Papers by Kaizhu Huang
Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)
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| Challenge: | Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question. |
| Approach: | They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K . |
| Outcome: | The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method . |
Logic Matters in Lightweight Hallucination Classification for RAG System (2026.acl-long)
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| Challenge: | Existing hallucination detection frameworks for RAGs lack robustness and performance . a compact model may lose track of precise information in retrieved segments or misinterpret a document's entailment score. |
| Approach: | They propose a lightweight, modular framework for hallucination detection in RAG systems . they capture logical relationships among retrieved documents within the vector space . |
| Outcome: | The proposed framework improves hallucination detection in RAG systems without complex architectures or pre-training on datasets. |
Unraveling LLM Jailbreaks Through Safety Knowledge Neurons (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved significant progress in alignment, ensuring safer and more reliable outputs. |
| Approach: | They propose a neuron-level interpretability method that focuses on the role of safety-related knowledge neurons to improve model robustness against jailbreak attacks. |
| Outcome: | The proposed method reduces attack success rates across multiple LLMs and outperforms all baseline defenses. |
Can GRPO Boost Complex Multimodal Table Understanding? (2025.emnlp-main)
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Xiaoqiang Kang, Shengen Wu, Zimu Wang, Yilin Liu, Xiaobo Jin, Kaizhu Huang, Wei Wang, Yutao Yue, Xiaowei Huang, Qiufeng Wang
| Challenge: | Existing table understanding methods struggle with low initialization accuracy and coarse rewards in tabular contexts. |
| Approach: | They propose a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities; (2) Perception Alignment GRPO (PA-GRPO); (3) Hint-Completion GR PO (HC-GRP); |
| Outcome: | The proposed framework outperforms existing models on held-in and held-out datasets, outperforming SFT and GRPO largely. |
Joint Multi-Label Attention Networks for Social Text Annotation (N19-1)
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| Challenge: | Present research shows that title metadata could affect social annotation. |
| Approach: | They propose a title-guided attention network for document annotation with user-generated tags that separates the title from the content of a document and applies a semantic-based loss regulariser over each sentence in the content. |
| Outcome: | The proposed approach outperforms the Bi-GRU and Hierarchical Attention Network (HAN) on two open datasets with 10%-30% reduction in training time. |
Prompt-based Zero-shot Text Classification with Conceptual Knowledge (2023.acl-srw)
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| Challenge: | Existing approaches to pre-training language models rely on verbalizers to translate the predicted vocabulary to task-specific labels. |
| Approach: | They propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting. |
| Outcome: | The proposed framework outperforms prompt-based approaches on four widely-used datasets for sentiment analysis and topic detection on the same experimental settings. |