1 citations · 1 across the 5 of their papers we have counts for
5 papers
RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
Sai Hao, Hao Zeng, Hongxin Wei +1
Efficiently routing queries to the optimal large language model (LLM) is crucial for optimizing the cost-performance trade-off in multi-model systems. However, most existing router…
Provable Training Data Identification for Large Language Models
Zhenlong Liu, Hao Zeng, Weiran Huang +1
Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However, existing works typically treat this t…
How does Bayesian Sampling help Membership Inference Attacks?
Zhenlong Liu, Wenyu Jiang, Feng Zhou +1
Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Existing state-of-the-art attacks typically rely on tra…
On the Noise Robustness of In-Context Learning for Text Generation
Hongfu Gao, Feipeng Zhang, Wenyu Jiang +3
Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from…
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
Wenyu Jiang, Zhenlong Liu, Zejian Xie +3
The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task per…