Papers by Zijian Huang
A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025.findings-emnlp)
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| Challenge: | Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings. |
| Approach: | They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings . |
| Outcome: | This paper summarises both automated and human evaluation metrics used to assess the quality of pun generation. |
[MASK] Insertion: a robust method for anti-adversarial attacks (2023.findings-eacl)
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| Challenge: | Existing studies have focused on adversarial defenses against pretrained language models. |
| Approach: | They propose an adversarial defensing algorithm that inserts tokens into input sequences . they show an improvement in accuracy between 3.2 and 11.1 absolute points . |
| Outcome: | The proposed algorithm improves model accuracy on clean and polluted inputs compared with state-of-the-art models . |
Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning (2026.acl-long)
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| Challenge: | Evidence-Augmented Policy Optimization (EAPO) improves long-context reasoning performance . Xu et al., 2025): large language models are a critical part of NLP . |
| Approach: | They propose an Evidence-Augmented Reasoning paradigm that uses a group-relative reward to improve evidence quality. |
| Outcome: | EAPO significantly improves long-context reasoning performance compared to baselines. |
Two-Stage Parameter Alignment for Multi-LoRA Merging in Large Language Models (2026.findings-acl)
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| Challenge: | Current general model merging methods are prone to parameter interference problems . a novel two-stage parameter alignment framework is proposed to address this problem . |
| Approach: | They propose a two-stage parameter alignment framework that integrates low-rank LoRAs . they propose to reduce the computational complexity of existing methods by preserving fine-grained functions . |
| Outcome: | The proposed framework exhibits greater robustness than other methods in high-rank and high-interference scenarios while preserving fine-grained functions. |
Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts (2026.acl-long)
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| Challenge: | Existing approaches to resolve explicit knowledge conflicts are based on semantic decoding and auxiliary embedding. |
| Approach: | They propose a framework that adjudicates conflicts by structuring the underlying logic. |
| Outcome: | Experiments show that the proposed framework improves on existing models. |
SKGSum: Structured Knowledge-Guided Document Summarization (2024.findings-acl)
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Qiqi Wang, Ruofan Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, Jiamou Liu, Xianda Zheng, Zijian Huang
| Challenge: | Existing summarization methods ignore the importance of summary structure, resulting in summaries that emphasize the most prominent information while omitting essential details from other sections. |
| Approach: | They propose a method that uses automatically extracted summary points to generate summaries. |
| Outcome: | The proposed methods improve quality and BERTScore of summaries and broaden the types of documents that can be effectively summarized. |
FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging (2025.findings-emnlp)
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| Challenge: | a new adaptive merging method is proposed to improve fine-tuning performance . traditional methods often encounter task interference when merging full fine-uning models . |
| Approach: | They propose an adaptive merging method that directly measures model parameters using the Frobenius norm . |
| Outcome: | The proposed method outperforms baseline methods in various fine-tuning scenarios. |
LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation (2026.findings-acl)
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| Challenge: | Existing approaches to fine-tune large language models are infeasible due to privacy regulations. |
| Approach: | They propose an offsite tuning framework that secures data privacy and model parameter and capability privacy. |
| Outcome: | The proposed framework secures data privacy and model parameter and capability privacy while preserving gradient alignment. |