Papers by Asaf Shabtai
DIESEL: A Lightweight Inference-Time Safety Enhancement for Language Models (2025.findings-acl)
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| Challenge: | Large language models generate outputs that are not aligned with human values, such as toxic content, malicious use cases, and vulnerabilities to adversarial jailbreak attacks. |
| Approach: | They propose a lightweight inference-guidance technique that can be seamlessly integrated into any autoregressive LLM to semantically filter undesirable content during generation. |
| Outcome: | The proposed technique can be integrated into any autoregressive LLM to semantically filter undesirable content during generation. |
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack (2025.findings-emnlp)
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| Challenge: | Recent studies on detecting pretraining data in large language models have focused on sentence-level membership inference attacks (MIAs) but these methods often exhibit poor accuracy, failing to account for the semantic importance of textual content and word significance. |
| Approach: | They propose a method that leverages established natural language processing techniques to tag keywords in input text and then uses them to obtain probabilities and calculate their average log-likelihood to determine input text membership. |
| Outcome: | The proposed method exploits established natural language processing techniques to tag keywords in input text and calculate their average log-likelihood to determine input text membership. |