghua Li, Ming Yan, Min Zhang

Trait Activation in Silicon: A Situation-Aware Framework for Psychologically Grounded Role-Playing (2026.acl-long)

Challenge:	Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in text.
Approach:	They propose a task that handles missing and redundant characters and an additional prompt-based large language model to improve performance.
Outcome:	The proposed task is based on a high-quality dataset and a prompt-based large language model.

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Zuolong Li, Pingyu Wu, Xianwen Huang, Tianyi Wei, Wenbo Zhou

What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective (2025.acl-long)

Challenge:	Role-playing agents lack a deep understanding of complex human psychological mechanisms.
Approach:	They propose a situation-aware framework that decouples personality traits into bidirectional LoRA adapters.
Outcome:	Empirical results show that PD-LLM achieves superior performance in both static fidelity and dynamic adaptability.

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Ming Li, Yanhong Li, Tianyi Zhou

PerMemSafe: Benchmarking Implicit Personalized Safety of Long Horizon Self-Evolving Agents (2026.findings-acl)

Challenge:	Xu et al., 2024) study shows that slow thinking can distinguish correct and irrelevant reasoning paths.
Approach:	They investigate how fast vs. slow thinking affects layer-wise gradients in large language models . they find that slow thinking can distinguish correct and irrelevant reasoning paths .
Outcome:	The results show that slow thinking can distinguish correct and irrelevant reasoning paths.

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Hengyu An, Minxi Li, Naen Xu, Chunyi Zhou, Xiaogang Xu, Tianyu Du, Jinbao Li, Shouling Ji

Chinese Court Simulation with LLM-Based Agents System (2026.findings-acl)

Challenge:	Existing self-evolving agents have a low safety rate in long-horizon interactions . however, this reliance on context-independent safety evaluations is insufficient .
Approach:	They propose a framework that explicitly models personalized risk inference and memory evolution.
Outcome:	The proposed framework improves implicit personalized safety by 23.8% over prior frameworks while maintaining helpfulness in long-horizon interactions.

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Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai

Language Models Resist Alignment: Evidence From Data Compression (2025.acl-long)

Challenge:	Existing studies have neglected the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulators in practice.
Approach:	They propose a court simulation paradigm based on the real-world procedure structure of Chinese courts and a framework that focuses on both legal judgment prediction and court procedure analysis.
Outcome:	The proposed model outperforms judges and lawyers from the real trials in many aspects.

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Jiaming Ji, Kaile Wang, Tianyi Alex Qiu, Boyuan Chen, Jiayi Zhou, Changye Li, Hantao Lou, Josef Dai, Yunhuai Liu, Yaodong Yang

Mixture of Small and Large Models for Chinese Spelling Check (2025.acl-long)

Challenge:	Large language models (LLMs) may exhibit undesirable behaviors due to the inevitable biases and harmful content present in training.
Approach:	They propose to investigate the elasticity of large language models by examining their performance.
Outcome:	The proposed model performance declines rapidly before reverting to the pre-training distribution, the authors show . the proposed model weight and code are available at pku-lm-res ist-alignment.github.io.

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Ziheng Qiao, Houquan Zhou, Zhenghua Li

Challenge:	Chinese Spelling Check (CSC) tasks have been developed to correct spelling errors in given sentences . fine-tuned BERT-based models show excellent performance but suffer from edit pattern overfitting . a novel mixture approach that effectively combines small models and LLMs during beam search decoding phase improves accuracy and fluency of LLM.
Approach:	They propose a dynamic mixture approach that effectively combines small models and LLMs during beam search decoding phase.
Outcome:	The proposed method significantly boosts error correction capabilities, achieving state-of-the-art results across multiple datasets.

Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation (2022.lrec-1)

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Liming Xiao, Bin Li, Zhixing Xu, Kairui Huo, Minxuan Feng, Junsheng Zhou, Weiguang Qu

OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)

Challenge:	Abstract Meaning Representation abstracts the meaning of sentences into a single-rooted, acyclic and directed graph.
Approach:	They propose to use a metric to evaluate concept alignment and relation alignment to improve Chinese AMR parsing evaluation methods.
Outcome:	The proposed method is more robust and compatible with concept alignment and relation alignment and more robust in evaluating arcs.

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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu

Quantification of Large Language Model Distillation (2025.acl-long)

Challenge:	a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide.
Approach:	They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions .
Outcome:	The proposed agents are based on operating systems (OS) and operating systems frameworks.

