Papers by Chen Guo

Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking (2022.emnlp-main)

Challenge:	Large Language Models are increasingly utilized as role-playing agents to simulate personas in interactive settings.
Approach:	They propose a role-playing agent trained to explicitly ground responses in individual identity.
Outcome:	The proposed agent can generate persona-consistent responses in long-context dialogues while maintaining general instruction-following capabilities.

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Gao Qixiang, Guanting Dong, Yutao Mou, Liwen Wang, Chen Zeng, Daichi Guo, Mingyang Sun, Weiran Xu

On the Emergence and Test-Time Use of Structural Information in Large Language Models (2026.acl-long)

Challenge:	Existing frameworks for dialogue state tracking with domain-slot-value labels are expensive . current models are limited due to high cost of data annotation and lack of data in some domains .
Approach:	They propose a framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST.
Outcome:	The proposed framework outperforms existing methods on MultiWOZ and gains strong slot accuracy compared to existing models.

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Michelle Chao Chen, Moritz Miller, Bernhard Schölkopf, Siyuan Guo

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

Challenge:	a controlled environment is required to study how language models learn structural information from observational data.
Approach:	They propose a natural language dataset based on linguistic structural transformations to study how language models learn abstract structures and utilize the learnt structural information at test-time.
Outcome:	The proposed model can generate new knowledge outside the training corpus in a controlled environment.

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

Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law (2025.acl-long)

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

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Qiming Ge, Shuhao Xing, Songyang Gao, Yunhua Zhou, Yicheng Zou, Songyang Zhang, Zhi Chen, Hang Yan, Qi Zhang, Qipeng Guo, Kai Chen

HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)

Challenge:	Large language models have demonstrated impressive performance across a wide range of tasks, but this achievement comes with the trade-off of significant computational demands.
Approach:	They propose a scaling law that decomposes the overall validation loss and assigns different importance weights to tokens to assess a specific meta-capability.
Outcome:	The proposed model can predict the loss trending of models across different levels of computation without a gap between validation loss and model's downstream capabilities.

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Jinyu Guo, Xunlei Chen, Qiyang Xia, Zhaokun Wang, Jie Ou, Libo Qin, Shunyu Yao, Wenhong Tian

X-Router: Decoupling Knowledge and Reasoning for Cost-Effective LLM Inference (2026.findings-acl)

Challenge:	Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance.
Approach:	They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations.
Outcome:	The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets.

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Zixuan Wang, Yinze Ding, Zihan Wang, Jinyu Guo, Zhenhong Zhou, Junhao Dong, Chaomeng Chen

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

Challenge:	Existing adaptive methods focus on a single axis, overlooking evidence need and reasoning depth are only partially correlated.
Approach:	They propose a dual-axis routing framework that separates retrieval necessity from reasoning necessity under a user-defined cost–quality trade-off.
Outcome:	The proposed framework reduces token usage and latency while improving answer quality over strong baselines.

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

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

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

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

Word Segmentation by Separation Inference for East Asian Languages (2022.findings-acl)

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

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Yu Tong, Jingzhi Guo, Jizhe Zhou, Ge Chen, Guokai Zheng

Syntactic Text Processing

Challenge:	Chinese Word Segmentation (CWS) is a sequence labeling task that divides sentences into words . despite diverse tagging schemas, they all carry implicit position information.
Approach:	They propose to model the separation state of every two consecutive characters by tagging them as two tags.
Outcome:	The proposed framework outperforms state-of-the-art on Japanese and Korean Word Segmentation datasets.

APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning (2024.lrec-main)

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Jiashuo Sun, Hang Zhang, Chen Lin, Xiangdong Su, Yeyun Gong, Jian Guo

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

Challenge:	Existing methods to generate reasoning programs that ignore the differences between facts treated all facts equally, leading to wrong punishment of programs that differed from the ground truth.
Approach:	They propose an optimized training framework for long-form numerical reasoning that incorporates a number-aware negative sampling strategy and consistency-based reinforcement learning to increase execution accuracy.
Outcome:	The proposed method improves the performance of long-form numerical reasoning on the FinQA and ConvFinQA leaderboards.

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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

The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing (2025.findings-acl)

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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Xinwei Guo, Jiashi Gao, Junlei Zhou, Jiaxin Zhang, Guanhua Chen, Xiangyu Zhao, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei

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

Challenge:	Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes.
Approach:	They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing.
Outcome:	The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function.

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

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

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

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

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

DET: A Dual-Encoding Transformer for Relational Graph Embedding (2024.lrec-main)

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Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Qiang Zhang, Huajun Chen

MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training (2026.acl-long)

Challenge:	Existing approaches to graph representation only consider the local neighbors, sacrificing the Transformer’s ability to attend to elements at any distance.
Approach:	They propose a dual-encoding Transformer architecture that uses a structural encoder and a semantic encoder to seek for semantically relevant nodes.
Outcome:	The proposed architecture achieves superior performance compared to state-of-the-art attention-based methods on complex relational graphs like KGs and citation networks.

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Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy

Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models (2026.acl-long)

Challenge:	Existing systems for multi-turn Text-to-SQL are limited to a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs.
Approach:	They propose to train an agentic training framework for long-horizon multi-turn Text-to-SQL that uses a Markov Decision Process to generate a query per turn without execution, explicit verification, and refinement.
Outcome:	Experiments on CoSQL and SParC show that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing.

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Wenxuan Wang, Zizhan Ma, Guo Yu, Yiu-Fai Cheung, Meidan Ding, Jie Liu, Wenting Chen, Linlin Shen

Personality-Guided Code Generation Using Large Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) are proving significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities.
Approach:	They propose a framework that deconstructs benchmark development into five stages from design to governance and provides a checklist of 46 medically-tailored criteria.
Outcome:	The framework deconstructs benchmark development into five stages from design to governance and provides a comprehensive checklist of 46 medically-tailored criteria.

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Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu, Yun Ma

SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs (2025.findings-acl)

Challenge:	Existing studies have shown that personality-guided code generation improves software development outcomes when individuals are assigned tasks that match their personality types.
Approach:	They evaluate how emulating personality traits appropriate to the coding tasks affects LLM performance by using seven widely adopted LLMs.
Outcome:	The proposed approach improves pass rates in 23 out of 28 LLM-dataset combinations, while emulating personality traits can be easily integrated with other prompting strategies to further boost performance.

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Yu Guo, Dong Jin, Shenghao Ye, Shuangwu Chen, Jianyang Jianyang, Xiaobin Tan

Cross-Modality Relevance for Reasoning on Language and Vision (2020.acl-main)

Challenge:	Existing closed-source LLMs have a performance gap in text-to-SQL reasoning tasks.
Approach:	They propose a SQL-based approach to synthesize reliable data to enhance text-to-SQL reasoning in LLMs.
Outcome:	The proposed model achieves state-of-the-art accuracy on the widely recognized Spider and BIRD benchmarks, significantly narrowing the performance gap with closed-source methods.

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Chen Zheng, Quan Guo, Parisa Kordjamshidi

Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation (2020.acl-main)

Challenge:	Existing approaches to learn and reason over language and vision data for downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR)
Approach:	They propose a cross-modality relevance module that is used in an end-to-end framework to learn the relevance representation between components of various input modalities under supervision of a target task.
Outcome:	The proposed approach shows competitive performance on two different language and vision tasks using public benchmarks and improves the state-of-the-art published results.

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Junliang Guo, Linli Xu, Enhong Chen

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

Challenge:	masked language models have been used for natural language processing tasks but few studies have adopted it in the sequence-to-sequence models.
Approach:	They propose to combine encoder and decoder to train a masked sequence-to-sequence model . they propose to train the encoder more rigorously by masking the encoded input .
Outcome:	The proposed model achieves 27.69/32.24 BLEU scores on English-German/German-English tasks with 5+ times speed up compared with an autoregressive model.

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

Speech & Audio in NLP

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.

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

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

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

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

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

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (2020.emnlp-main)

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

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Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu

SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)

Challenge:	Recent attempts to learn static representations of entities and references ignore their dynamic properties.
Approach:	They propose to learn static representations of entities and references ignoring their dynamic properties . a neighbor encoder learns entities' roles while a query-aware aggregator learns references' contributions .
Outcome:	The proposed approach achieves state-of-the-art results with different few-shot sizes.

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Yuxuan Feng, Qian Chen, Qianyou Wu, Xin Guo, Suge Wang

Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)

Challenge:	Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability.
Approach:	They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation.
Outcome:	The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods .

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Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov

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

Challenge:	Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people .
Approach:	They propose a dataflow graph-based dialogue agent that maps each user utterance to a program that extends this graph.
Outcome:	The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program .

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

Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning (2025.findings-acl)

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

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Xinghao Chen, Zhijing Sun, Guo Wenjin, Miaoran Zhang, Yanjun Chen, Yirong Sun, Hui Su, Yijie Pan, Dietrich Klakow, Wenjie Li, Xiaoyu Shen

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

Challenge:	Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought prompting.
Approach:	They examine the factors influencing CoT distillation including granularity, format and teacher model.
Outcome:	The proposed model is based on four teacher models and seven student models across seven mathematical and commonsense reasoning datasets.

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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

Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)

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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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu

Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review (2025.findings-acl)

Challenge:	Existing studies show that Large Language Models can be misused to generate undesired content.
Approach:	They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs.
Outcome:	The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs.

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Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Recent advances in vision-language models have unified perception and understanding tasks within Visual Question Answering paradigms.
Approach:	They propose to outline timeline, architecture, and pipeline of nearly all TIU MLLMs and review their performance on mainstream benchmarks.
Outcome:	The proposed models perform well on mainstream benchmarks and are compared with other models.

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Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang

LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN (2026.findings-acl)

Challenge:	Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability.
Approach:	They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action .
Outcome:	The proposed agent improves 19.8% over baselines on complex questions and multi-tasks.

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Sijie Wang, Kai Zhao, Wee Peng Tay, Shuo Zhang, Chengwen Liu, Quanjiang Guo, Ren Junhao, Xin Li, Heng Lian, Jingdi Lei, Rui She, Huacan Wang, Ronghao Chen

Challenge:	Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding.
Approach:	They propose an SWE-level benchmark for AI coding in the Huawei Ascend CANN software stack.
Outcome:	The proposed benchmark is constructed from real-world CANN repositories and consists of over 400 task instances spanning multiple file, multi-language, and execution-aware coding challenges.

VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2025.findings-emnlp)

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Zhanpeng Chen, Chengjin Xu, Yiyan Qi, Xuhui Jiang, Jian Guo

Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)

Challenge:	a significant drawback of Vision-language Models is their reliance on static training data, leading to outdated information and limited contextual awareness.
Approach:	They propose a framework with knowledge-enhanced reranking and noise-injected training to improve the VLM's ranking ability.
Outcome:	The proposed framework is based on a simple yet effective instruction template and is able to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images.

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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang

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

Challenge:	Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different.
Approach:	They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts .
Outcome:	The proposed method can bring signiffcant improvement to entity accuracy.

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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

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

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

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

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

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

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

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

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

Noise-powered Multi-modal Knowledge Graph Representation Framework (2025.coling-main)

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

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Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen, Wen Zhang

Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2024.findings-emnlp)

Challenge:	Current efforts to integrate MMKG with pretraining are scarce.
Approach:	They propose a method that integrates multi-modal entity features into MMKGs using a Transformer-based architecture equipped with modality-level noise masking.
Outcome:	The proposed method achieves SOTA performance across ten datasets.

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Lu Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng

EventOA: An Event Ontology Alignment Benchmark Based on FrameNet and Wikidata (2023.findings-acl)

Challenge:	Existing studies on retrieval-augmented generation (RAG) rarely address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect.
Approach:	They propose a framework that induces RAG models to alter latent factors and analyzes the effect on their answers.
Outcome:	The proposed framework identifies two critical factors affecting RAG models' confidence in their answers and analyzes the effect on their answers.

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Shaoru Guo, Chenhao Wang, Yubo Chen, Kang Liu, Ru Li, Jun Zhao

Knowledge Representation

Challenge:	Existing studies on event ontologies focus on entity-based OA, and neglect event-based one . however, independent development of event ontoologies often results in heterogeneous representations that raise the need for establishing alignments between semantically related events.
Approach:	They propose a multi-view event ontology alignment method that utilizes description information and neighbor information to obtain richer representations of the event ontoologies.
Outcome:	The proposed method outperforms existing entity-based methods and can serve as a strong baseline for future research.

Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)

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Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty

VRoPE: Rotary Position Embedding for Video Large Language Models (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities.
Approach:	They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs.
Outcome:	The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content.

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Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, Jing Liu

LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Existing versions of Large Language Models (LLMs) lack a positional encoding strategy for video.
Approach:	They propose a new positional encoding method tailored for Video-LLMs that mitigates positional biases and ensures a more uniform distribution of spatial focus.
Outcome:	The proposed method outperforms existing versions of RoPE in video understanding and reasoning tasks.

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Yuxuan Li, Xinwei Guo, Jiashi Gao, Guanhua Chen, Xiangyu Zhao, Jiaxin Zhang, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei

Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)

Challenge:	Large language models (LLMs) generate outputs that stray from user input or contravene established knowledge.
Approach:	They propose a new phenomenon, Authority Bias, where LLMs favor one knowledge source over the other . they propose atomic information that generates conflicts and a Conflict Detection Enhanced Query framework .
Outcome:	The proposed framework reduces Authority bias in large language models . it detects conflicts, performs credibility assessment on conflicting paragraphs, and detects perturbed text .

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Yufei Li, Xiao Yu, Yanghong Guo, Yanchi Liu, Haifeng Chen, Cong Liu

DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)

Challenge:	Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge .
Approach:	They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging .
Outcome:	The proposed framework outperforms baseline models on two benchmark datasets with better interpretability.

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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao

LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding (2023.acl-long)

Challenge:	Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models .
Approach:	They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently .
Outcome:	The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width.

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Yi Tu, Ya Guo, Huan Chen, Jinyang Tang

UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions (2026.acl-long)

Challenge:	Pre-trained models on document images with transformer-based backbones have led to significant performance gains in this field.
Approach:	They propose a multi-modal pre-training model that combines text, layout and image . they propose to use local 1D position instead of global 1D positions as layout input .
Outcome:	The proposed model can achieve state-of-the-art results on a wide variety of VrDU problems.

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Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang

A Parameter-Efficient and Fine-Grained Prompt Learning for Vision-Language Models (2025.acl-long)

Challenge:	Generative audio modeling has been fragmented into specialized tasks such as text-to-speech (TTS), text- to-music (TTM), and text-ta (TTA) specialized models require reference audio for timbre cloning and strict phoneme alignment, whereas TTA models generate unstructured textures from open-ended captions.
Approach:	They propose a unified flow-matching framework capable of synthesizing speech, music, sound effects . they propose 'token injection mechanism' that projects unstructured environmental sounds into structured temporal latent space .
Outcome:	The proposed framework achieves state-of-the-art performance in instruction-based TTS and TTM while maintaining competitive fidelity in TTA.

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Yongbin Guo, Shuzhen Li, Zhulin Liu, Tong Zhang, C.L.Philip Chen

Travel on the ICD Tree: Benchmarking Agentic Reasoning for ICD Coding from Chinese Electronic Medical Records (2026.findings-acl)

Challenge:	Current vision-language models extract semantic information from large-scale cross-modal associations, limiting performance and efficiency.
Approach:	They propose a detail-oriented prompt learning method to implement fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters.
Outcome:	The proposed method implements fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters.

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Xinjie Xu, Yongqi Fan, Shuang-shuang Chen, Qi Ye, Weibin Guo, Xinxuan Hu

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

Challenge:	Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance.
Approach:	They propose a framework to evaluate ICD coding based on complete EMRs . they use a dataset of 560 real clinical records covering 434 common diseases .
Outcome:	The proposed framework explores the capability boundaries of large language models under different paradigms.

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

CausalCite: A Causal Formulation of Paper Citations (2024.findings-acl)

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

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Ishan Agrawal, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Mrinmaya Sachan, Bernhard Schölkopf

Representation Learning

Challenge:	citation counts are often criticized for failing to accurately reflect the true impact of a paper.
Approach:	They propose a method to measure the impact of a paper on follow-up papers by comparing similar papers by cosine similarity.
Outcome:	The proposed method is based on a new causal inference method, TextMatch.

EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents (2026.findings-acl)

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Yaoqi Guo, Ying Xiao, Jie M. Zhang, Mark Harman, Yiling Lou, Yang Liu, Zhenpeng Chen

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)

Challenge:	Large language models are reshaping modern software development, but they often incur substantial monetary cost.
Approach:	They propose an experience-driven early termination approach that extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection.
Outcome:	The proposed approach reduces cost by 19%–55% with negligible loss in resolution rate (at most 0.2%) EET extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection.

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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan

Sentiment Analysis

Challenge:	Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information.
Approach:	They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words.
Outcome:	The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks.

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering (2026.acl-long)

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Xiaoke Guo, Songze Li, Zhiqiang Liu, Zhaoyan Gong, Yuanxiang Liu, Huajun Chen, Wen Zhang

ArkRepoBench: A Repository-Level Code Completion Benchmark for HarmonyOS Development (2026.findings-acl)

Challenge:	Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility . Existing tree-based approaches suffer from limited semantic adaptability .
Approach:	They propose a method that leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees.
Outcome:	The proposed method achieves state-of-the-art (SOTA) performance on complex table benchmarks.

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Yanlin Wang, Bowen Zhang, Yanli Wang, Daya Guo, Terry Yue Zhuo, Jiachi Chen, Mingwei Liu, Xingong Zhang, Zibin Zheng

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

Challenge:	Despite the maturity of LLM-based code assistance for mainstream languages, the capabilities of ArkTS are largely unexplored.
Approach:	They propose to benchmark repository-level code completion for ArkTS using 7,519 samples from 20 official HarmonyOS repositories.
Outcome:	The proposed benchmark covers multiple difficulty levels and categorizes completion instances into Single-File, Cross-Filled Independent, and Cross-Filed Dependent settings based on dependency analysis.

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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

MOBA-E2C: Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data (2022.findings-emnlp)

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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Dawei Zhang, Sixing Wu, Yao Guo, Xiangqun Chen

Challenge:	e-sports game competitions lack commentators because of the shortage of professional human commentators.
Approach:	They propose a data-driven MOBA commentary generation framework for MOBA games . they use a rule-based generator and a generative GPT generator to generate commentaries .
Outcome:	The proposed model generates commentaries based on the game meta-data and a rule-based generator and generative GPT generator.

Beyond Screenshots: Evaluating VLMs’ Understanding of UI Animations (2026.findings-acl)

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Chen Liang, Xirui Jiang, Naihao Deng, Eytan Adar, Anhong Guo

Challenge:	Recent studies of Vision Language Models (VLMs) for UI understanding have focused primarily on static screenshots, leaving it unclear how well these models handle dynamic UI animations.
Approach:	They evaluate UI animation models' ability to perceive animation effects and interpret animation meaning . they use motion, context, and perceptual cues to probe factors affecting VLM performance .
Outcome:	The proposed model can detect primitive motion, but its interpretation is inconsistent . the proposed model is based on 300 annotated UI animation videos .

Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act (2023.findings-emnlp)

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Bao Chen, Yuanjie Wang, Zeming Liu, Yuhang Guo

Are Multimodal LLMs Robust Against Adversarial Perturbations? RoMMath: A Systematic Evaluation on Multimodal Math Reasoning (2025.naacl-long)

Challenge:	Existing evaluations of dialogue quality rely on human judgments, which are time-consuming, labor-intensive, prone to biases, and lacking objectivity.
Approach:	They propose a method that utilizes the underlying patterns of dialogue act transitions to evaluate the appropriateness of chatbot responses.
Outcome:	The proposed method proves that human judgments are time-consuming, labor-intensive, and lacking objectivity.

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Yilun Zhao, Guo Gan, Chen Zhao, Arman Cohan

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

Challenge:	Recent-released MLLMs have shown remarkable performance on various multimodal math reasoning benchmarks.
Approach:	They introduce RoMMath, the first benchmark designed to evaluate the capabilities and robustness of multimodal large language models in handling multimodal math reasoning.
Outcome:	The proposed model performs well on a broad spectrum of 17 MLLMs and demonstrates that they are robust to adversarial perturbations.

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

Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models? (2024.emnlp-main)

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

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Pinzhen Chen, Simon Yu, Zhicheng Guo, Barry Haddow

IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback (2026.acl-long)

Challenge:	Existing practices of fine-tuning and evaluating multilingual large language models may not align with this objective due to a heavy reliance on translation.
Approach:	They propose to use translated or native instruction data to fine-tune multilingual large language models.
Outcome:	The proposed model can be fine tuned and evaluated in multilingual large language models . the results show that native or translated data can be used to compare model performance .

