Papers by Zeguan Xiao
Pruning Adatperfusion with Lottery Ticket Hypothesis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are computationally expensive to fine-tune and require large storage. |
| Approach: | They propose a method to identify the influence of each adapter module and a way to prune adapters based on the Lottery Ticket Hypothesis. |
| Outcome: | The proposed model reduces size significantly while keeping performance intact. |
Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)
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| Challenge: | Commercial large language models (LLMs) have made great progress in various NLP tasks. |
| Approach: | They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing. |
| Outcome: | The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies. |
Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, including instruction following, mathematical problem solving, and coding generation. |
| Approach: | They propose a method that truncates both preferred and dispreferred responses to match the shorter one’s length. |
| Outcome: | The proposed approach improves over standard implementations and achieves 11.8 points in AlpacaEval 2 and overall improvements across downstream tasks. |
Toward Automated Robustness Evaluation of Mathematical Reasoning (2026.findings-acl)
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Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang, Zhaoqian Dai, Hailiang Huang, Yun Chen, Guanhua Chen
| Challenge: | Existing robustness evaluations rely on hand-crafted templates or a limited set of perturbation rules, resulting in model failure. |
| Approach: | They propose a framework inspired by software stress testing that generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure. |
| Outcome: | The proposed framework generates adversarial variants dynamically for each LLM, minimizing the risk of data contamination. |
Representation-Guided Parameter-Efficient LLM Unlearning (2026.findings-acl)
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| Challenge: | Existing methods to unlearning large language models often memorize sensitive or harmful information, but they struggle with the forget-retain trade-off due to the polysemantic nature of LLMs parameters. |
| Approach: | They propose a representation-guided low-rank unlearning approach that leverages the geometric properties of representation spaces to achieve robust and precise unlearning. |
| Outcome: | The proposed approach outperforms state-of-the-art models on TOFU and WMDP benchmarks while maintaining higher model utility. |
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)
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| Challenge: | Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks. |
| Approach: | They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically. |
| Outcome: | The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios. |
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)
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| Challenge: | Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences. |
| Approach: | They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs. |
| Outcome: | The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models. |
Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem (2026.acl-long)
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| Challenge: | Large language models (LLMs) are inherently dual-use and can be leveraged for both beneficial and harmful purposes. |
| Approach: | They propose a retention-prioritized gradient synthesis framework that decouples task-specific gradient extraction from conflict-aware combination. |
| Outcome: | The proposed method achieves tighter alignment on WMDP Bio and RWKU benchmarks. |