Papers by Hadi Askari
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias (2024.naacl-short)
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| Challenge: | Position bias is a tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. |
| Approach: | They propose to measure position bias in large language models for zero-shot summarization tasks by measuring position bias. |
| Outcome: | The proposed model performance and position biases lead to new insights and discussion on zero-shot summarization tasks. |
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations (2025.findings-naacl)
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| Challenge: | Existing studies on backdoor defense have focused on training phase, overlooking critical aspect of testing time defense. |
| Approach: | They propose to use demonstrations as a defense mechanism against backdoor attacks in black-box LLMs. |
| Outcome: | The proposed method outperforms existing defense baselines across most evaluation scenarios. |
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs (2026.acl-long)
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Wenjie Jacky Mo, Qin Liu, Xiaofei Wen, Dongwon Jung, Hadi Askari, Wenxuan Zhou, Zhe Zhao, Muhao Chen
| Challenge: | Existing red-teaming approaches for code generation rely on extensive human effort and are prone to generating malicious code under adversarial environments. |
| Approach: | They propose a red-teaming agent that engages victim models in multi-turn conversations to elicit vulnerable code. |
| Outcome: | Experiments show that RedCoder outperforms red-teaming methods in inducing vulnerabilities in code generation. |
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot summarization of abstractive summaries for given articles, but little is known about their robustness at this task. |
| Approach: | They propose a strategy that uses the most relevant sentences to generate an ideal summary and then paraphrases them to obtain a minimally perturbed dataset. |
| Outcome: | The proposed approach can be used to measure the robustness of LLMs as summarizers on a minimally perturbed dataset. |