Papers with adversarial fine-tuning
SafeQuant: LLM Safety Analysis via Quantized Gradient Inspection (2025.naacl-long)
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| Challenge: | Existing approaches to jailbreak Large Language Models (LLMs) use computationally intensive verification or require adversarial fine-tuning, leaving models vulnerable to advanced attacks. |
| Approach: | They propose a framework that leverages quantized gradient patterns to identify harmful prompts efficiently. |
| Outcome: | The proposed framework outperforms existing defenses across multiple benchmarks while maintaining model utility. |
Dynamic Evil Score-Guided Decoding: An Efficient Decoding Framework For Red-Team Model (2025.findings-acl)
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Cong Gao, Bo Zhang, Linkang Yang, Minghao Hu, Zhunchen Luo, Xiaoying Bai, Guotong Geng, Jun Zhang, Yunhua Xue
| Challenge: | Existing red-teaming methods require expensive fine-tuning, especially for large LLMs. |
| Approach: | They propose a red-teaming method that uses an ‘evil score’ to evaluate the potential of tokens to contribute to harmful outputs during decoding. |
| Outcome: | The proposed method achieves an ASR of 92.83% on the Llama-3.2-3B-Instruct model, compared to 83.48% with adversarial fine-tuning while using less computational resources. |
Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025.emnlp-main)
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Yinuo Wang, Baiyang Wang, Robert Mercer, Frank Rudzicz, Sudipta Singha Roy, Pengjie Ren, Zhumin Chen, Xindi Wang
| Challenge: | achieving comprehensive trustworthiness in medical QA poses significant challenges due to complexity of healthcare data, critical nature of clinical scenarios, and multifaceted dimensions of trustworthy AI. |
| Approach: | They examine six key dimensions of trustworthiness in medical QA . they compare how each dimension is evaluated in existing LLM-based systems . |
| Outcome: | The findings show that large language models have improved patient safety and effectiveness . the models exhibit critical trust failures when deployed in clinical settings . |
Vulnerability of LLMs’ Stated Belief? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly employed in question-answering tasks. |
| Approach: | They analyze how different persuasive strategies influence stated belief stability . they also examine whether verbalized confidence prompting increases vulnerability . |
| Outcome: | The proposed model exhibits extreme compliance, with 82.5% of belief changes occurring at the first persuasive turn. |