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Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow (D19-1)

Challenge:	Existing studies have revealed the robustness degra-dation caused by data distillation.
Approach:	They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization.
Outcome:	The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization.

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Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, Eduard Hovy

Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)

Challenge:	Neural sequence-to-sequence models are autoregressive, meaning they factor the joint probability of the output sequence into the product of probabilities over the next to-ken.
Approach:	They propose a non-autoregressive sequence generation model using latent variables . they use generative flow to model complex distributions using neural networks .
Outcome:	The proposed model performs comparable to state-of-the-art models and has constant decoding time w.r.t the sequence length.

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Pengqi Li, Lizhong Ding, Zhehao Zhou, Chunhui Zhang, Jiarun Fu, Hao Li, Ye Yuan, Guoren Wang

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment.
Approach:	They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error.
Outcome:	The proposed method provides theoretical guarantees and empirical gains for reliability.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Shilin Zhou, Zhenghua Li, Yu Hong, Min Zhang, Zhefeng Wang, Baoxing Huai

ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)

Challenge:	Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities.
Approach:	They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription.
Outcome:	The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity.

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Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu, Jie Zhou

QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)

Challenge:	Existing approaches to building cross-lingual summarization systems on dialogue documents are limited.
Approach:	They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Outcome:	The proposed model outperforms pipeline models on ClidSum and mDialBART.

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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou

Explainability & Interpretability in NLP

Challenge:	QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal .
Approach:	a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate .
Outcome:	QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training .

Efficient Hyper-parameter Search for Knowledge Graph Embedding (2022.acl-long)

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Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li

Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)

Challenge:	Existing methods for learning knowledge graphs do not search hyper-parameters efficiently.
Approach:	They propose an efficient two-stage search algorithm which explores HP configurations on small subgraph and transfers top-performed configurations for fine-tuning on large full graph.
Outcome:	The proposed method finds better HPs than baseline algorithms within the same time budget and achieves 9.1% relative improvement on large-scale knowledge graphs.

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Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Xiaofang Zhou

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

Challenge:	Large language models (LLMs) face factual hallucination and knowledge obsolescence when tackling knowledge-intensive tasks.
Approach:	They propose a layer-knowledge guided attention method which harnesses the layer-wise knowledge of large language models to optimize per-layer attention on useful passages.
Outcome:	The proposed method outperforms existing methods on RALM benchmarks.

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

ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph (2023.emnlp-main)

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

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Jinhao Jiang, Kun Zhou, Xin Zhao, Yaliang Li, Ji-Rong Wen

Phrase-level Textual Adversarial Attack with Label Preservation (2022.findings-naacl)

Challenge:	Question Answering over Knowledge Graph (KGQA) aims to find answer entities for natural language questions based on knowledge graphs.
Approach:	They propose a subgraph-aware self-attention mechanism to imitate the graph neural network (GNN) based module to perform multi-hop reasoning on KG.
Outcome:	The proposed method surpasses state-of-the-art models by a large margin even with fewer updated parameters and less training data.

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Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy

WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training (2026.findings-acl)

Challenge:	Existing adversarial attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality.
Approach:	They propose a phrase-level perturbation-based adversarial ATtack that generates adversarials through phrase- level perturbations.
Outcome:	The proposed approach improves the performance of natural language processing models by reducing the need for word-level perturbations and preserving the fluency and grammaticality of the samples.

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Yifu Chen, Shengpeng Ji, Qian Chen, Tianle Liang, Yangzhuo Li, Ziqing Wang, Wen Wang, Jingyu Lu, Haoxiao Wang, Xueyi Pu, Fan Zhuo, Zhou Zhao

HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)

Challenge:	End-to-end spoken dialogue models have higher potential ceiling in expressiveness and perceptual ability than cascaded systems.
Approach:	They propose a modality-aware adaptive post-training recipe that constrains preference updates to the semantic channel and improves acoustic behavior via explicit anchoring.
Outcome:	The proposed model improves speech quality and expressiveness across spoken dialogue benchmarks and architectures.

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Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng

Plot Retrieval as an Assessment of Abstract Semantic Association (2024.acl-srw)

Challenge:	Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval.
Approach:	They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges.
Outcome:	Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations.

