Papers by Yi Guo

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

Challenge:	Large language models (LLMs) have revolutionized general natural language preprocessing tasks, but their performance in financial domains is not evaluated comprehensively.
Approach:	They propose a framework to evaluate financial language models on financial tasks . they compare performance of auto-encoding language models and ChatGPT .
Outcome:	The proposed framework compares the performance of auto-encoding language models and the LLM ChatGPT on financial tasks.

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

LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)

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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Yi Jing, Zijun Yao, Hongzhu Guo, Lingxu Ran, Xiaozhi Wang, Lei Hou, Juanzi Li

Semantic Text Processing

Challenge:	Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus.
Approach:	They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders.
Outcome:	The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs .

Detoxifying Large Language Models via the Diversity of Toxic Samples (2025.emnlp-main)

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Ying Zhao, Yuanzhao Guo, Xuemeng Weng, Yuan Tian, Wei Wang, Yi Chang

FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)

Challenge:	Existing methods for analyzing and utilizing toxic samples are limited . current methods fail to fully harness their potential .
Approach:	They propose a diverse detoxification framework that leverages toxic samples' diversity . they propose MPSG strategy and SC-DPO approach to elicit personalized toxic responses .
Outcome:	The proposed framework could be used to optimize large language models for user safety . it incorporates two components: MPSG strategy and SC-DPO approach .

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Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang

Reasoning

Challenge:	Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies.
Approach:	They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning.
Outcome:	The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states.

SDBench: A Survey-based Domain-specific LLM Benchmarking and Optimization Framework (2025.acl-long)

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Cheng Guo, Hu Kai, Shuxian Liang, Yiyang Jiang, Yi Gao, Xian-Sheng Hua, Wei Dong

Semantic Text Processing

Challenge:	acquiring domain-specific knowledge often requires professional expert manpower.
Approach:	They propose a generic framework for generating evaluation datasets for domain-specific LLMs.
Outcome:	The proposed framework reduces the reliance on expert manpower while ensuring that the collected data is uniformly distributed.

Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022.acl-long)

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Yue Guo, Yi Yang, Ahmed Abbasi

JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions (2023.findings-acl)

Challenge:	Existing methods to mitigate human-like biases in pretrained language models are based on external corpora and require a distribution alignment loss to mitigate them.
Approach:	They propose an automatic method to mitigate biases in pretrained language models by searching for biased prompts such that cloze-style completions are the most different with respect to different demographic groups.
Outcome:	The proposed method reduces biases in pretrained language models, including gender and racial bias, and improves fairness of the models.

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Mo Yu, Yi Gu, Xiaoxiao Guo, Yufei Feng, Xiaodan Zhu, Michael Greenspan, Murray Campbell, Chuang Gan

Reasoning

Challenge:	Existing benchmarks focus on a single reasoning type and ask human annotators to write candidate statements related to the particular type of commonsense.
Approach:	They propose a new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs.
Outcome:	The proposed dataset is challenging to previous machine reading models and large language models with a significant 20%performance gap compared to human experts.

Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)

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Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, Bo Wang

Text Generation

Challenge:	Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability.
Approach:	They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes.
Outcome:	The proposed framework achieves remarkable controllability while keeping the text fluent and diverse.

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

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Yi Tu, Ya Guo, Huan Chen, Jinyang Tang

LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)

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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Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, Zhendong Mao

Challenge:	prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios.
Approach:	They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework.
Outcome:	The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data.

[MASK] Insertion: a robust method for anti-adversarial attacks (2023.findings-eacl)

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Xinrong Hu, Ce Xu, Junlong Ma, Zijian Huang, Jie Yang, Yi Guo, Johan Barthelemy

Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering (2022.findings-naacl)

Challenge:	Existing studies have focused on adversarial defenses against pretrained language models.
Approach:	They propose an adversarial defensing algorithm that inserts tokens into input sequences . they show an improvement in accuracy between 3.2 and 11.1 absolute points .
Outcome:	The proposed algorithm improves model accuracy on clean and polluted inputs compared with state-of-the-art models .

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Jpliu@wtu.edu.cn Jpliu@wtu.edu.cn, Shijie Mei, Xinrong Hu, Xun Yao, Jack Yang, Yi Guo

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

Challenge:	Existing methods for Knowledge Base Question Answering rely on semantic parsing and information retrieval.
Approach:	They propose a contrastive regularization based method to extract correct answer entities from a context knowledge base and a corresponding question.
Outcome:	The proposed method achieves state-of-the-art performance on the WebQuestionsSP dataset and the effectiveness of proposed modules is also evaluated.

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

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

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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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.

