This survey reviews works in which language models (LMs) are augmented with reasoning
skills and the ability to use tools. The former is defined as decomposing a potentially …

We introduce INSTRUCTOR, a new method for computing text embeddings given task
instructions: every text input is embedded together with instructions explaining the use case …

In the past decades, recommender systems have attracted much attention in both research
and industry communities, and a large number of studies have been devoted to developing …

As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …

Ranking documents using Large Language Models (LLMs) by directly feeding the query and
candidate documents into the prompt is an interesting and practical problem. However …

We introduce C-Pack, a package of resources that significantly advance the field of general
Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a …

Large language models are powerful text processors and reasoners, but are still subject to
limitations including outdated knowledge and hallucinations, which necessitates connecting …

Abstract Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known
as multitask-prompted fine-tuning (MT), have shown capabilities to generalize to unseen …

While dense retrieval has been shown effective and efficient across tasks and languages, it
remains difficult to create effective fully zero-shot dense retrieval systems when no relevance …

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