Papers by Guangzeng Han
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing classification models only consider the temporal variations of existing data . current models focus on English corpora, leaving time as domains unexplored . |
| Approach: | They propose a framework to generalize classifiers over time on four languages, English, Danish, French, and German. |
| Outcome: | The proposed framework can generalize classifiers over time on four languages, English, Danish, French, and German. |
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies either overlook temporal shifts or hardly capture rich shifting patterns of both semantic and knowledge. |
| Approach: | They develop a temporal adaptive learning framework that captures temporal shifts . they use medical ontology and other knowledge sources to integrate temporal adaptation . |
| Outcome: | The proposed framework improves classification tasks across multiple domains and domains with knowledge integration. |
Model-Agnostic Meta Learning for Class Imbalance Adaptation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to address class imbalance and data difficulty have been used to train models. |
| Approach: | They propose a framework that prioritizes challenging samples and minority classes over hard examples and their semantically similar neighbors to address class imbalance. |
| Outcome: | The proposed framework outperforms baselines on six imbalanced datasets and achieves substantial improvements for minority classes. |
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)
Copied to clipboard
Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
| Challenge: | MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture. |
| Approach: | They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content. |
| Outcome: | The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context. |
Length-Aware Multi-Kernel Transformer for Long Document Classification (2024.starsem-1)
Copied to clipboard
| Challenge: | Existing SOTA models segment long texts into equal-length snippets, but they have new challenges of context fragmentation and generalizability due to sentence boundaries and varying text lengths. |
| Approach: | They propose a Length-Aware Multi-Kernel Transformer to encode long documents by transformers and vectorize text length by the kernels to promote model robustness over varying document lengths. |
| Outcome: | The proposed model outperforms existing models on five benchmarks from health and law domains up to an absolute 10.9% improvement. |
Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Genetic Prompt combines genetic algorithms with Large Language Models to augment synthetic data generation. |
| Approach: | They propose a framework that combines genetic algorithms with LLMs to augment synthetic data generation. |
| Outcome: | The proposed framework outperforms state-of-the-art models and shows robust performance across generator models. |
What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for instruction-tuning data contain redundancy and low-quality samples. |
| Approach: | They propose an instruction data selection framework based on weighted in-context influence . they show that sample difficulty negatively correlates with in-constext influence. |
| Outcome: | The proposed method outperforms baselines under constrained data budgets while demonstrating that sample difficulty negatively correlates with in-context influence. |