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Yichen Cai, Jiayang Li, Junyuan Qiu, Jingya Guo, Weitao You, Changyuan Yang, Lingyun Sun, Pei Chen

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

Challenge:	Existing approaches to optimize conversational agents often rely on explicit preference pairs and expert evaluations.
Approach:	They propose a conversational agent framework that leverages the structured dependency between agent responses and user reactions to extract implicit feedback.
Outcome:	The proposed framework improves on MT-Bench-101, WildBench, and FB-Bech, and shows that mining implicit feedback supports better multi-turn alignment under evolving user preferences.

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

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

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

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

Beyond Tokens: Concept-Level Training Objectives for LLMs (2026.eacl-short)

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

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Laya Iyer, Pranav Somani, Alice Guo, Dan Jurafsky, Chen Shani

Challenge:	Large language models (LLMs) are trained with a surprisingly narrow objective: predicting the next token in a sequence.
Approach:	They propose a shift from token-level to concept-level prediction where concepts group multiple surface forms of the same idea.
Outcome:	The proposed model improves on human-level models on diverse NLP benchmarks.

InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents (2026.findings-acl)

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Zhenghao Zhu, Yuanfeng Song, Xing Chen, Chengzhong Liu, Cui Yakun, Caleb Chen Cao, Sirui Han, Yike Guo

Challenge:	Existing frameworks for data analysis and insight exploration are lacking in terms of benchmarks . existing frameworks suffer from format inconsistencies, poorly conceived objectives, and redundant insights.
Approach:	They propose a data-curation pipeline to construct a new dataset named InsightEval.
Outcome:	The proposed benchmarks highlight prevailing challenges in automated insight discovery and raise key findings to guide future research.

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)

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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan

UnitCoder: Scalable Code Synthesis from Pre-training Corpora (2025.emnlp-main)

Challenge:	Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world.
Approach:	They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning.
Outcome:	The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task.

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Yichuan Ma, Yunfan Shao, Peiji Li, Demin Song, Qipeng Guo, Linyang Li, Xipeng Qiu, Kai Chen

Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) excel at code understanding and generation, yet code generation remains a challenge.
Approach:	They propose a model that supervises pre-training data quality through automatically generated unit tests while ensuring correctness via an iterative fix and refine flow.
Outcome:	The proposed model improves performance on a large dataset with high quality pre-training data.

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Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

Challenge:	Existing algorithms for achieving optimal alignment are mostly unidirectional . a recent study suggests that large language models can be ground with evident preferences .
Approach:	They propose to ground large language models with evident preferences . they propose to use controllable preference optimization to specify different objectives .
Outcome:	The proposed models can provide responses that match various preferences among the ”3H” desiderata.

PCQPR: Proactive Conversational Question Planning with Reflection (2024.emnlp-main)

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Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Chen

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

Challenge:	Current CQG methods focus on immediate context without strategic consideration of the specified conversational outcome.
Approach:	They propose a method that uses a planning algorithm inspired by Monte Carlo Tree Search to generate contextually relevant questions.
Outcome:	The proposed approach surpasses existing methods in e-learning and customer service fields . it generates contextually appropriate questions strategically devised to reach a specified outcome .

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

Machine Translation

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

MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification (2026.findings-acl)

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Bo Zheng, Yudong Chen, Zihua Xiong, Shuai Fang, Peidong He, Yang Yang, Sheng Guo

Know the Known and the Unknown: Reasonable Answer Generation with Knowledge-Informed Citations (2026.acl-long)

Challenge:	Tabular data is high-dimensional, riddled with missing entries, and rarely labeled at scale.
Approach:	They propose a unified pre-training framework for industrial-scale tabular data . MaskTab encodes missing values via dedicated learnable tokens .
Outcome:	The proposed framework outperforms XGBoost and MaskTab-L on industrial-scale . it achieves +5.04% AUC and +8.28% KS over prior art under rigorous scaling .

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Jun Xu, Mengshu Sun, Zhizhen Liu, Lei Liang, Wen Zhang, Huajun Chen

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

Challenge:	Existing approaches focus on generating multi-level citations linked to specific references, making it verifiable and trustworthy.
Approach:	They propose a new data construction pipeline and a benchmark to improve citation granularity and awareness of unknown information.
Outcome:	The proposed model improves on the existing benchmark and data construction pipeline and provides citation granularity and awareness of unknown information.

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Lei Ji, Xianglin Guo, Haoyang Huang, Xilin Chen

Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning (2025.findings-acl)

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

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Shuhe Wang, Guoyin Wang, Yizhong Wang, Jiwei Li, Eduard Hovy, Chen Guo

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)

Challenge:	Packing is an optimization technique that optimizes training time and resources by combining different training sequences to fit the model’s maximum input length.
Approach:	They perform extensive comparisons between packing and padding methods, covering datasets ranging from 69K to 1.2M and models from 8B to 70B.
Outcome:	The proposed method has been shown to improve training efficiency while maintaining performance.

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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu

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

Challenge:	Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence.
Approach:	They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs.
Outcome:	The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks.

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

Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)

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

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Shasha Guo, Jing Zhang, Xirui Ke, Cuiping Li, Hong Chen

Efficient Domain Adaptation for Non-Autoregressive Machine Translation (2024.findings-acl)

Challenge:	Existing methods on knowledge base question generation focus on refining the quality of a single generated question.
Approach:	They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth.
Outcome:	The proposed model outperforms pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT.

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WangJie You, Pei Guo, Juntao Li, Kehai Chen, Min Zhang

What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning (2026.findings-acl)

Challenge:	Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive translation models less efficient . despite their impressive generalization and task performance, LLMs suffer from prohibitive inference cost when confronted with specific domains.
Approach:	They propose a domain adaptation approach that tailors a k-nearest-neighbor algorithm for NAT models that incorporates the parallel nature of NAT.
Outcome:	The proposed approach achieves significant improvements over the Base-NAT model and exhibits enhanced efficiency.

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Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, Wen Zhang

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

Challenge:	Existing GUI reasoning methods rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure.
Approach:	They propose a GUI reasoning paradigm that treats the GUI reasoning task as a cyclic *Screen-UI elements-Action process.
Outcome:	The proposed paradigm achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.

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

Contrastive Learning enhanced Author-Style Headline Generation (2022.emnlp-main)

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

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Hui Liu, Weidong Guo, Yige Chen, Xiangyang Li

Mind Reader: Latent User Demand-Guided Content Optimization for Generative Search Engine (2026.acl-long)

Challenge:	Current work only uses the article itself in the headline generation, but have not taken the writing style of headlines into account.
Approach:	They propose a model which takes historical headlines into account to integrate the stylistic features of the author into the model and integrate them into the decoder.
Outcome:	The proposed model can integrate the stylistic features of the author into the model and generate a headline that is appropriate for the article and consistent with the author’s style.

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Tong Chen, JiaWei Guo, Yuxi Li, Baiming Chen, Houxing Ren, Zhang Zhiwei, Yunxiang Zhang, Hanyang Xia, Kun Liang, Zhaoran Fan

SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation (2024.findings-naacl)

Challenge:	Generative Search Engines (GSEs) have reshaped information retrieval and Generating Engine Optimization (GEO) emerges to improve the content visibility in GSEs’ responses.
Approach:	They propose a method to optimize content to cover latent semantic information of GSEs by decomposing query into diverse perspectives and capturing underlying semantic information.
Outcome:	The proposed method outperforms baselines and effectively improves content visibility (with up to 2.44x objective metrics and 1.23x subjective metrics on average).

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Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen

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

Challenge:	Existing methods have significantly boosted the performance of Knowledge Base Question Generation (KBQG) through pre-trained language models thanks to the richly endowed semantic knowledge.
Approach:	They propose a framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG by combining skeleton heuristic guidance with a soft prompting approach.
Outcome:	The proposed framework incorporates "skeleton heuristics" which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions.

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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

DSM: Question Generation over Knowledge Base via Modeling Diverse Subgraphs with Meta-learner (2022.emnlp-main)

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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Shasha Guo, Jing Zhang, Yanling Wang, Qianyi Zhang, Cuiping Li, Hong Chen

Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective (2025.findings-acl)

Challenge:	Existing methods on knowledge base question generation learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraph.
Approach:	They propose a graph contrastive learning-based retriever to model diverse subgraphs with meta-learner to learn semantics-specific and semantics agnostic knowledge on and across these tasks.
Outcome:	The proposed approach reduces learning difficulty and improves performance on two widely-adopted benchmarks on KBQG.

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Yuchen Wen, Keping Bi, Wei Chen, Jiafeng Guo, Xueqi Cheng

DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models (2025.emnlp-main)

Challenge:	Existing studies have shown that large language models (LLMs) can elicit implicit biases that hurt certain demographics without explicit harmful words.
Approach:	They propose three attack approaches to elicit agreements to biased viewpoints from LLMs from a psychometric perspective and built two benchmarks to compare them.
Outcome:	The proposed methods elicit agreements to biased viewpoints more effectively than baselines.

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YiQiu Guo, Yuchen Yang, Zhe Chen, Pingjie Wang, Yusheng Liao, Ya Zhang, Yanfeng Wang, Yu Wang

Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)

Challenge:	Existing approaches to reliability of large language models often lack self-correction or use costly post-hoc verification.
Approach:	They propose a decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction.
Outcome:	Extensive experiments across five benchmarks show the proposed framework improves truthfulness and factual accuracy.

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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo

SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection (2025.emnlp-main)

Challenge:	Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity.
Approach:	They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability.
Outcome:	The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability.

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Mei Guo, Chen Chen, Chunyan Hou, Yike Wu, Xiaojie Yuan

Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)

Challenge:	Existing methods for rumor detection on social media focus on static graphs, ignoring dynamic and incremental propagation . rumour detection on the social media platform is crucial to mitigating harmful effects of rumors.
Approach:	They propose a sliding window and memory-augmented attention model for rumor detection . they use a dynamic propagation graph and memory to capture the long-term dependency .
Outcome:	The proposed model is compared with the state-of-the-art models on two public datasets.

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Junfan Chen, Richong Zhang, Yongyi Mao, Hongyu Guo, Jie Xu

Challenge:	Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean.
Approach:	They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels.
Outcome:	The proposed framework improves the current art in uncovering the ground-truth relation labels.

