Papers by Fang Guo

Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding (2020.emnlp-main)

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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Jiaxin Huang, Yu Meng, Fang Guo, Heng Ji, Jiawei Han

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)

Challenge:	Existing methods for aspect-based sentiment analysis of review text use only a few keywords describing each aspect/sentiment without using any labeled examples.
Approach:	They propose a weakly-supervised approach for aspect-based sentiment analysis which uses only a few keywords describing each aspect/sentiment without using any labeled examples.
Outcome:	The proposed method generates quality joint topics and outperforms baselines significantly on benchmark datasets.

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Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang

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

Challenge:	Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies .
Approach:	They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT .
Outcome:	The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.

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

Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection (2026.acl-long)

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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Xiaowei Zhu, Yubing Ren, Fang Fang, Shi Wang, Yanan Cao, Li Guo

Robustness in NLP

Challenge:	Existing methods for AI-generated text detection assume uniform token contributions, making them less robust under short sequences or localized token modifications.
Approach:	They propose a training-free method for AI-generated text detection based on an exon-aware token reweighting perspective.
Outcome:	The proposed method achieves state-of-the-art detection performance and robustness to adversarial attacks and varying input lengths.

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

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

DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack (2026.findings-acl)

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

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Hao Li, Yubing Ren, Yanan Cao, Yingjie Li, Fang Fang, Shi Wang, Li Guo

Robustness in NLP

Challenge:	Existing watermarking algorithms focus on defending against paraphrase and piggyback spoofing attacks, which can inject harmful content, compromise reliability, and undermine trust in attribution.
Approach:	They propose an algorithm capable of defending against paraphrase and spoofing attacks.
Outcome:	Experiments on large language models and language models show that DualGuard is the first watermarking algorithm capable of defending against both paraphrase and spoofing attacks.

Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework (2026.findings-acl)

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Zhuoshang Wang, Yubing Ren, Yanan Cao, Fang Fang, Xiaoxue Li, Li Guo

Robustness in NLP

Challenge:	Existing secret-key schemes tightly couple detection with injection . this dependency creates a fundamental barrier for real-world governance .
Approach:	et al. introduce a black-box framework for non-intrusive, third-party watermark verification . they propose a proxy model to amplify watermark-relevant signals and complementary relative measurements .
Outcome:	a new framework decouples detection from injection and assesses alignment of query text with watermark distributions.

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

GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning (2026.findings-acl)

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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Yongyi Liao, Wencan Lai, Jun Fang, Jinjin Guo, Xiaohui Zhang, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Qixia Jiang

Challenge:	Large language models demonstrate remarkable zero-shot generalization, but adapting to downstream tasks requires continual fine-tuning.
Approach:	They propose a method that incrementally constructs a pool of frozen, task-specific LoRA experts.
Outcome:	The proposed approach outperforms state-of-the-art methods in task-free and blurred-boundary settings.

Beyond Topology: Generative Node Importance Estimation via Structure-Guided Semantic Reasoning (2026.findings-acl)

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Kuofei Fang, Siyan Wu, Yu Guo, Bin Wu

Structured Data in NLP

Challenge:	Existing methods for estimating node importance are limited and rely on topological aggregation.
Approach:	They propose a generative reasoning framework that leverages Large Language Models to generate precise importance scores for entities in Knowledge Graphs.
Outcome:	Extensive experiments show that the proposed framework outperforms existing methods and is generalized across domains.

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

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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

AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity (2025.emnlp-main)

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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Yu Zhang, Dong Guo, Fang Wu, Guoliang Zhu, Dian Ding, Yiming Zhang

Green & Sustainable NLP

Challenge:	Existing methods for large language models with extended context lengths face significant computational challenges during the prefill phase.
Approach:	They propose a difference-aware, dynamic sparse attention mechanism that efficiently identifies critical attention regions at a finer stripe granularity while adapting to global contextual information.
Outcome:	The proposed model achieves a speedup of 1.44 while maintaining higher recall rates.

StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning (2024.acl-long)

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Shaolei Zhang, Qingkai Fang, Shoutao Guo, Zhengrui Ma, Min Zhang, Yang Feng

Text Generation

Challenge:	Existing simultaneous translation methods focus on text-to-text and speech-totext translation.
Approach:	They propose a Simul-S2ST model that jointly learns translation and simultaneous policy in a unified framework of multi-task learning.
Outcome:	The proposed model can perform offline and simultaneous speech recognition, speech translation and speech synthesis via an "All-in-One" seamless model.

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Responsible & Trustworthy NLP

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)

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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin

Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)

Challenge:	Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding.
Approach:	They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications.
Outcome:	The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows.

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

Information Retrieval

Challenge:	Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks.
Approach:	They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions .
Outcome:	The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation .

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun

Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs (2024.acl-long)

Challenge:	Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
Approach:	They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies.
Outcome:	The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%.

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Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, Ji-Rong Wen

Information Retrieval

Challenge:	Existing methods to determine the knowledge an LLM already possesses and the knowledge that requires the help of a search engine are expensive and require excessive computational costs.
Approach:	They propose a slim proxy model that detects missing knowledge in LLMs with a proxy model and use it to perform retrieval for the missing knowledge.
Outcome:	The proposed approach detects missing knowledge in LLMs with a slim proxy model and takes its answers as heuristic answers.

Mere Contrastive Learning for Cross-Domain Sentiment Analysis (2022.coling-1)

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Yun Luo, Fang Guo, Zihan Liu, Yue Zhang

Knowledge Graph-Driven Memory Editing with Directional Interventions (2025.findings-emnlp)

Challenge:	Existing approaches to cross-domain sentiment analysis are labor-intensive and time-consuming.
Approach:	They propose a modified contrastive objective with in-batch negative samples to allow sentence representations from the same class to be pushed closer while those from the different classes become further apart in the latent space.
Outcome:	The proposed model can achieve state-of-the-art in cross-domain and multi-domain sentiment analysis tasks while transferring knowledge learned in the source domain to the target domain.

