This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

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 …

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 limited …

Various techniques have been developed in recent years to improve dense retrieval (DR),
such as unsupervised contrastive learning and pseudo-query generation. Existing DRs …

We propose a simple and effective re-ranking method for improving passage retrieval in
open question answering. The re-ranker re-scores retrieved passages with a zero-shot …

We study the problem of retrieval with instructions, where users of a retrieval system
explicitly describe their intent along with their queries. We aim to develop a general-purpose …

Dense retrieval has become a prominent method to obtain relevant context or world
knowledge in open-domain NLP tasks. When we use a learned dense retriever on a …

In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a
simple yet effective pre-training method for dense passage retrieval. It employs a simple …

Multi-vector retrieval models such as ColBERT [Khattab et al., 2020] allow token-level
interactions between queries and documents, and hence achieve state of the art on many …

A ranker plays an indispensable role in the de facto'retrieval & rerank'pipeline, but its
training still lags behind--learning from moderate negatives or/and serving as an auxiliary …