7 papers
SimDiff: Depth Pruning via Similarity and Difference
Yuli Chen, Shuhao Zhang, Fanshen Meng +4
Depth pruning improves the deployment efficiency of large language models (LLMs) by identifying and removing redundant layers. A widely accepted standard for this identification pr…
Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models
Shuhao Zhang, Yuli Chen, Jiale Han +2
Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text. Correspondingly, stealing watermark algorith…
A Robust Multi-Item Auction Design with Statistical Learning
Jiale Han, Xiaowu Dai
We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credi…
Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction
Xiang Li, Jiabao Gao, Sipei Lin +5
The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet unanswered question is whether th…
DLP: Dynamic Layerwise Pruning in Large Language Models
Yuli Chen, Bo Cheng, Jiale Han +3
Pruning has recently been widely adopted to reduce the parameter scale and improve the inference efficiency of Large Language Models (LLMs). Mainstream pruning techniques often rel…
DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue
Xiang Li, Duyi Pan, Hongru Xiao +5
Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual a…