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

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

Robustness in NLP

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

VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)

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Yuyan Chen, Jiyuan Jia, Jiaxin Lu, Siyue Li, Yu Guan, Ming Yang, Qingpei Guo

Task-wrapped Continual Learning in Task-Oriented Dialogue Systems (2025.findings-naacl)

Challenge:	Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent.
Approach:	They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer.
Outcome:	The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets.

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Min Zeng, Haiqin Yang, Xi Chen, Yike Guo

Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction (2023.emnlp-main)

Challenge:	Continual learning is vital for task-oriented dialogue systems (ToDs), but its performance is limited by training separate adapters for each task, preventing global knowledge sharing.
Approach:	They propose a framework that employs task-wrapped Adapters to learn global and task-specific information through parameter sharing.
Outcome:	The proposed framework outperforms AdapterCL in 37 domains while using only 46% of the parameters.

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Chong Zhang, Ya Guo, Yi Tu, Huan Chen, Jinyang Tang, Huijia Zhu, Qi Zhang, Tao Gui

Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic (2026.acl-long)

Challenge:	Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs).
Approach:	They propose a method to predict token sequences within visually-rich documents by a simple prediction head.
Outcome:	The proposed method can be used to predict token mentions as token sequences within documents.

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Yichuan Ma, Linyang Li, Yongkang Chen, Peiji Li, Xiaozhe Li, Qipeng Guo, Dahua Lin, Kai Chen

SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning (2026.findings-acl)

Challenge:	Large language models (LLMs) tackle complex reasoning tasks, but test-time scaling is becoming expensive.
Approach:	They propose to redefine test-time as wall-clock time, where models dynamically adjust strategies based on time budgets.
Outcome:	The proposed model improves time budget awareness and boosts performance across Timely-Eval.

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Boyan Shi, Wei Chen, Shuyuan Zhao, Junfeng Shen, Shengnan Guo, Shaojiang Wang, Huaiyu Wan

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

Challenge:	Existing methods for multitask learning fail to match input semantics with expert capabilities, leading to weak expert specialization.
Approach:	They propose a parameter-efficient mixture-of-experts framework for task-adaptive learning that aligns textual semantics with the most suitable experts for precise routing.
Outcome:	The proposed framework outperforms the state-of-the-art methods and holds excellent task generalization capabilities.

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

From Conversation to Evaluation: Benchmarking LLMs on Development Knowledge via SimpleDevQA (2026.findings-acl)

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

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Jing Zhang, Lianghong Guo, Yanlin Wang, Terry Yue Zhuo, Yong Wang, Mingwei Liu, Jiachi Chen, Ensheng Shi, Yuchi Ma, Hongyu Zhang, Zibin Zheng

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

Challenge:	Existing Dev Knowledge QA benchmarks are limited in development knowledge scope and often not built from real user queries.
Approach:	They conduct preliminary analysis of real user–LLM dialogues from WildChat to investigate the importance of Dev Knowledge QA in AI-assisted software development scenarios.
Outcome:	The proposed benchmark is based on real user–LLM dialogues from WildChat.

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

Sparse Adapter Fusion for Continual Learning in NLP (2026.eacl-long)

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

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Min Zeng, Xi Chen, Haiqin Yang, Yike Guo

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

Challenge:	Existing methods for learning continuously face challenges such as inefficient parameter reuse across tasks and catastrophic forgetting when tasks are dissimilar.
Approach:	They propose a Sparse Adapter Fusion Method which dynamically fuses old and new adapters to address these challenges.
Outcome:	The proposed method outperforms state-of-the-art methods while utilizing less than 60% of the parameters.

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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei

Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models (2026.findings-acl)

Challenge:	Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach:	They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome:	The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.

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Bajian Xiang, Tingwei Guo, Xuan Chen, Yang Han

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Large Speech Language Models (LSLMs) typically operate at high token rates to ensure acoustic fidelity, yet this results in sequence lengths that exceed the underlying semantic content, incurring prohibitive inference costs.
Approach:	They propose a token-based token merging mechanism that uses a training-free token pooling mechanism to reduce prefilling FLOPs by 27.48% while maintaining competitive accuracy.
Outcome:	The proposed method reduces prefilling FLOPs by 27.48% while maintaining competitive accuracy.

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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

Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications (2025.acl-long)

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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Zhe Chen, Yusheng Liao, Shuyang Jiang, Pingjie Wang, YiQiu Guo, Yanfeng Wang, Yu Wang

Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	Existing approaches to source planning fail to achieve this due to misalignment between the model’s expectation of the sources and their actual content.
Approach:	They propose a method to optimise large-scale medical knowledge models by combining multiple medical knowledge sources into one query.
Outcome:	The proposed method significantly improves multi-source planning performance while training a smaller model to learn source alignment.

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Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu, Zhenhua Ling, Wu Guo, Quan Liu, Zhigang Chen, Cong Liu

Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)

Challenge:	Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer.
Approach:	They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently.
Outcome:	The proposed method shows great generalization and compatibility across languages and fields.

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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen

MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale (2025.acl-long)

Challenge:	Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China.
Approach:	They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform.
Outcome:	The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower.

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Jiawei Guo, Tianyu Zheng, Yizhi Li, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Graham Neubig, Wenhu Chen, Xiang Yue

Trident: Self-Supervised Preference Alignment via Triplet Regularization (2026.findings-acl)

Challenge:	Current instruction-tuning datasets focus on simplistic visual question answering tasks, and provide phrase-level answers without any intermediate rationales.
Approach:	They propose to use open-source multimodal large language models to train MLLMs on a dataset with 12M instruction-response pairs to elicit CoT reasoning.
Outcome:	The proposed model achieves state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro, and MuirBench, and gains improvements of up to 4% on non-reasoning-based benchmarks.

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Yingnan Guo, Kejia Chen, Xiaofeng Zhang, Zifei Wu, Yu Zhang

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)

Challenge:	Large vision-Language Models suffer from noisy supervision and semantic ambiguity in self-supervised settings.
Approach:	They propose a self-supervised framework that constructs reliable preference triplets . they propose 'trident' objective that enforces semantic separation between the triplet components .
Outcome:	The proposed framework outperforms state-of-the-art RLHF and RLAIF benchmarks on LLaVA-1.5-7B and achieves 95.70% precision on POPE using only 4k self-generated triplets and a single epoch.

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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang

Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)

Challenge:	Foundational models and their checkpoints have advanced deep learning, boosting performance across applications.
Approach:	They propose a method for pruning fine-tuned models by calculating differences between them and original model.
Outcome:	The proposed method can improve performance across vision, NLP, and multi-modal benchmarks.

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Yuxia Wang, Minghan Wang, Yimeng Chen, Shimin Tao, Jiaxin Guo, Chang Su, Min Zhang, Hao Yang

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

Challenge:	Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions.
Approach:	They propose to capture the human disagreement distribution from the perspective of model calibration.
Outcome:	The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy.

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

SpecCache: Speculative KV Cache Reuse for Efficient RAG Serving (2026.acl-long)

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

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Zijian Wen, Tao Zhang, Shuangwu Chen, Shenghao Ye, Yu Guo, Qirui Chen, Jingxian Shuai, Yunpeng Hou, Huasen He, null Jianyang

VizoMem: A Visual-Textual Memory Framework for Efficient Long-Horizon Reasoning (2026.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) improves LLMs but faces high prefill latency during long contexts.
Approach:	They propose a method that uses deep-layer hidden-state norms to guide token selection . they propose to use deep-layered hidden-status norms as a proxy to guide the token selection.
Outcome:	The proposed SpecCache outperforms state-of-the-art (SOTA) benchmarks.

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Weijie Liang, Yuanfeng Song, Xing Chen, Caleb Chen Cao, Sirui Han, Yike Guo

Visual Data in NLP

Challenge:	Existing systems that use long-context modeling incur computational and memory overhead.
Approach:	They propose a visual memory framework that pre-rendered text into structured images and stored as visual notes for agentic systems.
Outcome:	The proposed system reduces token consumption while preserving effective long-term memory recall.

SafeMT: Multi-turn Safety for Multimodal Language Models (2026.acl-long)

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Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li, Chengkun Cai, Pengcheng Wen, Chi-Min Chan, Boyuan Chen, Yaodong Yang, Sirui Han, Yike Guo

INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)

Challenge:	Multi-turn dialogues pose a greater risk than single prompts, but existing safety benchmarks do not account for this situation.
Approach:	They propose a benchmark that features dialogues of varying lengths generated from harmful queries accompanied by images.
Outcome:	The proposed model reduces multi-turn Attack Success Rate (ASR) compared to existing guard models.

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Hengchao Shang, Zongyao Li, Daimeng Wei, Jiaxin Guo, Minghan Wang, Xiaoyu Chen, Lizhi Lei, Hao Yang

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)

Challenge:	Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module.
Approach:	They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information.
Outcome:	The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets.

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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou

RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning (2022.findings-naacl)

Challenge:	Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling.
Approach:	They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation.
Outcome:	The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes.

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Yaqing Wang, Xin Tian, Haoyi Xiong, Yueyang Li, Zeyu Chen, Sheng Guo, Dejing Dou

Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design (2026.findings-acl)

Challenge:	Pre-trained language models (PLMs) are a good starting point for downstream applications, but it is difficult to generalize them to new tasks given a few labeled samples.
Approach:	They propose to use Relation Graph augmented learning to improve the performance of few-shot natural language understanding tasks by rewriting the input sequence into a cloze question with masks.
Outcome:	Extensive experiments show that Relation Graph augmented learning (RGL) improves performance of prompt-based tuning strategies.

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Xu Guo, Qiming Ge, Jian Tong, Kedi Chen, Jin Zhang, Xiaogui Yang, Xuan Gao, Haijun Lv, Zhihui Lu, Yicheng Zou, Qipeng Guo

Using Review Combination and Pseudo-Tokens for Aspect Sentiment Quad Prediction (2025.findings-naacl)

Challenge:	Existing approaches to RLVR use multiple-choice questions as verifiable rewards . however, not all tasks provide reliable verification .
Approach:	They propose a framework that actively constructs high-quality distractors to block elimination shortcuts and promote deep reasoning.
Outcome:	The proposed method significantly improves reasoning capabilities of Large Language Models.

