8 citations · 17 across the 13 of their papers we have counts for
6 papers · 1 filter
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts
Zhenpeng Su, Zijia Lin, Xue Bai +8
Scaling the size of a model enhances its capabilities but significantly increases computation complexity. Mixture-of-Experts models (MoE) address the issue by allowing model size t…
HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus
Zhenpeng Su, Xing Wu, Wei Zhou +2
ChatGPT has garnered significant interest due to its impressive performance; however, there is growing concern about its potential risks, particularly in the detection of AI-genera…
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems
Zhenpeng Su, Xing Wu, Wei Zhou +2
Dialogue response selection aims to select an appropriate response from several candidates based on a given user and system utterance history. Most existing works primarily focus o…
CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval
Guangyuan Ma, Xing Wu, Peng Wang +1
Passage retrieval aims to retrieve relevant passages from large collections of the open-domain corpus. Contextual Masked Auto-Encoding has been proven effective in representation b…
CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval
Xing Wu, Guangyuan Ma, Peng Wang +4
Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-e…
ConTextual Masked Auto-Encoder for Dense Passage Retrieval
Xing Wu, Guangyuan Ma, Meng Lin +3
Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent s…