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

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

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

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

EconNLI: Evaluating Large Language Models on Economics Reasoning (2024.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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Yue Guo, Yi Yang

Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)

Challenge:	Large Language Models (LLMs) are widely used for writing economic analysis reports or providing financial advice, but their ability to understand economic knowledge and reason about potential results of economic events or actions lacks systematic evaluation.
Approach:	They propose a dataset to evaluate LLMs’ knowledge and reasoning abilities in the economic domain.
Outcome:	The proposed dataset evaluates LLMs’ knowledge and reasoning abilities in the economic domain.

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Wenxin Guo, Lei Zhang, Kun Zhang, Yi Liu, Zhendong Mao

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

Challenge:	Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities.
Approach:	They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions.
Outcome:	The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset.

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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

Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-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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Guisheng Liu, Yi Li, Zhengcong Fei, Haiyan Fu, Xiangyang Luo, Yanqing Guo

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Existing image captioning models require large trainable parameters to bridge visual and textual representations.
Approach:	They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model.
Outcome:	The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications (2023.emnlp-main)

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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Yue Guo, Chenxi Hu, Yi Yang

Information Retrieval

Challenge:	Existing methods for financial sentiment analysis use random splits of a dataset into training and testing to ensure there is no distribution shift between training and deployment.
Approach:	They propose a method that combines out-of-distribution detection with time series modeling for temporal financial sentiment analysis.
Outcome:	The proposed method improves the model’s ability to adapt to evolving temporal shifts in a volatile financial market.

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

On Synthetic Data Strategies for Domain-Specific Generative Retrieval (2025.acl-long)

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

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Haoyang Wen, Jiang Guo, Yi Zhang, Jiarong Jiang, Zhiguo Wang

Information Retrieval

Challenge:	Generative retrieval models can be used to generate ranked lists of potentially relevant document identifiers for a user query.
Approach:	They propose a synthetic data generation strategy for a two-stage training framework that focuses on learning to decode document identifiers from queries and a strategy for mining hard negatives based on initial model's predictions.
Outcome:	The proposed model can generate ranked lists of potentially relevant document identifiers for a user query and then refine ranking through preference learning.

LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization (2025.findings-emnlp)

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Yupeng Chang, Chenlu Guo, Yi Chang, Yuan Wu

Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)

Challenge:	Low-Rank Adaptation (LoRA) adapts large language models by training only a small fraction of parameters, but as the rank of the low-rank matrices increases, LoRA exhibits an unstable “double descent” phenomenon, which delays convergence and impairs generalization by causing instability due to the attraction to sharp local minima.
Approach:	They propose a framework that incorporates Momentum-Guided Perturbation Optimization (MGPO) MGPO stabilizes training dynamics by mitigating double descent phenomenon and guiding weight perturbations using momentum vectors from the optimizer’s state.
Outcome:	The proposed framework improves performance on natural language understanding benchmarks and shows that it improves convergence and generalization.

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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang

Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)

Challenge:	PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations .
Approach:	They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations .
Outcome:	The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD.

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Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, Zheng Zhang

Natural Language Interfaces

Challenge:	Existing work on dialogue meaning representations is limited in scalability for complex expressions.
Approach:	They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility .
Outcome:	The proposed representation can be easily extended to a task-oriented dialogue dataset.

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

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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

CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)

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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Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu

Information Extraction & Text Mining

Challenge:	Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events.
Approach:	They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists .
Outcome:	The proposed model improves on two widely used DEE datasets on the Internet.

HVGuard: Utilizing Multimodal Large Language Models for Hateful Video Detection (2025.emnlp-main)

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Yiheng Jing, Mingming Zhang, Yong Zhuang, Jiacheng Guo, Juan Wang, Xiaoyang Xu, Wenzhe Yi, Keyan Guo, Hongxin Hu

Triggerless Backdoor Attack for NLP Tasks with Clean Labels (2022.naacl-main)

Challenge:	Existing methods for hateful video detection rely on unimodal analysis or feature fusion . Existing tools struggle to capture cross-modal interactions and reason through implicit hate in sarcasm and metaphor .
Approach:	They propose a reasoning-based hateful video detection framework with multimodal large language models . they integrate Chain-of-Thought reasoning to enhance multimodal interaction modeling .
Outcome:	The proposed framework outperforms existing tools on two public datasets covering English and Chinese.

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Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Yi Yang, Shangwei Guo, Chun Fan

Robustness in NLP

Challenge:	Backdoor attacks are a new threat to neural natural language processing models due to the fragility and lack of interpretability of NLP models.
Approach:	They propose a method to perform backdoor attacks without an external trigger . they propose to use clean-labeled examples to generate poisoned clean-labelled examples .
Outcome:	The proposed strategy is effective and hard to defend due to its triggerless nature.

