15 citations · 44 across the 25 of their papers we have counts for
7 papers · 1 filter
MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models
Zhenpeng Su, Xing Wu, Xue Bai +5
Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonst…
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…
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
Guangyuan Ma, Xing Wu, Peng Wang +2
In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage th…
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…