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Jiazhou Chen, Xu Jia, RuiQiang Guo

Sentiment Analysis

Challenge:	Existing models confuse implicit and explicit sentiment, making it difficult to extract quadruples effectively.
Approach:	They propose a framework that leverages distinct labeled features from diverse reviews and incorporates pseudo-token prompts to harness the semantic knowledge of pre-trained models.
Outcome:	The proposed framework improves over four public datasets, averaging 1.99% F1 improvement, particularly in instances involving implicit sentiment.

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

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

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

Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images (2026.findings-acl)

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Wen Zhang, Huajun Chen

Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction (2026.findings-acl)

Challenge:	Existing studies on understanding and reasoning with abstractive information from the visual modality have not explored the use of STructured and Abstractive Reasoning (STAR) on such data.
Approach:	They propose an automatic STAR data engine to synthesize images with MMRK to build multi-modal instructions with reliable chain-of-thought thinking for various STAR tasks.
Outcome:	The proposed framework outperforms GPT-4o in STAR and improves performance across 8 open-source MLLMs.

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Xingwu Chen, Zhanqiu Zhang, Steven Y. Guo, Difan Zou

R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities when provided with full information in a single turn, yet they exhibit substantial vulnerability in multi-turn interactions.
Approach:	They propose a generalizable training approach to stabilize multi-turn interactions by leveraging the model's intrinsic single-turn capabilities as stable internal anchors.
Outcome:	The proposed approach outperforms fine-tuning and abstention-based methods and exhibits strong cross-domain generalization.

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Wenlong Meng, Guo Zhenyuan, Lenan Wu, Chen Gong, Wenyan Liu, Weixian Li, Chengkun Wei, Wenzhi Chen

MFinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset (2025.findings-acl)

Challenge:	Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging.
Approach:	They propose a two-step privacy stealing attack that enables attackers to reconstruct PII entities from scrubbed training data where the PI I entities have been masked.
Outcome:	The proposed attack can reconstruct PII entities from scrubbed training data where the PI I entities have been masked.

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Jie Zhu, Junhui Li, Yalong Wen, Xiandong Li, Lifan Guo, Feng Chen

HOTVCOM: Generating Buzzworthy Comments for Videos (2024.findings-acl)

Challenge:	Existing financial benchmarks rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings.
Approach:	They propose a multilingual, multi-sector, and multi-task dataset called MFinMeeting that supports English, Chinese, and Japanese .
Outcome:	The proposed benchmark supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts.

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Yuyan Chen, Songzhou Yan, Qingpei Guo, Jiyuan Jia, Zhixu Li, Yanghua Xiao

Simple-VGC: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition (2026.acl-long)

Challenge:	Existing research focuses on generating descriptive comments in English . hot-comments are important for video marketing and branding, authors say .
Approach:	They propose a framework to generate hot-comments on a Chinese video dataset . they use a combination of visual, auditory, and textual data to generate them .
Outcome:	The proposed framework shows that it generates hot-comments on both the new and existing datasets.

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Ye Wang, Qianglong Chen, Siyuan Wang, Zejun Li, Shijie Guo, Zhirui Zhang, Zhongyu Wei

SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems (2025.findings-acl)

Challenge:	Existing multimodal large language models suffer from systematic failures in basic visual understanding.
Approach:	They propose a tool-augmented reasoning framework with three targeted compensation strategies to address these problems.
Outcome:	The proposed framework improves visual grounding by re-injecting the original image to mitigate visual forgetting, the authors show . the proposed framework also improves the accuracy of the visual inputs, the researchers show - and the results are promising .

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Ziyu Guo, Renrui Zhang, Hao Chen, Jialin Gao, Dongzhi Jiang, Jiaze Wang, Pheng-Ann Heng

Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)

Challenge:	SciVerse is a multi-modal scientific evaluation benchmark to assess large multi-models . it examines the scientific knowledge comprehension, multi-mod content interpretation and Chain-of-Thought reasoning . authors examine the scientific proficiency of LMMs in scientific domains based on their work .
Approach:	They propose a multi-modal scientific evaluation benchmark to thoroughly assess Large Multi-modal Models across 5,735 test instances in five different versions.
Outcome:	The proposed evaluation reveals critical limitations in LMMs' scientific proficiency and provides new insights into future developments.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP.
Approach:	They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations.
Outcome:	The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives.

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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

Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation (2022.naacl-main)

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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Dingcheng Li, Zheng Chen, Eunah Cho, Jie Hao, Xiaohu Liu, Fan Xing, Chenlei Guo, Yang Liu

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

Challenge:	Existing work on lifelong learning requires incremental memory space to learn a model . existing work on experience replay or elastic weighted consolidation requires incremental space .
Approach:	They propose a framework that leverages a recall optimization mechanism to memorize parameters of previous tasks via regularization and a domain drift estimation algorithm to compensate the drift between different domains in the embedding space.
Outcome:	The proposed framework outperforms SOTA models on paraphrase and dialog response generation tasks.

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

Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset (2026.acl-long)

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

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Qian Chen, Xianyin Zhang, Yanzhi Liu, Lifan Guo, Feng Chen, Chi Zhang

InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) have expanded capabilities beyond text understanding . a novel Chinese financial multimodal evaluation benchmark is used to evaluate LVLM capabilities .
Approach:	They propose a Chinese financial multimodal evaluation benchmark to evaluate LVLMs' capabilities . the model has an overall accuracy of 66.11% and an average score of 77.18 .
Outcome:	The proposed model achieves an overall accuracy of 66.11% on the question answering task and an average score of 77.18 on detection, recognition, and information extraction tasks.

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Meizhen Liu, Xu Guo, He Jiakai, Jianye Chen, Fengyu Zhou, Siu Hui

What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices (2025.acl-long)

Challenge:	Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs.
Approach:	They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models.
Outcome:	The proposed approach improves the performance of multilingual language models on low-resource languages.

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Zhi Chen, Qiguang Chen, Libo Qin, Qipeng Guo, Haijun Lv, Yicheng Zou, Hang Yan, Kai Chen, Dahua Lin

Challenge:	Existing methods to generate long-context instruction-tuning data are limited by poor quality and fewer than 35% of samples are multi-hop .
Approach:	They propose a framework that integrates a quality verification agent, a single-hop question generation agent, and a multi-hop questions merger agent to enhance model performance.
Outcome:	The proposed framework significantly improves data quality with high-quality, multi-hop, and diverse data.

NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)

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Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang

FOCUS: A Fine-Grained Customer-Oriented Sentiment Dialogue Summarization Dataset for Chinese Customer Service (2026.findings-acl)

Challenge:	Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles.
Approach:	They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words.
Outcome:	The proposed generator significantly improves performance and provides explainable word alignments.

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Qian Chen, Mengqiang Hu, Xin Guo

JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations (2026.acl-long)

Challenge:	Existing studies largely overlook fine-grained sentiment dynamics expressed by customers . current methods often exhibit misalignment between aspects and sentiments .
Approach:	They propose a three-stage approach to building an aspect-aware sentiment dataset . they use a fine-grained customer-oriented Chinese dialogUe summarization dataset based on this scheme .
Outcome:	The proposed model improves faithfulness and interpretability of the proposed dataset.

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Xinyi Xu, Bingguang Hao, Yongyi Xiong, Zimo Chen, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou, Shihan Dou

An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)

Challenge:	Multimodal large language models (MLLMs) are a common communicative strategy in human society, often using image-text interplay to express emotions and intentions.
Approach:	They propose to evaluate multimodal large language models (MLLMs)' understanding of self-deprecation in real-world conversations using 2,016 bilingual memes.
Outcome:	The proposed framework evaluates MLLMs' understanding of self-deprecation in real-world conversations.

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Zhuoran Li, Chunming Hu, Xiaohui Guo, Junfan Chen, Wenyi Qin, Richong Zhang

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

Challenge:	Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains.
Approach:	They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain.
Outcome:	Empirical studies on 7 different languages confirm the effectiveness of the proposed model.

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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

Challenge:	Existing methods focus on knowledge and linguistic patterns of characters.
Approach:	They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome:	The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.

SCCD: A Session-based Dataset for Chinese Cyberbullying Detection (2025.coling-main)

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Qingpo Yang, Yakai Chen, Zihui Xu, Yu-ming Shang, Sanchuan Guo, Xi Zhang

Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)

Challenge:	Existing work on cyberbullying detection in Chinese is underdeveloped due to the lack of comprehensive and reliable datasets.
Approach:	They propose to use Chinese social media sessions to analyze Chinese cyberbullying content to improve the quality of annotations.
Outcome:	The proposed dataset shows that it performs better than existing methods on Weibo and a major social media platform.

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Xinnan Guo, Qian Zhu, Qiuhui Shi, Xuan Lin, Liubin Wang, DaqianLi DaqianLi, Yongrui Chen

V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)

Challenge:	Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues.
Approach:	They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context.
Outcome:	The proposed method improves existing models and achieves state-of-the-art on three benchmarks.

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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou

Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction (2024.findings-acl)

Challenge:	Existing code-related benchmarks focus on single modality rather than visual game development.
Approach:	They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome:	The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Linan Zhu, Xiangfan Chen, Xiaolei Guo, Chenwei Zhang, Zhechao Zhu, Zehai Zhou, Xiangjie Kong

Sentiment Analysis

Challenge:	Current studies on aspect-based sentiment analysis focus on essential content for model generation, ignoring the incorporation of various noise during training.
Approach:	They propose a grid noise-diffusion pinpoint network (GDP) model that incorporates three new modules to tackle generation instability.
Outcome:	The proposed model reduces the generation instability of model learning and outputs by incorporating Consistency Likelihood Learning and GDP-FOR.

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

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

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

FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025.findings-acl)

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

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Mei Guo, Chen Chen, Chunyan Hou, Yike Wu, Xiaojie Yuan

Teaching Neural Module Networks to Do Arithmetic (2022.coling-1)

Challenge:	Existing methods for detecting rumors on social media focus on coarse-grained temporal information and ignore fine-grain temporal dynamics.
Approach:	They propose a fine-grained dynamic graph neural network model which incorporates fine-grain temporal information into a unified framework for rumor detection.
Outcome:	The proposed model improves on three public real-world datasets.

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Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs (2026.acl-long)

Challenge:	Neural Module Networks (NMNs) have limited reasoning abilities and lack numerical reasoning capability.
Approach:	They propose to integrate the original question in the interpreter and introduce addition and subtraction modules that perform numerical reasoning over numbers.
Outcome:	The proposed methods outperform previous state-of-the-art models on a subset of DROP and achieve competitive reasoning performance.

