Papers by Zikang Zhang
Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)
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| Challenge: | Existing temporal language models are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components. |
| Approach: | They propose a method that captures syntactically changed tokens and captures the relationship between the time prefix and tokens. |
| Outcome: | The proposed method outperforms existing temporal language models on two datasets and three tasks. |
IDEATE: Detecting AI-Generated Text Using Internal and External Factual Structures (2024.lrec-main)
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| Challenge: | Existing methods to detect AI-generated text rely on internal evidences, but external evidences are not considered. |
| Approach: | They propose a hierarchical graph network that utilizes internal and external factual structures to detect AI-generated text. |
| Outcome: | The proposed network outperforms current state-of-the-art methods on four datasets. |
Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models (2026.findings-acl)
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| Challenge: | Existing safety-related methodologies for large language models are lacking . despite advances in safety alignment techniques, safeguarding LLMs during adaptation to various tasks remains a challenge. |
| Approach: | They propose a framework to quantify how different parameters affect LLM safety . they propose two targeted intervention paradigms for safety enhancement and preservation . |
| Outcome: | The proposed framework reveals safety-critical patterns across different LLM architectures. |
M-RangeDetector: Enhancing Generalization in Machine-Generated Text Detection through Multi-Range Attention Masks (2025.findings-acl)
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| Challenge: | Existing supervised methods for text detection are overfitting within their training domains. |
| Approach: | They propose a method that integrates four distinct attention masking strategies into a Multi-Range Attention module to learn various writing strategies for machine-generated text detection. |
| Outcome: | The proposed method improves the generalization capability of existing detectors on three datasets. |
A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
| Outcome: | The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration. |