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Shicheng Xu, Liang Pang, Jiangnan Li, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

Challenge:	Existing information retrieval datasets cannot capture abstract semantic associations well.
Approach:	They propose a task that retrieves relevant plots from the book for a query using a labeled dataset.
Outcome:	The proposed task can be used to evaluate the performance of IR models on the novel task Plot Retrieval.

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Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding

Challenge:	Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens.
Approach:	They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks.
Outcome:	The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%)

Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)

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Jiang Li, Pengfei Cao, Chenxi Zhou, Tian Lan, Xiangdong Su, Kang Liu, Jun Zhao, Guanglai Gao

Challenge:	Large language models (LLMs) have impressive performance but require computational and memory resources.
Approach:	They propose a post-training framework that uses a Haar wavelet transform to prune weights.
Outcome:	The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture.

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

Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (2023.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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Qing Wang, Kang Zhou, Qiao Qiao, Yuepei Li, Qi Li

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

Challenge:	Recent studies on relation representation learning focus on contrastive learning strategies, but these studies overlook important aspects.
Approach:	They propose to use within-sentence pairs augmentation and cross-sentent pairs extraction to increase diversity of positive pairs and strengthen the discriminative power of contrastive learning.
Outcome:	The proposed task increases diversity of positive pairs and strengthens discriminative power . it overcomes limitations of traditional Relation Extraction tasks, which require manual annotations .

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

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

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

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

Robust Singing Voice Transcription Serves Synthesis (2024.acl-long)

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

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Ruiqi Li, Yu Zhang, Yongqi Wang, Zhiqing Hong, Rongjie Huang, Zhou Zhao

MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization (2024.lrec-main)

Challenge:	Current AST methods struggle with accuracy and robustness when used for practical annotation.
Approach:	They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets.
Outcome:	The proposed model outperforms baseline models on enlarged, automatically annotated datasets.

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Tao Chen, Ze Lin, Hui Li, Jiayi Ji, Yiyi Zhou, Guanbin Li, Rongrong Ji

Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)

Challenge:	Existing product summarization methods lack end-to-end product summaries and multi-grained multi-modal modeling.
Approach:	They propose an end-to-end multi-grained multi-modal attribute-aware product summarization method that jointly models product attributes and generates product summaries.
Outcome:	The proposed method outperforms state-of-the-art product summarization methods on a large-scale Chinese e-commence dataset.

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Sen Yang, Qingyu Zhou, Dawei Feng, Yang Liu, Chao Li, Yunbo Cao, Dongsheng Li

UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)

Challenge:	Existing tasks to generate question-answer pairs from visual images are under-explored.
Approach:	They propose a task that targets question-answer pair generation from visual images.
Outcome:	The proposed model can generate diverse or consistent QAPs on two benchmarks.

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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)

Challenge:	Existing datasets address understanding and generation in isolation, limiting the performance of unified vision large language models.
Approach:	They propose a dataset that facilitates mutual enhancement between multimodal understanding and generation.
Outcome:	The proposed framework integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment.

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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai

Template-free Prompt Tuning for Few-shot NER (2022.naacl-main)

Challenge:	elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored.
Approach:	They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency .
Outcome:	The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training.

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Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, Xuanjing Huang

Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)

Challenge:	Prompt-based methods have been successfully applied in few-shot learning tasks . however, when applied to token-level labeling tasks, it would be time-consuming to enumerate the template queries over all potential entity spans.
Approach:	They propose a method to reformulate NER tasks as LM problems without templates.
Outcome:	The proposed method is 30.12 times faster than the template-based method under few-shot settings.

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Zehan Li, Fu Zhang, Wenqing Zhang, Jiawei Li, Zhou Li, Jingwei Cheng, Tianyue Peng

TeachMaster: Generative Teaching via Code (2026.acl-industry)

Challenge:	Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance.
Approach:	They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text.
Outcome:	The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods.

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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian

More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021.emnlp-main)

Challenge:	Existing methods for creating video content are limited by high costs and slow update cycles.
Approach:	They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution.
Outcome:	The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education.

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Sixing Wu, Ying Li, Minghui Wang, Dawei Zhang, Yang Zhou, Zhonghai Wu

RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model (2025.findings-naacl)

Challenge:	Existing knowledge-enhanced methods use a single-source homogeneous knowledge base with limited knowledge coverage.
Approach:	They propose a multi-source heterogeneous knowledge-enhanced dialogue generation model that leverages multiple knowledge sources to improve knowledge coverage.
Outcome:	The proposed model outperforms existing knowledge-enhanced models on a Chinese dataset and shows that it can leverage multiple heterogeneous knowledge sources to improve knowledge coverage.

