Papers by Junjie Chu
JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs (2025.acl-long)
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| Challenge: | Large language models (LLMs) have been used to mitigate misuse and to align with human values. |
| Approach: | They propose to use large-scale evaluations of various jailbreak attacks to identify key patterns and test them under eight advanced defenses. |
| Outcome: | The proposed attacks achieve high success rates but are easy to mitigate by defenses. |
CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges (2025.acl-long)
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| Challenge: | Empirical evaluations of large language models demonstrate that they improve performance in a wide range of tasks. |
| Approach: | They propose a label-free method for mitigating selection bias during inference by reformulating debiasing as an optimization task. |
| Outcome: | The proposed method mitigates selection bias and improves performance compared to existing methods. |
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models (2024.emnlp-main)
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| Challenge: | Existing GPT models allow users to interact with them for multiple rounds to optimize the task execution. |
| Approach: | They propose a conversation reconstruction attack targeting the contents of previous conversations between GPT models and benign users, i.e., the benign users’ input contents during their interaction with GPT. |
| Outcome: | The proposed attacks demonstrate that GPT-4's defense mechanisms are ineffective against these attacks. |