1 citations · 2 across the 13 of their papers we have counts for
15 papers
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation
Zixu Wang, Xiaoran Zhao, Yahui Liu +8
Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challenging. Existing agentic approach…
Efficient RLVR Scheduling via Graph-Structured Online Difficulty Estimation
Zhizhao Liu, Zhiliang Tian, Xi Wang +4
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but relies on costly rollout exploration. Assigning the same expl…
Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations
Xinyue Fang, Zhiliang Tian, Zhen Huang +5
Existing LLM hallucination mitigation methods, including prompt engineering and model optimization, either hardly alter models'internal knowledge or have poor cross-domain generali…
CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher
Tianlun Liu, Zhiliang Tian, Zhen Huang +5
Text understanding often suffers from domain shifts. To handle testing domains, domain adaptation (DA) is trained to adapt to a fixed and observed testing domain; a more challengin…
Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling
Xinyue Fang, Zhen Huang, Zhiliang Tian +6
LLMs obtain remarkable performance but suffer from hallucinations. Most research on detecting hallucination focuses on the questions with short and concrete correct answers that ar…
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Zhihua Wen, Zhiliang Tian, Zexin Jian +5
Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations. The knowledge boundary (KB) of an LLM limits its factual understanding, beyond wh…
Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a Multi-agent Attacker-Disguiser Game
Qianqiao Xu, Zhiliang Tian, Hongyan Wu +4
With the enhanced performance of large models on natural language processing tasks, potential moral and ethical issues of large models arise. There exist malicious attackers who in…
LTOS: Layout-controllable Text-Object Synthesis via Adaptive Cross-attention Fusions
Xiaoran Zhao, Tianhao Wu, Yu Lai +5
Controllable text-to-image generation synthesizes visual text and objects in images with certain conditions, which are frequently applied to emoji and poster generation. Visual tex…
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification
Yiping Song, Juhua Zhang, Zhiliang Tian +3
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmen…
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation
Shilong Pan, Zhiliang Tian, Liang Ding +3
Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data, prompting research into unsupervised methods. Unsupervised neur…
GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence
Zhihua Wen, Zhiliang Tian, Wei Wu +4
Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on m…
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation
Menglong Lu, Zhen Huang, Yunxiang Zhao +3
Self-training emerges as an important research line on domain adaptation. By taking the model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the…
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification
Menglong Lu, Zhen Huang, Zhiliang Tian +3
Text classification is a fundamental task for natural language processing, and adapting text classification models across domains has broad applications. Self-training generates ps…
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Haoqi Zheng, Qihuang Zhong, Liang Ding +4
Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effect…
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Peijie Dong, Xin Niu, Zhiliang Tian +5
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field. Conventi…