15 citations · 30 across the 7 of their papers we have counts for
4 papers · 1 filter
Rethinking LLM Evaluation: Can We Evaluate LLMs with 200x Less Data?
Shaobo Wang, Cong Wang, Wenjie Fu +11
As the demand for comprehensive evaluations of diverse model capabilities steadily increases, benchmark suites have correspondingly grown significantly in scale. Despite notable ad…
Sanitize Your Responses: Mitigating Privacy Leakage in Large Language Models
Wenjie Fu, Huandong Wang, Junyao Gao +2
As Large Language Models (LLMs) achieve remarkable success across a wide range of applications, such as chatbots and code copilots, concerns surrounding the generation of harmful c…
MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector
Wenjie Fu, Huandong Wang, Chen Gao +3
The increasing parameters and expansive dataset of large language models (LLMs) highlight the urgent demand for a technical solution to audit the underlying privacy risks and copyr…
Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
Wenjie Fu, Huandong Wang, Chen Gao +3
Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) ca…