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Yuanxiang Liu, Songze Li, Xiaoke Guo, Zhaoyan Gong, Qifei Zhang, Huajun Chen, Wen Zhang

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

Challenge:	Existing approaches to large language models often exhibit cognitive rigidity, causing reasoning stagnation.
Approach:	They propose a training-free framework that mimics the interplay between intuition and deliberation.
Outcome:	The proposed framework outperforms state-of-the-art approaches on three benchmarks.

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

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

ReAlign: Structured Revision for Small Language Model Alignment (2025.findings-emnlp)

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Ruijun Chen, Jiajian Guo, Hongzhan Chen, Fanqi Wan, Qifan Wang, Xiaojun Quan

Long-form Hallucination Detection with Self-elicitation (2025.findings-acl)

Challenge:	weak policies struggle to generate informative on-policy samples and suffer from unstable gradients when trained on off-police signals from stronger models.
Approach:	They propose a training framework that combines stability of on-policy learning with reviser-assisted supervision.
Outcome:	The proposed training framework outperforms strong preference optimization baselines on AlpacaEval-2 and Arena-Hard.

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Zihang Liu, Jiawei Guo, Hao Zhang, Hongyang Chen, Jiajun Bu, Haishuai Wang

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

Challenge:	Existing methods for hallucination detection tend to decompose text into isolated statements, unable to understand contextual semantics.
Approach:	They propose a framework to leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge and understand contextual semantics.
Outcome:	The proposed framework enables self-elicitation to elicit expressions of knowledge and understand semantics.

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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

SAM3-I: Segment Anything with Instructions (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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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering (2024.findings-emnlp)

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

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Yu Zhang, Kehai Chen, Xuefeng Bai, Zhao Kang, Quanjiang Guo, Min Zhang

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

Challenge:	Knowledge graph question answering (KGQA) aims to provide factual answers to natural language questions by leveraging structured information stored in a knowledge graph.
Approach:	They propose a Question-guided Knowledge Graph Re-scoring method to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge.
Outcome:	The proposed method eliminates noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge.

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

ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing (2026.findings-acl)

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

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Xi Chen, Wei Xue, Yike Guo

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

Challenge:	Existing work on role-playing focuses on textual modalities, neglecting speech . et al., 2025) show that speech role-players can generate spontaneous responses with personalized traits based on the context.
Approach:	They propose a framework that allows models to deliver spontaneous responses with personalized verbal traits based on their role, scene, and spoken dialogue.
Outcome:	The proposed framework enhances speech role-playing by generating spontaneous responses with personalized traits based on their role, scene, and spoken dialogue.

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

Visual Data in NLP

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

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

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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.

ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)

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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma

CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)

Challenge:	Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities.
Approach:	They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks.
Outcome:	The proposed benchmark renders fragmented documents directly from Markdown.

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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan

FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)

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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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen

Powering Verifiable Learning via Automated Evolutionary Data Synthesis (2026.acl-long)

Challenge:	Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks.
Approach:	They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings.
Outcome:	The proposed framework achieves the strongest overall performance across all models.

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He Du, Bowen Li, Aijun Yang, Siyang He, Qipeng Guo, Kai Chen, Dacheng Tao

Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)

Challenge:	Existing approaches to building generalizable verifiable data are task-specific and lack a principled, universal evaluator of verifikatability.
Approach:	They propose a task-agnostic, strategy-guided, executably-checkable data synthesis framework that synthesizes problems, diverse candidate solutions and verification artifacts from a single source.
Outcome:	The proposed framework synthesizes problems, candidates, and verification artifacts from human-annotated and strategy-induced checks and iteratively discovers strategies.

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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui

Deep Reinforcement Learning for Entity Alignment (2022.findings-acl)

Challenge:	Existing models of layout reading order do not convey the complete reading order information in the layout.
Approach:	They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance .
Outcome:	The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization.

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Lingbing Guo, Yuqiang Han, Qiang Zhang, Huajun Chen

Representation Learning

Challenge:	Entity alignment (EA) methods identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves.
Approach:	They propose to model entity alignment as a sequential decision-making task where an agent sequentially decides whether two entities are matched or mismatched based on representation vectors.
Outcome:	The proposed framework consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.

Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliance (2026.eacl-long)

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Jingyi Chen, Zhimeng Guo, Jiyun Chun, Pichao Wang, Andrew Perrault, Micha Elsner

From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)

Challenge:	LISTEN is a controlled benchmark to disentangle lexical reliance from acoustic sensitivity in emotion understanding.
Approach:	They propose a benchmark to disentangle lexical reliance from acoustic sensitivity in emotion understanding.
Outcome:	LISTEN shows that current LALMs largely "transcribe" rather than "listen" authors note that models underutilize acoustic cues while relying on lexical semantics .

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Jiawei Liu, Xun Gong, Muli Yang, Xingrui Yu, Fen Fang, Xulei Yang, Ivor Tsang, Yunfeng hu, Hong Chen, Qing Guo

Challenge:	Recent large language model-based AD research offers new avenues to address this challenge.
Approach:	They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control.
Outcome:	The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines.

E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025.findings-emnlp)

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Zeyang Liu, Jingfeng Xue, Xiuqi Yang, Wenbiao Du, Jiarun Fu, Junbao Chen, Wenjie Guo, Yong Wang

Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search (2026.acl-long)

Challenge:	Existing factuality verification methods follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency.
Approach:	They propose a Decompose-Embed-Interact paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space.
Outcome:	The proposed paradigm shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space .

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Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, null Jianyang, Xiaobin Tan

Text Style Transfer Back-Translation (2023.acl-long)

Challenge:	Existing pruning methods rely on sequential revisions and unreliable critique signals . Existing methods fail to detect the loss of answer-critical data .
Approach:	They propose a table pruning framework which transforms table pruning to gold trajectory-supervised parallel search.
Outcome:	The proposed framework outperforms the strongest baseline pruning framework by 3.2% on various tabular reasoning tasks.

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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang

JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal Practice (2026.acl-long)

Challenge:	Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task.
Approach:	They propose a method to modify the style of inputs by modifying the source side of BT data.
Outcome:	The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs.

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Ziang Chen, Guannan Li, Fanlin Ji, Yipeng Kang, Jiaqi Li, Muhan Zhang, Yangtao Zhang, Li Tianjiao, Jiannan Wang, Xin Guo, Song-Chun Zhu, Bin Ling

Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

Challenge:	Existing legal benchmarks evaluate isolated tasks or exam-style questions, failing to capture the procedural interdependencies and adjudicative rigor inherent in professional practice.
Approach:	They propose a vertical, depth-oriented, domain-specific benchmark to evaluate Large Language Models (LLMs) in Chinese civil litigation.
Outcome:	The proposed benchmarks show that large language models exhibit an "illusion of competence" the results highlight a critical gap between fluent linguistic output and judicial reliability .

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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang

A Novel Paradigm Boosting Translation Capabilities of Large Language Models (2024.findings-naacl)

Challenge:	Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach:	They propose a model merging framework that modulates the contribution of each source model.
Outcome:	Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

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Jiaxin Guo, Hao Yang, Zongyao Li, Daimeng Wei, Hengchao Shang, Xiaoyu Chen

Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition (2025.acl-long)

Challenge:	Existing studies on LLMs focused on supervised fine-tuning but their effectiveness has been limited.
Approach:	They propose a paradigm consisting of three stages: Secondary Pre-training using extensive monolingual data, Continual Pre- training with interlinear text format documents, and Leveraging source-language consistent instruction for supervised fine-tuning.
Outcome:	The proposed approach surpasses previous work and achieves superior performance compared to models such as NLLB-54B(CITATION) and GPT3.5-text-davinci-003.

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Kehua Feng, Keyan Ding, Tan Hongzhi, Kede Ma, Zhihua Wang, Shuangquan Guo, Cheng Yuzhou, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

Challenge:	Existing methods for evaluation of large language models are inefficient and inefficient due to inaccuracy of standard metrics in human perception of text quality and inefficiency in sampling informative test examples.
Approach:	They propose a sample-efficient human evaluation method for large language models based on the principle of MAximum Discrepancy (MAD) competition.
Outcome:	The proposed method achieves the “golden” ranking of LLMs with a minimum set of input instructions, which in turn reveal their relative strengths and weaknesses.

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

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

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

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

CANDY: Benchmarking LLMs’ Limitations and Assistive Potential in Chinese Misinformation Fact-Checking (2025.findings-emnlp)

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

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Ruiling Guo, Xinwei Yang, Chen Huang, Tong Zhang, Yong Hu

Automatic Song Translation for Tonal Languages (2022.findings-acl)

Challenge:	CANDY is a benchmark to evaluate the capabilities and limitations of large language models (LLMs) for fact-checking misinformation.
Approach:	a team of researchers develop a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in Chinese.
Outcome:	CANDY is a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in China.

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Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, Jordan Boyd-Graber

Machine Translation

Challenge:	Existing automatic song translation systems for tonal languages do not match the number of notes and beat the original rhythm of the song.
Approach:	They propose three criteria for effective AST: preserving meaning, singability and intelligibility.
Outcome:	The proposed system balances semantics and singability with human evaluations.

League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models (2026.acl-long)

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Qianhong Guo, Wei Xie, Xiaofang Cai, Enze Wang, Shuoyoucheng Ma, Xiaobing Sun, Tian Xia, Kai Chen, Xiaofeng Wang, Baosheng Wang

Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering (2025.findings-acl)

Challenge:	Large language models (LLMs) have shown exceptional capabilities across a wide range of tasks, but reliable evaluation remains a challenge due to data contamination, opaque operation, and subjective preferences.
Approach:	They propose a benchmark-free evaluation paradigm that organizes multiple LLMs into a self-governed league for multi-round mutual evaluation.
Outcome:	Experiments on eight mainstream LLMs in mathematics and programming show that the proposed model can distinguish capabilities while maintaining high internal ranking stability.

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Zheng Chu, Huiming Fan, Jingchang Chen, Qianyu Wang, Mingda Yang, Jiafeng Liang, Zhongjie Wang, Hao Li, Guo Tang, Ming Liu, Bing Qin

PyramidCodec: Hierarchical Codec for Long-form Music Generation in Audio Domain (2024.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but they still face challenges in knowledge-intensive multi-hop reasoning.
Approach:	They propose a method that uses self-critique feedback to guide iterative reasoning by enabling iteration and self-evaluation of its intermediate reasoning steps.
Outcome:	The proposed method surpasses the previous SOTA by 8.6% on three multi-hop reasoning datasets.