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Changhai Zhou, Shijie Han, Lining Yang, Yuhua Zhou, Xu Cheng, Yibin Wang, Hongguang Li

ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)

Challenge:	Current compression techniques entail structural pruning and a recovery phase that leverages the Low-Rank Adaptation algorithm.
Approach:	They propose a hierarchical rank allocation method that enables efficient fine-tuning of pruned LLMs according to layerwise specific recovery requirements.
Outcome:	The proposed algorithm outperforms state-of-the-art methods across pruning settings and LLM architectures with improvements ranging from 0.7% to 5.5%.

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Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Distributionally Robust Multilingual Machine Translation (2021.emnlp-main)

Challenge:	Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow.
Approach:	They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition.
Outcome:	The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT.

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Chunting Zhou, Daniel Levy, Xian Li, Marjan Ghazvininejad, Graham Neubig

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)

Challenge:	Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, but the data imbalance hinders it from performing uniformly across language pairs.
Approach:	They propose a distributionally robust optimization objective which minimizes the worst-case expected loss over the set of language pairs.
Outcome:	The proposed learning objective outperforms baseline methods on three sets of languages and shows that it is cost-effective and efficient.

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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu

Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)

Challenge:	Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting.
Approach:	They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status.
Outcome:	The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B).

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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou

G-SPEED: General SParse Efficient Editing MoDel (2023.findings-emnlp)

Challenge:	Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability.
Approach:	They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem.
Outcome:	The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines.

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Haoke Zhang, Yue Wang, Juntao Li, Xiabing Zhou, Min Zhang

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages.
Approach:	They propose a general SParse Efficient Editing MoDel which can fulfill diverse editing requirements through a single model while maintaining low computational costs.
Outcome:	The proposed model can fulfill diverse editing requirements through a single model while maintaining low computational costs.

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

Non-Sequential Graph Script Induction via Multimedia Grounding (2023.acl-long)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Yu Zhou, Sha Li, Manling Li, Xudong Lin, Shih-Fu Chang, Mohit Bansal, Heng Ji

XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)

Challenge:	Existing scripts for everyday tasks are presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life.
Approach:	They propose to use loosely aligned videos to train a non-sequential graph script induction task by using a multimodal framework to ground procedural videos to WikiHow textual steps.
Outcome:	The proposed model outperforms the WikiHow linear baseline by 48.76% . it can predict future steps given a partial step sequence and generate explicit graph scripts .

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Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

Situated Embedding Models for Context-Aware Dense Retrieval (2026.acl-short)

Challenge:	Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English.
Approach:	They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach .
Outcome:	The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks.

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Junjie Wu, Jiangnan Li, Yuqing Li, Lemao Liu, Liyan Xu, Jiwei Li, Dit-Yan Yeung, Jie Zhou, Mo Yu

Representation Learning

Challenge:	Existing embedding models are not well-equipped to encode situated context effectively, i.e., situating a chunk’s meaning within its context.
Approach:	They propose to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance.
Outcome:	The proposed model outperforms state-of-the-art embedding models on a book-plot retrieval dataset.

Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training (2021.acl-long)

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Wangchunshu Zhou, Qifei Li, Chenle Li

AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)

Challenge:	Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better.
Approach:	They propose an algorithm that encourages the model to be sensitive to perturbations in dialogue history and learn from perturbations.
Outcome:	The proposed approach can model dialogue history better and generate more diverse responses on two benchmark datasets.

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Kaibo Huang, Jin Tan, Yukun Wei, Wanling Li, Zipei Zhang, Hui Tian, Zhongliang Yang, Linna Zhou

Challenge:	Recent advances in large language models (LLMs) have improved text generation and reasoning.
Approach:	They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility.
Outcome:	The proposed framework embeds multi-bit provenance into planning decisions while preserving utility.

Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)

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Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie Zhou

Rethinking Negative Pairs in Code Search (2023.emnlp-main)

Challenge:	Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks.
Approach:	They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities.
Outcome:	The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios.

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Haochen Li, Xin Zhou, Anh Luu, Chunyan Miao

What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)

Challenge:	Comparative learning is a key component in fine-tuning code search models . however, negative samples of InfoNCE may deteriorate its representation learning .
Approach:	They propose a loss function that inserts weight terms into InfoNCE to improve contrastive learning.
Outcome:	The proposed loss function is a special case of Soft-InfoNCE, the authors show . it is more accurate than other loss functions, and it is faster than other models.

