Papers by Hao An

Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025.findings-emnlp)

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Md Mahbubur Rahman, Shufeng Hao, Chongyang Shi, An Lao, Jinyan Liu

Challenge:	rumor detection models often assume a simplistic one-to-one alignment between modalities . authors present a method that preserves hierarchical, non-linear relationships .
Approach:	They propose a method that uses hyperbolic geometry to preserve hierarchical relationships . it decomposes image and text content into three levels and embeds them in hyperbolical space .
Outcome:	The proposed method preserves hierarchical relationships rather than representing them at a flat semantic level.

Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)

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Yang Xu, Yu Wang, Hao An, Zhichen Liu, Yongyuan Li

Psycholinguistics

Challenge:	Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints.
Approach:	They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task.
Outcome:	The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies.

Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)

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Hao An, Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Yuexian Zou

Semantic Text Processing

Challenge:	Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text.
Approach:	They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics.
Outcome:	The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset.

ProtT3: Protein-to-Text Generation for Text-based Protein Understanding (2024.acl-long)

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Zhiyuan Liu, An Zhang, Hao Fei, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua

Semantic Text Processing

Challenge:	Language Models excel in understanding textual descriptions of proteins, but struggle to process texts.
Approach:	They propose a framework for Protein-to-Text Generation for Text-based Protein Understanding that integrates a PLM as its protein understanding module.
Outcome:	The proposed framework surpasses existing baselines and is highly efficient in protein-to-text generation.

Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)

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Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

Natural Language Interfaces

Challenge:	Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions.
Approach:	They propose a model-agnostic framework for long-term dialogue agents . they use event summary and persona management to enable reasoning .
Outcome:	The proposed framework incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs (2026.findings-acl)

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Xuan Feng, Bo An, Tianlong Gu, Liang Chang, Fengrui Hao, Peipeng Yu, Shuai Zhao

Responsible & Trustworthy NLP

Challenge:	Existing approaches to solve large language models address stereotypical and structural biases in isolation . however, prior paradigms address these in isolation, often at the expense of exacerbating the other .
Approach:	They propose a framework to tackle latent spurious feature correlations within input that drive erroneous reasoning shortcuts.
Outcome:	The proposed framework mitigates stereotypical and structural biases while preserving robust general reasoning capabilities.

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

Reasoning

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation (2026.acl-long)

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Hao Li, Jiayang Gu, Jingkuan Song, An Zhang, Lianli Gao

Responsible & Trustworthy NLP

Challenge:	Existing noise mitigation methods face limitations due to their task-specific design, model dependency, and significant computational overhead.
Approach:	They propose a model-agnostic noise label mitigation paradigm that uses an estimator model and a scoring function to assess the noise level of input pairs.
Outcome:	The proposed method is superior to existing methods in training tasks and tasks.

Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)

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Zhihong Zhu, Xuxin Cheng, Hao An, Zhichang Wang, Dongsheng Chen, Zhiqi Huang

Language Models

Challenge:	Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods.
Approach:	They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs.
Outcome:	The proposed framework can be built by directly prompting LLMs to understand user needs without training data.

Purging the Gray Zone: Latent-Geometric Denoising for Precise Knowledge Boundary Awareness (2026.findings-acl)

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Hao An, Yibin Lou, Jiayi Guo, Yang Xu

Language Models

Challenge:	Existing abstention fine-tuning methods cause models to suffer from label noise near the decision boundaries.
Approach:	They propose a latent space representation perspective for abstention fine-tuning . they propose 'geometric denoising' framework that constructs a truth hyperplane .
Outcome:	The proposed framework significantly enhances model truthfulness and demonstrates strong generalization in out-of-distribution scenarios.

Prompt Tuning for Unified Multimodal Pretrained Models (2023.findings-acl)

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Hao Yang, Junyang Lin, An Yang, Peng Wang, Chang Zhou

Semantic Text Processing

Challenge:	Prompt tuning has demonstrated success in natural language pretraining and even vision pretraining.
Approach:	They propose to apply prompt tuning to a unified sequence-to-sequence pretrained model by adding a sequence of learnable embeddings to each layer and finetuning the pretrained models on downstream tasks.
Outcome:	The proposed method outperforms other parameter-efficient tuning methods on multimodal models and is robust against adversarial attacks.

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

Language Models

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

Soul-Mix: Enhancing Multimodal Machine Translation with Manifold Mixup (2024.acl-long)

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Xuxin Cheng, Ziyu Yao, Yifei Xin, Hao An, Hongxiang Li, Yaowei Li, Yuexian Zou

Multimodality

Challenge:	Multimodal machine translation (MMT) aims to improve the performance of machine translation with the help of visual information.
Approach:	They propose a multimodal machine translation mixup method that integrates visual information into conventional text-only neural machine translation systems.
Outcome:	The proposed method outperforms existing models on a multi-directional dataset with fewer parameters and achieves new state-of-the-art performance.