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Jianyi Chen, Zheqi Dai, Zhen Ye, Xu Tan, Qifeng Liu, Yike Guo, Wei Xue

Speech & Audio in NLP

Challenge:	Existing approaches to generate long music are inefficient and lack of structured representation.
Approach:	They propose a hierarchical discrete representation of audio for long audio-domain music generation using residual vector quantization on different levels of features.
Outcome:	The proposed method achieves competitive performance in terms of reconstruction quality and token per second (TPS) the proposed method facilitates training a language model that can generate well-structured long-form music for up to 3 minutes.

DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router (2026.findings-eacl)

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Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng

CAP: Controllable Alignment Prompting for Unlearning in LLMs (2026.acl-long)

Challenge:	Existing RAG methods lack fine-grained control over query and source sides, resulting in noisy retrieval and shallow reasoning.
Approach:	They propose an agentic RAG framework that integrates information sieving via LLM-as-a-knowledge-router.
Outcome:	Experiments on multi-hop QA tasks across heterogeneous sources demonstrate improved reasoning depth, retrieval precision, and interpretability over conventional approaches.

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Zhaokun Wang, Jinyu Guo, Jingwen Pu, Hongli Pu, Meng Yang, Xunlei Chen, Jie Ou, Wenyi Li, Guangchun Luo, Wenhong Tian

Understanding Programs by Exploiting (Fuzzing) Test Cases (2023.findings-acl)

Challenge:	Existing methods for modifying parameters are unsystematic and rely on empirical experience.
Approach:	They propose a controllable alignment prompting for unlearning framework that decouples unlearning into a learnable prompt optimization process via reinforcement learning.
Outcome:	The proposed framework achieves precise, controllable unlearning without updating model parameters.

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

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

Challenge:	Semantic understanding of programs has attracted great attention in the community . large language models (LLMs) are capable of learning contextual information from data at scale .
Approach:	They propose to incorporate a relationship between inputs and possible outputs into learning for achieving a deeper semantic understanding of programs.
Outcome:	The proposed method outperforms current state-of-the-art on two programming tasks and outperformed current state of the art by large margins.

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

MURAL: Multimodal, Multitask Representations Across Languages (2021.findings-emnlp)

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

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Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge

Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)

Challenge:	Image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
Approach:	They propose a dual encoder that integrates image-text matching and translation pairs to solve two tasks by learning from billions of pairs.
Outcome:	The proposed encoder outperforms ALIGN's cross-modal retrieval performance on well-resourced languages and significantly improves on under-resource languages.

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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang

Analytical Reasoning of Text (2022.findings-naacl)

Challenge:	Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions.
Approach:	They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency .
Outcome:	The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo .

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

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Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li

PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025.acl-long)

Challenge:	Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view.
Approach:	Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .
Outcome:	Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .

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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.

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

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

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

Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of Stealing Privacy (2025.findings-acl)

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Guo Zhenyuan, Yi Shi, Wenlong Meng, Chen Gong, Chengkun Wei, Wenzhi Chen

Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (2024.findings-naacl)

Challenge:	Model merging is a widespread technology in large language models that integrates multiple task-specific LLMs into a unified one.
Approach:	They propose a model merging approach that trains a phishing model capable of stealing privacy using a privacy phish instruction dataset.
Outcome:	The proposed model cloaking method mimics a specialized capability to conceal attack intent, luring users into merging the phishing model.

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Jiashuo Sun, Yi Luo, Yeyun Gong, Chen Lin, Yelong Shen, Jian Guo, Nan Duan

Unsupervised Sign Language Translation and Generation (2024.findings-acl)

Challenge:	Chain-of-thought (CoT) prompting is a technique to enhance the reasoning abilities of Large language models (LLMs) however, the reasoning chains of demonstrations are observed to be prone to errors, which can lead to incorrect reasoning during inference.
Approach:	They propose an iterative bootstrapping technique to enhance the reasoning abilities of Large language models (LLMs) by generating a series of reasoning steps to obtain the answer, and using the reasoning chains as exemplars to demonstrate the task.
Outcome:	The proposed method improves the performance of Large language models (LLMs) on three reasoning tasks on ten datasets.

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Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang

EchoMLLM: Incentivizing Echocardiographic Video Understanding with Keyframe Grounding and Report Generation (2026.findings-acl)

Challenge:	Experimental results on the BBC-Oxford Sign Language dataset reveal that USLNet achieves competitive results compared to supervised baseline models.
Approach:	They propose an unsupervised sign language translation and generation network that learns from abundant single-modality data without parallel sign language data.
Outcome:	The proposed model achieves competitive results compared to baseline models on the BBC-Oxford Sign Language dataset and Open-Domain American Sign Language data.

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Heyu Huang, Wanran Sun, Chi Chen, Bo Chen, Zonghao Guo, Yuhua Li, Ruixuan Li, Kunlun He, Maosong Sun

Visual Data in NLP

Challenge:	Echocardiography analysis requires a dual capability: rigorous quantitative keyframe localization and comprehensive qualitative synthesis.
Approach:	They propose a unified framework designed for real-world echocardiography video understanding.
Outcome:	a new framework is designed to support real-world echocardiography video understanding . it reduces temporal grounding errors by up to 76% and improves report generation quality by 65% .

From Logical to Computational Sparsity: Structure-Aware Block-Sparse Attention for Long-Code Completion (2026.acl-long)

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Yanli Wang, Yanlin Wang, Bowen Zhang, Yiwei Zhang, Daya Guo, Jiachi Chen, Hongyu Zhang, Zibin Zheng

ProCeedRL: Process Critic with Explorative Demonstration Reinforcement Learning for LLM Agentic Reasoning (2026.findings-acl)

Challenge:	Existing sparse attention methods for long-context generation pose high latency . general sparsity methods cause excessive accuracy degradation without considering code structure .
Approach:	They propose a training-free Structure-aware block-sparse attention mechanism that bridges the gap between logical and computational sparsity.
Outcome:	The proposed method reduces TTFT by 45-55% while maintaining accuracy within 3% of dense attention.

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Jingyue Gao, Yanjiang Guo, Chen Xiaoshuai, Jianyu Chen

GPT-NER: Named Entity Recognition via Large Language Models (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) exhibit exceptional reasoning capabilities, driven by Reinforcement Learning with Verifiable Rewards (RLVR).
Approach:	They propose a method that uses a process-level critic to monitor interactions in real time, incorporating reflection-based demonstrations to guide agents in stopping the accumulation of errors.
Outcome:	The proposed approach exceeds the model’s saturated exploration performance and achieves superior performance on complex deep search and embodied tasks.

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Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang, Chen Guo

DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Large-scale language models (LLMs) have shown impressive ability for in-context learning with limited training data.
Approach:	They propose a novel sequence labeling task that transforms a sequence labeled as a text-generation task into a self-verification task that LLMs can adapt to.
Outcome:	The proposed model performs better on NER than supervised models on a variety of tasks . the proposed model can be easily adapted by LLMs to generate a text sequence .

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Hanghui Guo, Jia Zhu, Shimin Di, Weijie Shi, Zhangze Chen, Jiajie Xu

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Challenge:	Existing methods for generating large language models face limitations in key aspects such as retrieval triggers and contextual scrutiny of retrieval content.
Approach:	They propose a dynamic RAG method that uses cognitive detection and contextual retrieval optimization to determine when retrieval is needed and what to retrieve for LLMs.
Outcome:	The proposed method achieves superior performance on all tasks, demonstrating the effectiveness of the proposed method.

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions (2024.findings-acl)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo

Robustness in NLP

Challenge:	Existing large language models (LLMs)-backed generative search engines may not always be accurate.
Approach:	They propose to evaluate the robustness of retrieval-augmented generation in a realistic and high-risk setting where adversaries have only black-box system access.
Outcome:	The proposed model exhibits higher susceptibility to factual errors compared to LLMs without retrieval.

Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions (2026.acl-long)

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Guo Gan, Yuxuan Ding, Cong Chen, Yuwei Ren, Yin Huang, Hong Zhou

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models (2024.findings-naacl)

Challenge:	Existing reinforcement learning methods are expensive due to high latency and sample inefficiency . Currently, RL is limited to one-to-one state-action pairs .
Approach:	They propose a framework that shifts the training paradigm to Single State Multiple Actions and introduce a group-wise advantage estimator based on the averaged critic outputs.
Outcome:	The proposed framework achieves 7.5% and 8.3% success rate improvements on AndroidLab and AndroidWorld over UI-TARS-1.5-7B and attains 1.4x higher training efficiency than existing methods.

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Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

Challenge:	Traditional benchmarks for evaluating foundation models often fail to accurately represent their general abilities for human-centric tasks.
Approach:	They propose a bilingual benchmark to assess foundation models in the context of human-centric standardized exams such as college entrance exams, law school admission tests, and math competitions.
Outcome:	The proposed benchmark exceeds the average human performance on SAT, LSAT, and math competitions with 95% accuracy and 92.5% on the Chinese college entrance English exam.

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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

ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry (2025.acl-industry)

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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Qinwen Chen, Wenbiao Tao, Zhiwei Zhu, Mingfan Xi, Liangzhong Guo, Yuan Wang, Wei Wang, Yunshi Lan

QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods for Community Question Answering (CQA) focus on static knowledge, limiting their applicability to real-world scenarios.
Approach:	They propose a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism.
Outcome:	The proposed framework outperforms baselines on three industrial CQA datasets and achieves 25.9% improvement in vector similarity, reducing latency by 8.7%–23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

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Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo, Zhao-Yun Chen

Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)

Challenge:	Large language models lack reliability in scientific domains that require strict adherence to physical constraints.
Approach:	They propose a large-scale dataset constructed via a task-adaptive strategy and a hybrid verification protocol that combines deterministic solvers with semantic auditing to guarantee scientific rigor.
Outcome:	The proposed model outperforms baselines and general-purpose preference models and is competitive with proprietary models.

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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang

Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models (2025.findings-acl)

Challenge:	Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency.
Approach:	They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible.
Outcome:	The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets.

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You Li, Heyu Huang, Chi Chen, Kaiyu Huang, Chao Huang, Zonghao Guo, Zhiyuan Liu, Jinan Xu, Yuhua Li, Ruixuan Li, Maosong Sun

Towards Faithful Multi-step Reasoning through Fine-Grained Causal-aware Attribution Reasoning Distillation (2025.coling-main)

Challenge:	Existing MLLMs still struggle to achieve precise grounding in multi-image scenarios.
Approach:	They propose a Chain-of-Thought framework that integrates single-image grounding with multi-image comprehension to address this challenge.
Outcome:	The proposed model outperforms existing models in multi-image grounding tasks by 24.94% and surpasses larger 70B models.