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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)

Challenge:	Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields.
Approach:	They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies.
Outcome:	The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges.

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Bingyu Wang, Li Chen, Wei Sun, Kechen Qin, Kefeng Li, Hui Zhou

Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods .
Approach:	They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems.
Outcome:	The proposed method is competitive to state-of-the-art methods on benchmark datasets.

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Yufeng Shi, Weilin Luo, Yuxiang Zhang, Zongmeng Zhang, Haoyang Liu, Yubing Wang, Bin Wang, Wengang Zhou, Houqiang Li

Discovering Representation Sprachbund For Multilingual Pre-Training (2021.findings-emnlp)

Challenge:	Large Reasoning Models (LRMs) are constrained by the overthinking issue.
Approach:	They propose a policy optimization framework that reshapes the exploration and exploitation through two core components: self-imitation and self-guidance exploration.
Outcome:	The proposed model achieves superior reasoning efficiency without compromising overall accuracy.

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Yimin Fan, Yaobo Liang, Alexandre Muzio, Hany Hassan, Houqiang Li, Ming Zhou, Nan Duan

LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization (2025.coling-main)

Challenge:	Existing models perform poorly on many languages and cross-lingual tasks due to typological differences and contradictions between some languages.
Approach:	They propose to pre-train multilingual pre-trained models to handle cross-lingual tasks in one model.
Outcome:	The proposed model improves performance on cross-lingual tasks compared to baselines on multiple languages .

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Yanyue Zhang, Pengfei Li, Yilong Lai, Yulan He, Deyu Zhou

A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)

Challenge:	Existing methods to generate negative summaries are expensive and lack the capacity to generate large data sets.
Approach:	They propose a data augmentation framework based on LArge and Small language models for debiaSing opinion summarization that generates a small number of synthesized negative reviews by rewriting the positive text via a large language model.
Outcome:	The proposed framework can generate large numbers of negative reviews by rewriting the positive text using a large language model and training a disentangle reconstruction model based on the generated data.

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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.

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis (2026.acl-demo)

Challenge:	Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach:	They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome:	The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Zhichao Shi, Cehao Yang, Hao Zhou, Xiaojun Wu, Huajie Li, Xuhui Jiang, Chengjin Xu, Yuanzhuo Wang, Jian Guo

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.acl-long)

Challenge:	Existing synthetic data tools are limited by convoluted workflows, fragmented data standards, and limited scalability across modalities.
Approach:	They develop an open-source framework that aims to reduce the technical barrier to synthetic data generation and subsequent model training.
Outcome:	The proposed framework achieves an optimal balance between generation efficiency and data quality.

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Qian Cao, Yahui Liu, Wei Bi, Yi Zhao, Ruihua Song, Xiting Wang, Ruiming Tang, Guorui Zhou, Han Li

Challenge:	Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity.
Approach:	They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation.
Outcome:	The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

Text-to-Song: Towards Controllable Music Generation Incorporating Vocal and Accompaniment (2024.acl-long)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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Zhiqing Hong, Rongjie Huang, Xize Cheng, Yongqi Wang, Ruiqi Li, Fuming You, Zhou Zhao, Zhimeng Zhang

Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)

Challenge:	Existing studies focus on singing voice synthesis and music generation independently.
Approach:	They propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation.
Outcome:	The proposed method can synthesize songs with comparable quality and style consistency.

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Yanyue Zhang, Yilong Lai, Zhenglin Wang, Pengfei Li, Deyu Zhou, Yulan He

Large Language Models are Miscalibrated In-Context Learners (2025.findings-acl)

Challenge:	Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions.
Approach:	They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss.
Outcome:	The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution .

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Chengzu Li, Han Zhou, Goran Glavaš, Anna Korhonen, Ivan Vulić

Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)

Challenge:	In-context Learning and Supervised Fine-Tuning have emerged as pre-dominant methodologies for machine learning and NLP.
Approach:	They propose to use self-ensembling to improve both performance and calibration of language models.
Outcome:	The proposed learning paradigms can achieve better calibration and better performance than the previous learning paradigm.

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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang

Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)

Challenge:	Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs.
Approach:	They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information.
Outcome:	The proposed method significantly improves model safety while maintaining utility compared to existing methods.

