13 citations · 21 across the 5 of their papers we have counts for
5 papers
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
Siheng Li, Cheng Yang, Yichun Yin +6
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stym…
NewsDialogues: Towards Proactive News Grounded Conversation
Siheng Li, Yichun Yin, Cheng Yang +7
Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce…
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce
Chao Yang, Ru He, Fangquan Lin +3
Our goal is to build general representation (embedding) for each user and each product item across Alibaba's businesses, including Taobao and Tmall which are among the world's bigg…
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives
Wei Wang, Liangzhu Ge, Jingqiao Zhang +1
Following SimCSE, contrastive learning based methods have achieved the state-of-the-art (SOTA) performance in learning sentence embeddings. However, the unsupervised contrastive le…
SAS: Self-Augmentation Strategy for Language Model Pre-training
Yifei Xu, Jingqiao Zhang, Ru He +4
The core of self-supervised learning for pre-training language models includes pre-training task design as well as appropriate data augmentation. Most data augmentations in languag…