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Jinhu Fu, Kun Wang, Chongye Guo, Junfeng Fang, Wentao Zhang, Sen Su

DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)

Challenge:	Large Language Models (LLMs) are hampered by inaccuracies and outdated information.
Approach:	They propose a framework that constructs knowledge graphs using available information to guide the direction of knowledge editing.
Outcome:	The proposed framework allows consistent, aligned, and stable information during large-scale editing scenarios.

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Yubing Ren, Yanan Cao, Hao Li, Yingjie Li, Zixuan ZM Ma, Fang Fang, Ping Guo, Wei Ma

Information Extraction & Text Mining

Challenge:	Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model .
Approach:	They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments.
Outcome:	The proposed dataset has over 56,000+ events and 242,000+ arguments.

A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Any Translation (2024.acl-long)

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Zhengrui Ma, Qingkai Fang, Shaolei Zhang, Shoutao Guo, Yang Feng, Min Zhang

Text Generation

Challenge:	Existing translation pipelines require additional cascade components to achieve speech-to-speech translation.
Approach:	They propose a non-autoregressive generation framework for simultaneous speech translation . it integrates both text-to-text and speech-tospeech tasks into a unified framework .
Outcome:	The proposed framework outperforms state-of-the-art models in speech-to-text and speech- to-speech tasks.

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

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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.

To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)

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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh

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

Challenge:	Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation.
Approach:	They propose a framework that relies on neuronal mechanisms to mitigate task interference . they decomposed task-specific representations into two complementary subspaces . their results offer new insights into mitigating task interference and improving knowledge fusion .
Outcome:	The proposed framework reduces task interference within neurons and improves knowledge fusion.

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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.

AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)

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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang

Green & Sustainable NLP

Challenge:	Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency.
Approach:	They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images.
Outcome:	The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images.

Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding (2026.acl-long)

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Wenkai Wang, Xiyun Li, Hongcan Guo, Wenhao Yu, Tianqing Fang, Haitao Mi, Dong Yu, Shengyu Zhang

Empowering Diffusion Models on the Embedding Space for Text Generation (2024.naacl-long)

Challenge:	Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates due to visually homogeneous elements and dense layouts.
Approach:	They propose to replace static consistency strategies with a learnable selection mechanism that selects the optimal target by critiquing its own proposals rendered on the screenshot.
Outcome:	The proposed model significantly improves both grounding and critiquing capabilities over 6 benchmarks.

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Zhujin Gao, Junliang Guo, Xu Tan, Yongxin Zhu, Fang Zhang, Jiang Bian, Linli Xu

Representation Learning

Challenge:	Recent work adapts diffusion models to textual data by diffusing on the embedding space.
Approach:	They propose an embedding diffusion model based on Transformer to solve the problem of embeddable space and denoising model.
Outcome:	The proposed model is more efficient than previous methods on seminal text generation tasks and is superior to existing models.

When Slower Isn’t Truer: Inverse Scaling Law of Truthfulness in Multimodal Reasoning (2026.findings-acl)

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Sitong Fang, Wenjing Cao, Jiahao Li, Xuyao Wang, Chi-Min Chan, Sirui Han, Juntao Dai, Yike Guo, Yaodong Yang, Jiaming Ji

Responsible & Trustworthy NLP

Challenge:	a study of slow reasoning models for multimodal reasoning finds that they are more prone to fabricating plausible yet false details when confronted with incomplete or misleading visual inputs.
Approach:	They conduct the first systematic study of the inverse scaling law in slow-thinking paradigms for multimodal reasoning.
Outcome:	The findings suggest that slower reasoning models are more prone to fabricating false details . the study analyzed 5,000-sample hierarchical prompt dataset by 50 participants .

Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence (2025.acl-long)

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Jinghan He, Kuan Zhu, Haiyun Guo, Junfeng Fang, Zhenglin Hua, Yuheng Jia, Ming Tang, Tat-Seng Chua, Jinqiao Wang

Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation (2025.findings-emnlp)

Challenge:	Existing methods focus on alignment training or decoding refinements but address symptoms at the generation stage without probing the underlying causes.
Approach:	They propose a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads.
Outcome:	The proposed method achieves superior performance compared to state-of-the-art approaches in mitigating hallucinations while maintaining high efficiency with negligible additional time overhead.

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Zhenglin Hua, Jinghan He, Zijun Yao, Tianxu Han, Haiyun Guo, Yuheng Jia, Junfeng Fang

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

Challenge:	Existing approaches to address hallucinations in large vision-language models require substantial computational cost and time.
Approach:	They propose to leverage sparse autoencoders to identify semantic directions closely associated with faithfulness or hallucination, extracting more precise and disentangled hallucinian-related representations.
Outcome:	The proposed method outperforms existing decoding approaches while maintaining transferability across different model architectures with negligible additional time overhead.

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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

Reasoning

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.

PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)

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Yun Luo, Yingjie Li, Xiangkun Hu, Qinglin Qi, Fang Guo, Qipeng Guo, Zheng Zhang, Yue Zhang

Information Extraction & Text Mining

Challenge:	Experimental results show that the main challenge lies in long context and perspective extraction.
Approach:	They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline .
Outcome:	The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform .

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Junshu Pan, Panzhong Lu, Yixuan Weng, QiYao Sun, Fang Guo, Zijie Yang, Qiji Zhou, Yue Zhang

Challenge:	Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs.
Approach:	They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback.
Outcome:	The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions.

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

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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