47 citations · 59 across the 5 of their papers we have counts for
5 papers · 1 filter
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Kun Zhou, Yeyun Gong, Xiao Liu +8
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 uninforma…
Promptagator: Few-shot Dense Retrieval From 8 Examples
Zhuyun Dai, Vincent Y. Zhao, Ji Ma +7
Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is lim…
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
Kai Hui, Honglei Zhuang, Tao Chen +8
State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. To this end, models generally utilize an encoder-only (like BERT) paradig…
Neural Passage Retrieval with Improved Negative Contrast
Jing Lu, Gustavo Hernandez Abrego, Ji Ma +2
In this paper we explore the effects of negative sampling in dual encoder models used to retrieve passages for automatic question answering. We explore four negative sampling strat…
Aligning the Pretraining and Finetuning Objectives of Language Models
Nuo Wang Pierse, Jingwen Lu
We demonstrate that explicitly aligning the pretraining objectives to the finetuning objectives in language model training significantly improves the finetuning task performance an…