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

NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly (2023.emnlp-main)

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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Yi Fung, Tuhin Chakrabarty, Hao Guo, Owen Rambow, Smaranda Muresan, Heng Ji

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to understand acceptable behavior have focused on a single culture and manually built datasets from non-conversational settings.
Approach:	They propose a framework to automatically extract culture-specific norms from multi-lingual conversations.
Outcome:	The proposed framework extracts culture-specific norms from multi-lingual conversations.

Using Natural Sentence Prompts for Understanding Biases in Language Models (2022.naacl-main)

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Sarah Alnegheimish, Alicia Guo, Yi Sun

A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation (2022.naacl-main)

Challenge:	Recent work has shown that language models are susceptible to biases present in the training dataset.
Approach:	They propose to use natural sentence prompts to analyze gender-occupation biases in language models.
Outcome:	The proposed dataset can be used to analyze gender-occupation biases in language models.

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Kexun Zhang, Rui Wang, Xu Tan, Junliang Guo, Yi Ren, Tao Qin, Tie-Yan Liu

Multilinguality

Challenge:	Non-autoregressive translation models suffer from the multi-modality problem when a source sentence corresponds to multiple correct translations.
Approach:	They propose to decompose the syntactic multi-modality problem into short- and long-range models and evaluate them on synthesized and real datasets.
Outcome:	The proposed loss functions can handle short- and long-range syntactic multi-modalities better than existing models.

mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture (2024.findings-emnlp)

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Wei Zhang, Hongcheng Guo, Jian Yang, Zhoujin Tian, Yi Zhang, Yan Chaoran, Zhoujun Li, Tongliang Li, Xu Shi, Liangfan Zheng, Bo Zhang

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

Challenge:	Root cause analysis (RCA) in Micro-services architectures with escalating complexity is challenging due to fault propagation and circular dependencies among nodes.
Approach:	They propose a framework where multiple agents follow Agent Workflow and collaborate in blockchain-inspired voting to ensure the reliability of root cause analysis.
Outcome:	The proposed framework reduces the number of steps and standardizes task processing through Agent Workflow.

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

Text Generation

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.

FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization (2024.emnlp-main)

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Mingye Zhu, Yi Liu, Quan Wang, Junbo Guo, Zhendong Mao

Challenge:	Recent advances in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values.
Approach:	They propose a constrained optimization approach to detect and mitigate update regression with focal attention.
Outcome:	The proposed approach detects and mitigates update regression with focal attention while maintaining excellent overall performance.

CoLT5: Faster Long-Range Transformers with Conditional Computation (2023.emnlp-main)

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Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontanon, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai

NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models (2025.findings-emnlp)

Challenge:	Many natural language processing tasks require long inputs, but processing long documents with a Transformer model is expensive due to quadratic attention complexity and applying feedforward and attention projection layers to every input token.
Approach:	They propose a long-input Transformer model that builds on the intuition that some tokens are more important than others and uses conditional computation to devote more computation to important tokens.
Outcome:	The proposed model achieves stronger performance than LongT5 with faster training and inference, achieving SOTA on the long-input SCROLLS benchmark.

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Chenlu Guo, Yi Chang, Yuan Wu

Thinking Alignment of Scenario-Oriented User Simulation (2026.acl-long)

Challenge:	Parameter-efficient fine-tuning is essential for adapting large language models (LLMs). However, LoRA suffers from slow convergence and some recent LoRA variants, such as PiSSA, rely on Singular Value Decomposition (SVD) for initialization.
Approach:	They propose to introduce a small intermediate matrix between the low-rank matrices (A) and (B) and propose NyströmLoRA (NLoRA) which leverages Nyström-based initialization for SLoRA to improve its effectiveness and efficiency.
Outcome:	The proposed approach improves on 5 natural language generation tasks and 8 natural language understanding tasks with minimal parameter overhead.

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Xiaoting Wu, Yi Huang, Chunyang Gao, Mengfei Guo, Jingyu Yao, Junlan Feng

Dialogue Systems & Conversational Agents

Challenge:	Existing user simulators based on prompting to role-play or SFT focus on imitating textual utterances without considering multi-faceted cognitive processes that underlie human decision-making during interactions.
Approach:	They construct a user-simulator dataset that augments 51k human–LLM conversations by reconstructing the user’s inner reasoning during and at the end of each dialogue.
Outcome:	The proposed user simulators augment 51k human–LLM conversations by reconstructing the user’s inner reasoning both during and at the end of each dialogue.

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

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

Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)

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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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou

Low-Resource NLP

Challenge:	Existing methods for few-shot learning are insufficient to capture task variations in natural language domains.
Approach:	They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task.
Outcome:	The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets.

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

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