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Zheng Chu, Jingchang Chen, Zhongjie Wang, Guo Tang, Qianglong Chen, Ming Liu, Bing Qin

Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System (P19-3)

Challenge:	Recent advances have witnessed large language models (LLMs) achieving significant milestones across various domains of natural language processing.
Approach:	They introduce fine-grained attribution reasoning distillation (FARD) which incorporates grounded citations to consolidate the relationships between reasoning steps.
Outcome:	The proposed method outperforms CoT distillation methods on mathematical and general reasoning benchmarks.

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Guo Zhipeng, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Jiannan Liang, Huimin Chen, Yuhui Zhang, Ruoyu Li

RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction (2026.findings-acl)

Challenge:	Existing systems for automatic poetry generation are model-oriented, resulting in poor user participation.
Approach:	They propose a human-machine collaborative Chinese classical poetry generation system called Jiuge . Jiuge allows users to revise unsatisfied parts of a generated poem draft repeatedly .
Outcome:	The proposed system allows users to revise unsatisfied parts of a generated poem draft repeatedly.

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Haonan Bian, Zhiyuan Yao, Sen Hu, Zishan Xu, Shaolei Zhang, Yifu Guo, Ziliang Yang, Xueran Han, Huacan Wang, Ronghao Chen

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

Challenge:	Existing benchmarks focus on casual conversation or task-oriented dialogue, failing to capture “long-term project-oriented” interactions where agents must track evolving goals.
Approach:	They propose a benchmark that simulates the dynamic evolution of memory in real-world projects.
Outcome:	The proposed benchmarks simulate the dynamic evolution of memory in real-world projects.

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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

The Program Testing Ability of Large Language Models for Code (2024.emnlp-industry)

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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Weimin Xiong, Yiwen Guo, Hao Chen

Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation (2022.findings-emnlp)

Challenge:	Recent development of large language models (LLMs) for code shows promise in achieving code intelligence.
Approach:	They explore the ability of large language models to generate automated test cases . they show +11.77% and +4.22% higher code pass rates on HumanEval+ .
Outcome:	The proposed models show higher pass rates on humanEval+ compared with the current state-of-the-art models.

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Xiaonan Li, Daya Guo, Yeyun Gong, Yun Lin, Yelong Shen, Xipeng Qiu, Daxin Jiang, Weizhu Chen, Nan Duan

Human-Agent Collaborative Paper-to-Page Crafting (2026.findings-acl)

Challenge:	Existing methods for contrastive pre-training ignore the relevance between codes in large code corpus.
Approach:	They propose a Soft-labeled contrastive pre-training framework with positive sample construction methods to learn functional-level code representation.
Outcome:	The proposed framework can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation.

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Qianli Ma, Siyu Wang, Chen Yilin, Yinhao Tang, Yixiang Yang, Chang Guo, Bingjie Gao, Zhening Xing, Yanan Sun, Zhipeng Zhang

Challenge:	Existing approaches to create project pages from academic papers have focused on static slides and posters, but the dynamic nature of webpages remains an unaddressed challenge.
Approach:	They propose a novel multi-agent system that deconstructs paper-to-page creation into a coarse-to fine pipeline from narrative planning to multimodal content generation and interactive rendering.
Outcome:	The proposed system generates high-quality, visually appealing pages in under 15 minutes for less than $0.1 .

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models (2026.acl-long)

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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Shaonan Liu, Guo Yu, Xiaoling Luo, Shiyi Zheng, Jie Liu, Wenting Chen, Linlin Shen

Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning (2026.findings-acl)

Challenge:	Existing benchmarks fail to evaluate egocentric clinical intent understanding of medical multimodal large language models.
Approach:	They propose a benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation and diagnostic interpretation.
Outcome:	The proposed benchmark addresses challenges of visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols.

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Longteng Guo, Yifan Wang, Pengkang Huo, Tailai Chen, Yuze Wu, Jing Liu, Xinxin Zhu

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization (2025.emnlp-main)

Challenge:	Recent advances in multimodal large language models demonstrate strong performance on visual reasoning benchmarks.
Approach:	They propose a benchmark for vision-centric reasoning that integrates visual and textual information for non-trivial reasoning.
Outcome:	The proposed benchmark exposes gaps between humans and current MLLMs and reveals limited benefits from test-time reasoning strategies.

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Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, Junbo Zhao

CritiQ: Mining Data Quality Criteria from Human Preferences (2025.acl-long)

Challenge:	Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL).
Approach:	They propose a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.
Outcome:	The proposed framework can boost LLMs’ reasoning ability by integrating external knowledge sources through retrieval-augmented generation (RAG) The proposed model can mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.

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Honglin Guo, Kai Lv, Qipeng Guo, Tianyi Liang, Zhiheng Xi, Demin Song, Qiuyinzhe Zhang, Yu Sun, Kai Chen, Xipeng Qiu, Tao Gui

Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)

Challenge:	Existing methods to train language models rely on manual design, perplexity, or careful prompt engineering.
Approach:	They propose a method that automatically mines criteria from human preferences for data quality with only 30 human-annotated pairs and performs efficient data selection.
Outcome:	The proposed method improves on human-annotated test sets and shows high accuracy on code, math, and logic domains.

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Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Existing knowledge graph completion models only evaluated candidate triples from content information.
Approach:	They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation.
Outcome:	The proposed model improves on two representative datasets and improves performance.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models (2025.findings-acl)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Bohan Jin, Shuhan Qi, Kehai Chen, Xinyi Guo, Xuan Wang

Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)

Challenge:	Large Multimodal Models (LMMs) have raised concerns about model toxicity.
Approach:	They propose a model to measure the toxicity gap between models and their hard level to determine whether they can handle dual-implicit toxicity.
Outcome:	The proposed model can handle dual-implicit toxicity effectively on 13 prominent LMMs, but its performance drops significantly in hard level.

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Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya

Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)

Challenge:	Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event .
Approach:	They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s.
Outcome:	The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance .

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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme

Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)

Challenge:	Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in .
Approach:	They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English.
Outcome:	The proposed techniques show that they perform better than any single strategy.

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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu

NutFrame: Frame-based Conceptual Structure Induction with LLMs (2024.lrec-main)

Challenge:	Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections.
Approach:	They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight.
Outcome:	The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%.

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Shaoru Guo, Yubo Chen, Kang Liu, Ru Li, Jun Zhao

Challenge:	Existing studies focus on syntactic knowledge and world knowledge, but conceptual structure is not well-understood.
Approach:	They propose a benchmark for coNceptual structure induction based on FrameNet . they use prompts to induce conceptual structure of Framenet with LLMs .
Outcome:	The proposed model is able to induce conceptual structure of FrameNet with LLMs.

How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention (2020.coling-main)

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Yue Guan, Jingwen Leng, Chao Li, Quan Chen, Minyi Guo

MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)

Challenge:	Recent work on multi-head attention mechanism shows heuristics and clues in analyzing various aspects of the mechanism.
Approach:	They propose to cluster attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features.
Outcome:	The proposed features can explain and calibrate different attention heads in Transformer models.

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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

Challenge:	Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases.
Approach:	They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process.
Outcome:	The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations.

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

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Zeqi Chen, Zhaoyang Chu, Yi Gui, Feng Guo, Yao Wan, Chuan Shi

Learning Diverse Responses with Prefix-Conditioned Supervised Fine-Tuning (2026.acl-long)

Challenge:	Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference.
Approach:	They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module.
Outcome:	The proposed method decouples structural reasoning from textual generation without updating the backbone.

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Zhiyuan Fan, Guanqiao Chen, Yanyi Huang, Mingkuan Zhao, Dadi Guo, Yi R. Fung

Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer (2020.acl-main)

Challenge:	Large language models exhibit highly homogeneous, repetitive responses, resulting in inefficient exploration.
Approach:	They propose a method that constructs semantically consistent yet distributionally distinct prior contents to different responses and decouple the one-to-many mapping.
Outcome:	The proposed method improves absolute performance by 5.3% and increases generation diversity by 198.3% on average while significantly enhancing output diversity and test-time scaling.

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Chulun Zhou, Liangyu Chen, Jiachen Liu, Xinyan Xiao, Jinsong Su, Sheng Guo, Hua Wu

Improved Unbiased Watermark for Large Language Models (2025.acl-long)

Challenge:	Existing methods to unsupervised style transfer lack fine-grained control of the influence from the target style.
Approach:	They propose a model that exploits the relevance of each output word to the target style . they pretrain a style classifier and train an attentional Seq2seq model to reconstruct input sentences .
Outcome:	The proposed model achieves state-of-the-art performance in terms of transfer accuracy and content preservation.

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Ruibo Chen, Yihan Wu, Junfeng Guo, Heng Huang

When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables (2026.acl-long)

Challenge:	Unbiased watermarks allow to distinguish between text generated by humans and machines without causing distortion.
Approach:	They introduce a family of unbiased, Multi-Channel-based watermarks that partition the language model into segments and promote token probabilities within a selected segment based on a watermark key.
Outcome:	The proposed watermarks preserve the original distribution of the language model and offer significant improvements in detectability and robustness over existing unbiased watermark systems.

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Shenghao Ye, Yu Guo, Dong Jin, Yuxiang Wang, Yikai Shen, Yunpeng Hou, Shuangwu Chen, null Jianyang, Xiaofeng Jiang

Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models (2024.naacl-long)

Challenge:	Extensive research shows that noisy data significantly degrades the performance of table reasoning in real-world applications.
Approach:	They propose a dual denoising framework for complex questions and large-scale tables that uses Tree-guided table pruning to remove irrelevant data step by step.
Outcome:	The proposed framework achieves outstanding performance on TableQA tasks with complex questions and large-scale tables.

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Yi Luo, Zhenghao Lin, YuHao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong

An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching (2022.aacl-short)

Challenge:	Guide-Align is a guideline-oriented approach to augment the safety and quality of Large Language Models.
Approach:	They propose a guideline-oriented method to augment the safety and quality of large language models.
Outcome:	The proposed method outperforms existing methods on three benchmarks and shows significant improvements in security and quality.

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Yankai Chen, Yifei Zhang, Huifeng Guo, Ruiming Tang, Irwin King