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Junru Zhou, Zuchao Li, Hai Zhao

MSMO: Multimodal Summarization with Multimodal Output (D18-1)

Challenge:	Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing.
Approach:	They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way.
Outcome:	The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank.

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Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Chengqing Zong

How Adversarial Environments Mislead Agentic AI? (2026.findings-acl)

Challenge:	Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality.
Approach:	They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs.
Outcome:	The proposed method improves user satisfaction by 12.4% compared to the current system .

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Zhonghao Zhan, Huichi Zhou, Zhenhao Li, Peiyuan Jing, Krinos Li, Hamed Haddadi

Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)

Challenge:	Current evaluations benchmark capability in benign settings, but never "what if the tools lie" we formalize this vulnerability as Adversarial Environmental Injection (AEI) AEI constitutes environmental deception by constructing a "fake world" of poisoned search results .
Approach:	They propose an attack model where adversaries compromise tool outputs to deceive agents.
Outcome:	The proposed model exploits a trust gap between tool outputs and actual exposure to adversaries.

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Qinhong Zhou, Zonghan Yang, Peng Li, Yang Liu

Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)

Challenge:	Existing knowledge distillation methods require access to internal information of teachers . however, such information is not always accessible for large pre-trained language models .
Approach:	They propose a method to estimate logits from the decision distributions using logits theoretically and empirically.
Outcome:	The proposed method outperforms baselines on natural language understanding and machine reading comprehension datasets.

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Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)

Challenge:	Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions.
Approach:	They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph.
Outcome:	The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm.

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Wei Wang, Zhaowei Li, Qi Xu, YiQing Cai, Hang Song, Qi Qi, Ran Zhou, Zhida Huang, Tao Wang, Li Xiao

Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models (2023.emnlp-main)

Challenge:	Recent research has focused on smaller, task-specific models enhanced by distilling knowledge from LLMs, but the diversity and quality of negative knowledge remains understudied.
Approach:	They propose a quality-guided contrastive rationale distillation framework that aims to enhance reasoning capabilities through contrastive knowledge learning.
Outcome:	The proposed method consistently outperforms existing distillation techniques yielding higher-quality rationales.

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Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan

CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)

Challenge:	Recent work has shown that language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities.
Approach:	They propose a mechanistic interpretation of language models for multi-step reasoning tasks by introducing a new probing approach that recovers the reasoning tree from the model’s attention patterns.
Outcome:	The proposed model implicitly embeds a reasoning tree resembling the correct reasoning process within it, and detects the information from the model’s attention patterns for most examples.

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Zhenhong Zhou, Zherui Li, Jie Zhang, Yuanhe Zhang, Kun Wang, Yang Liu, Qing Guo

CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)

Challenge:	Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted.
Approach:	They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage.
Outcome:	The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks.

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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang

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

Challenge:	Existing knowledge base question answering methods struggle with complex queries.
Approach:	They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation.
Outcome:	The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ.

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

Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification (2020.acl-main)

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

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Hao Tang, Donghong Ji, Chenliang Li, Qiji Zhou

AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)

Challenge:	Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a given aspect.
Approach:	They propose a dependency graph enhanced dual-transformer network to support mutual reinforcement between the flat representation learning and graph-based representation learning.
Outcome:	The proposed model outperforms state-of-the-art methods on five datasets with a large margin.

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Ziming Li, Yan Zhou, Weibo Zhang, Yaxin Liu, Chuanpeng Yang, Zheng Lian, Songlin Hu

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)

Challenge:	Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information.
Approach:	They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems.
Outcome:	The proposed model outperforms state-of-the-art models on two public datasets.

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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 .

Dynamic Knowledge Distillation for Pre-trained Language Models (2021.emnlp-main)

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

ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)

Challenge:	Existing methods conduct knowledge distillation statically, e.g., student model aligns output distribution to teacher model on pre-defined training dataset.
Approach:	They propose a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency . they find it is promising and provide discussions on potential future directions towards more efficient methods .
Outcome:	The proposed method can boost student model performance while accelerating training . the proposed method reduces memory usage and accelerates model inference .

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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang

Challenge:	Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources.
Approach:	They propose a framework for sentence-level faithfulness verification with context-aware disambiguation.
Outcome:	The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets.

