Papers by Zhiliang Tian

GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)

Challenge:	Existing methods for LGT detection assume that it is a single homogeneous distribution.
Approach:	They propose a framework for LGT detection based on density-aware manifold learning and hybrid Mahalanobis energy.
Outcome:	The proposed framework outperforms baselines in detecting LLM-generated text (LGT) it is based on density-aware manifold learning and hybrid Mahalanobis energy .

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Zejiang He, Jingyuan Huang, Menglong Lu, Zhen Huang, Shanshan Liu, Zhiliang Tian, Dongsheng Li

Advancing Collaborative Debates with Role Differentiation through Multi-Agent Reinforcement Learning (2025.acl-long)

Challenge:	Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation.
Approach:	They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain.
Outcome:	The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition.

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Haoran Li, Ziyi Su, Yun Xue, Zhiliang Tian, Yiping Song, Minlie Huang

Representation Learning

Challenge:	Multi-agent collaborative tasks exhibit exceptional capabilities in natural language applications and generation.
Approach:	They propose a multi-LLM Cooperation framework with automatic role assignment capabilities that allows multiple agents to embed roles in turn-based speaking.
Outcome:	The proposed framework improves collaboration and expertise among agents and teams by enabling them to share roles and develop complementary strengths from the optimization level.

Context-aware Watermark with Semantic Balanced Green-red Lists for Large Language Models (2024.emnlp-main)

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Yuxuan Guo, Zhiliang Tian, Yiping Song, Tianlun Liu, Liang Ding, Dongsheng Li

Robustness in NLP

Challenge:	Recent research suggests that watermarking methods cause degradation of text quality due to semantic disparities between the watermarked text and the unwatermarked text.
Approach:	They propose a semantic-aware watermark method that generates a watermark key considering contexts to split a green/red list for watermark injection.
Outcome:	The proposed method reduces performance drop due to adding bias on green lists . it also allows green lists to cover almost all semantics .

Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)

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Haiyang Wang, Zhiliang Tian, Xin Song, Yue Zhang, Yuchen Pan, Hongkui Tu, Minlie Huang, Bin Zhou

Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022.acl-long)

Challenge:	Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity.
Approach:	They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models.
Outcome:	The proposed framework matches intents with hate mitigation intents and performs well.

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Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang

Information Extraction & Text Mining

Challenge:	Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available.
Approach:	They propose a memory imitation meta-learning method that enhances the model’s reliance on support sets for task adaptation.
Outcome:	The proposed method outperforms baselines on both text classification and generation tasks.

Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)

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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation (2024.acl-long)

Challenge:	Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored.
Approach:	They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Outcome:	The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models.

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Shilong Pan, Zhiliang Tian, Liang Ding, Haoqi Zheng, Zhen Huang, Zhihua Wen, Dongsheng Li

Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)

Challenge:	Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data.
Approach:	They propose a method that uses a dynamic graph to organize auxiliary languages in prompts to improve LRL translations.
Outcome:	The proposed method improves translation accuracy in low-resource languages (LRLs) using auxiliary language pairs and synthetic pseudo-parallel data.

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Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Jian Sun, Nevin L. Zhang

Language Models

Challenge:	Existing generative replay methods use only a single task-specific token to control their models.
Approach:	They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation.
Outcome:	The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems.

LLM-based Rumor Detection via Influence Guided Sample Selection and Game-based Perspective Analysis (2025.acl-long)

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Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li

Challenge:	Existing methods for rumor detection on social media are limited by limited modeling capacity and insufficient training corpora.
Approach:	They propose an SFT-based rumor detection model with Influence guided Sample selection and Game-based multi-perspective analysis to address these issues.
Outcome:	The proposed model outperforms existing SOTA on three datasets.

Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference (2025.emnlp-main)

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Libo Zhang, Zhaoning Zhang, null Xubaizhou, Rui Li, Zhiliang Tian, Songzhu Mei, Dongsheng Li

Green & Sustainable NLP

Challenge:	Large language models (LLMs) are demanding more memory and computational resources . however, these devices typically feature weaker GPUs and stronger CPUs .
Approach:	They propose a lossless inference acceleration method that leverages the characteristics of heterogeneous devices and the advantages of speculative decoding.
Outcome:	The proposed method achieves speedups ranging from 1.79 to 10.1 across different devices . it uses a draft model on the GPU to perform preliminary predictions, while a target model on CPU validates these outputs .

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)

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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao

CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher (2026.acl-long)

Challenge:	Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial.
Approach:	They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training.
Outcome:	The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training.

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Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li

DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)

Challenge:	Existing models for text understanding fail to adapt to domain shifts in real-world applications . current models do not improve themselves as they are applied to new domains .
Approach:	They propose a continual test-time adaptation framework that adapts to evolving domains . they propose accumulating domains and a refine-then-filter framework to calibrate teacher predictions .
Outcome:	The proposed model excels in a teacher-student framework adaptable to evolving domains.

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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li

DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)

Challenge:	Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage.
Approach:	They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation.
Outcome:	The proposed model excels on three datasets.

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Menglong Lu, Zhen Huang, Yunxiang Zhao, Zhiliang Tian, Yang Liu, Dongsheng Li

GRACE: Gradient-guided Controllable Retrieval for Augmenting Attribute-based Text Generation (2023.findings-acl)

Challenge:	Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model.
Approach:	They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set.
Outcome:	The proposed framework reduces label noise and preserves hard examples while maintaining accuracy.

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Zhihua Wen, Zhiliang Tian, Zhen Huang, Yuxin Yang, Zexin Jian, Changjian Wang, Dongsheng Li

Language Models

Challenge:	Existing methods for controlling the generation of pre-trained language models infuse domain bias into the generation process, making it difficult to generate out-of-domain texts.
Approach:	They propose a retrieval-augmented generation framework that uses retrieval to generate fluent sentences with high attribute relevance.
Outcome:	The proposed method can generate fluent sentences with high attribute relevance while keeping domain bias out of the model.

Correlation-Aware Example Selection for In-Context Learning with Nonsymmetric Determinantal Point Processes (2025.emnlp-main)

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Qiunan Du, Zhiliang Tian, Zhen Huang, Kailun Bian, Tianlun Liu, Zhaoning Zhang, Xinwang Liu, Feng Liu, Dongsheng Li

Hard Gate Knowledge Distillation - Leverage Calibration for Robust and Reliable Language Model (2022.emnlp-main)

Challenge:	Existing studies on in-context learning (ICL) focus on the selection of individual examples and ignore correlations among examples.
Approach:	They propose a method to capture positive and negative correlations using the determinantal point process . they optimize the method via kernel decomposition-based MLE to fit a constructed pseudo-labeled dataset .
Outcome:	The proposed method outperforms baselines in ICL example selection.

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Dongkyu Lee, Zhiliang Tian, Yingxiu Zhao, Ka Chun Cheung, Nevin Zhang

Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation (2026.findings-acl)

Challenge:	Existing knowledge distillation schemes focus on a teacher as a source of knowledge and a gauge to detect miscalibration of a student.
Approach:	They propose a method that uses a teacher model as a source of knowledge and a model as an error detector to detect miscalibration of a student.
Outcome:	The proposed scheme improves model generalization and significantly lowers calibration error.

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Hongyan Wu, Zhiliang Tian, Zhen Huang, Tengyue Hu, Linbo Qiao, Yifu Gao, Feng Liu, Dongsheng Li

Information Retrieval

Challenge:	Existing strategies focus on planning the next-turn dialogue strategies, while external strategy planners focus on generating empathetic responses.
Approach:	They propose a Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support.
Outcome:	The proposed framework can anticipate future emotions and achieve sustained emotional support on two datasets with two models.

Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues (2025.acl-long)

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Haiyang Wang, Zhiliang Tian, Yuchen Pan, Xin Song, Xin Niu, Minlie Huang, Bin Zhou

Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization (2021.acl-long)

Challenge:	Non-collaborative dialogue involves two participants with conflicting interests engaging in multiround dialogue to achieve their own goals.
Approach:	They propose a Game-based Adversarial self-play InterActive training paradigm which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning.
Outcome:	The proposed model achieves state-of-the-art performance on three datasets.

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Dongkyu Lee, Zhiliang Tian, Lanqing Xue, Nevin L. Zhang

Text Generation

Challenge:	Text style transfer aims to alter the style of a sentence while preserving its content.
Approach:	They propose to remove style information at token level and fuse it to style representations using conditional layer normalization.
Outcome:	The proposed model outperforms the state-of-the-art models in terms of content preservation and fluency.

StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer (2024.lrec-main)

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Kangchen Zhu, Zhiliang Tian, Jingyu Wei, Ruifeng Luo, Yiping Song, Xiaoguang Mao

Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory (2022.findings-emnlp)

Challenge:	Existing methods to separate content from style but some words contain both content and style information.
Approach:	They propose a method which uses a reversible encoder to improve content disentanglement.
Outcome:	The proposed method outperforms baselines on sentiment transfer and formality transfer tasks.

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Zhiliang Tian, Yinliang Wang, Yiping Song, Chi Zhang, Dongkyu Lee, Yingxiu Zhao, Dongsheng Li, Nevin L. Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing emotional conversation systems output responses according to either a given emotion or the user’s emotion reflected in the input queries.
Approach:	They propose to generate empathetic responses catering to the user’s emotions while leading the conversation to be emotionally positive by abstracting the conversation corpus and extracting the different responding strategies for different users’ emotions and conversational topics into a memory.
Outcome:	The proposed model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.

DPGA-TextSyn: Differentially Private Genetic Algorithm for Synthetic Text Generation (2025.findings-acl)

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Zhonghao Sun, Zhiliang Tian, Yiping Song, Yuyi Si, Juhua Zhang, Minlie Huang, Kai Lu, Zeyu Xiong, Xinwang Liu, Dongsheng Li

Ethical NLP

Challenge:	Existing methods to fine-tune large language models pose privacy risks . researchers have synthesized data with strong generation capabilities closed-source LLMs to alleviate this problem .
Approach:	They propose to combine general LLMs with genetic algorithm to produce relevant and diverse synthetic text under differential privacy constraints.
Outcome:	The proposed method significantly improves the performance of the model in downstream tasks while ensuring privacy.

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence (2023.findings-emnlp)

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Zhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang, Yanqi Shi, Zhen Huang, Dongsheng Li

Learning to Abstract for Memory-augmented Conversational Response Generation (P19-1)

Challenge:	Existing methods for generating stories with complex plots rely on detailed prompts, which inadvertently limit the creative potential of the generated stories.
Approach:	They propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce to enhance stories’ complexity.
Outcome:	The proposed framework enables generating more diverse plotlines from human-written stories.

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Zhiliang Tian, Wei Bi, Xiaopeng Li, Nevin L. Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing generative models for open-domain chit-chat conversations lack informativeness and diversity.
Approach:	They propose a retrieval-augmented generative model that learns to abstract from the training corpus and saves useful information to the memory to assist the response generation.
Outcome:	The proposed model outperforms other baselines in query-response clustering and learning to utilize these characteristics for response generation.

Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation (2020.acl-main)

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Zhiliang Tian, Wei Bi, Dongkyu Lee, Lanqing Xue, Yiping Song, Xiaojiang Liu, Nevin L. Zhang

Text Generation

Challenge:	Neural conversation models generate appropriate but non-informative responses in general.
Approach:	They propose to construct a document memory with anticipated responses in mind using a teacher-student framework and a student's input.
Outcome:	The proposed model outperforms the state-of-the-art for the Conversing by Reading task.

360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System (2024.findings-acl)

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Shen Gao, Hao Li, Zhengliang Shi, Chengrui Huang, Quan Tu, Shuo Shang, Zhiliang Tian, Minlie Huang