Papers by Zhiqing Xiao
D.Va: Validate Your Demonstration First Before You Use It (2025.acl-long)
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| Challenge: | In-context learning (ICL) heavily relies on selecting effective demonstrations to achieve outputs that better align with the expected results. |
| Approach: | They propose a method which integrates a demonstration validation perspective into this field and integrates it into the learning paradigm. |
| Outcome: | The proposed method surpasses all retrieval-based in-context learning techniques across both natural language understanding (NLU) and natural language generation (NLG) tasks. |
ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models (2025.findings-acl)
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Hao Chen, Haoze Li, Zhiqing Xiao, Lirong Gao, Qi Zhang, Xiaomeng Hu, Ningtao Wang, Xing Fu, Junbo Zhao
| Challenge: | Prior research has focused on optimizing general-purpose large language models to downstream tasks . however, these approaches inherently introduce data dependency, which hinders generalization and reusability. |
| Approach: | They propose an algorithm that localizes the most task-sensitive attention heads and prunes by restricting attention training updates to these heads, thereby reducing alignment costs. |
| Outcome: | The proposed algorithm achieves 2% performance improvement over baselines on three tasks while localizing the most task-sensitive attention heads. |
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models (2025.findings-naacl)
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| Challenge: | Text-to-image (T2I) models can be used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. |
| Approach: | They propose a more practical and universal attack that does not require the presence of a target model. |
| Outcome: | The proposed attack bypasses both text and image safety checkers while preserving high semantic alignment with the target prompt. |
AIGT: AI Generative Table Based on Prompt (2025.coling-main)
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| Challenge: | Tabular data is an essential resource for many fields, but current methods do not fully utilize the rich information available in tables. |
| Approach: | They propose a method that utilizes metadata information to generate tabular data . they propose long-token partitioning algorithms that enable AIGT to model tables of any scale . |
| Outcome: | The proposed approach achieves state-of-the-art on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system. |