Re3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination (2026.acl-long)

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Jiawei Cao, Jie Ouyang, Mingyue Cheng, Zhaomeng Zhou, Chunli Liu, Yupeng Li, Zirui Liu, Shijin Wang

InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)

Challenge:	Existing retrievers suffer from temporal-semantic misalignment and outdated-document interference . Existing frameworks suffer from both temporal validity and outdated factual versions .
Approach:	They propose a framework that mitigates temporal hallucinations by embedding heterogeneous temporal signals into the semantic space to ensure retrieval fidelity.
Outcome:	Experiments show that Re3 outperforms baselines by 9.7% in generation accuracy . the framework outperformed strongest baselines on challenging dynamic tasks .

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Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)

Challenge:	Existing methods for learning cross-lingual representations are lacking in the field of NLP.
Approach:	They propose a framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts.
Outcome:	The proposed approach improves cross-lingual transferability on benchmarks.

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)

Challenge:	Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain.
Approach:	They propose a pre-training method to improve the joint extraction performance with just extra entity annotations.
Outcome:	The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks.

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Shilin Zhou, Zhenghua Li, Chen Gong, Lei Zhang, Yu Hong, Min Zhang

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)

Challenge:	Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics.
Approach:	They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions.
Outcome:	The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results.

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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin

Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach (2025.coling-main)

Challenge:	Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks.
Approach:	They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset.
Outcome:	The proposed model performs well across tasks and languages.

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Yuepei Li, Kang Zhou, Qiao Qiao, Qing Wang, Qi Li

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

Challenge:	Existing DS-NER approaches rely on large validation sets and test set for tuning inappropriately.
Approach:	They propose a method where training data is annotated using domain dictionaries and test data is analyzed by domain experts.
Outcome:	The proposed method reduces the need for labor-intensive manual annotations but rely on large human labeled validation set.

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

Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation (2026.acl-long)

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

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Jianing Zhang, Runan Li, Honglin Pang, Ding Xia, Zhou Zhu, Qian Zhang, Chuntao Li, Xi Yang

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)

Challenge:	Existing methods for deciphering ancient Chinese Oracle Bone Script (OBS) treat deciphering as a closed-set image recognition problem, which fails to bridge the "interpretation gap" .
Approach:	They propose a vision-language model framework that integrates a VLM and an LLM to automate a reasoning chain of component identification and knowledge retrieval.
Outcome:	The proposed framework yields more detailed and precise decipherments compared to baseline methods.

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Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, Zhilin Yang

MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)

Challenge:	Existing methods for text data augmentation are limited to simple tasks and weak baselines.
Approach:	They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data.
Outcome:	The proposed method improves many tasks while not negatively affecting the others.

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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin

Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning (2026.findings-acl)

Challenge:	Existing GUI benchmarks lack fine-grained diagnostics to identify which capabilities lead to task failures.
Approach:	They propose a multilingual P R GUI Benchmark to assess LVLMs' language capabilities . they propose XLI to align non-English hidden states with English ones during inference .
Outcome:	The proposed benchmark reveals consistent gaps between English and non-English settings . it reduces the cross-lingual gaps with an average gain of 6.5% in non- English settings compared to static benchmarks .

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Ziyuan Nan, Qi Yi, Di Huang, Yutong Wu, Guanhua Huang, Xue Gong, Kejiao Li, Yuhao Jiang, Chenchen Zhang, Zenan Xu, Xing Hu, Bo Zhou

The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)

Challenge:	Parallel thinking is a promising avenue for scaling test-time compute in Large Language Models . however, coordinating the exploration and aggregation stages remains challenging .
Approach:	They propose a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning.
Outcome:	The proposed framework improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets.

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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng

Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties (2026.eacl-short)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct spelling errors, which are caused by the phonological or visual similarity.
Approach:	They propose an Error-driven COntrastive Probability Optimization framework to refine the knowledge representations of pre-trained language models to avoid predicting common characters.
Outcome:	Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective.

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Zhenglin Wang, Jialong Wu, Pengfei Li, Yong Jiang, Deyu Zhou

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

Challenge:	Existing temporal reasoning benchmarks rely on rule-based construction and lack contextual depth . a recent study found existing LLMs struggle with nuanced temporal understanding .
Approach:	a benchmark is designed to evaluate LLMs on temporal reasoning in Chinese dynasties.
Outcome:	a new benchmark evaluates LLMs on temporal reasoning across Chinese dynasties . it emphasizes cross-entity relationships, pairwise temporal alignment, contextualized and culturally-grounded reasoning . results show existing LLM benchmarks struggle with nuanced temporal understanding .

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun