Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-
ranging applications spanning machine learning, computer vision, natural language …

Contrastive learning has become a popular approach in natural language processing,
particularly for the learning of sentence embeddings. However, the discrete nature of natural …

Large-scale vision-language pretrained (VLP) models like CLIP have shown remarkable
performance on various downstream cross-modal tasks. However, they are usually biased …

This paper presents a whitening-based contrastive learning method for sentence
embedding learning (WhitenedCSE), which combines contrastive learning with a novel …

Contrastive learning has been extensively studied in sentence embedding learning, which
assumes that the embeddings of different views of the same sentence are closer. The …

Sampling proper negatives from a large document pool is vital to effectively train a dense
retrieval model. However, existing negative sampling strategies suffer from the uninformative …

Sentence embedding, which aims to learn an effective representation of the sentence, is
beneficial for downstream tasks. By utilizing contrastive learning, most recent sentence …

Unsupervised sentence embedding aims to obtain the most appropriate embedding for a
sentence to reflect its semantics. Contrastive learning has been attracting developing …

The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language
Models (PLMs) efficiently, precisely and continuously. Although existing methods can deal …