Media bias, ie, slanted news coverage, can strongly impact the public perception of the
reported topics. In the social sciences, research over the past decades has developed …

In this paper, we present a new embedding model, called M3-Embedding, which is
distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It …

The popularity of LLaMA (Touvron et al., 2023a; b) and other recently emerged moderate-
sized large language models (LLMs) highlights the potential of building smaller yet powerful …

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 …

Large language models (LLMs) have advanced to encompass extensive knowledge across
diverse domains. Yet controlling what a large language model should not know is important …

Language model pretraining has led to significant performance gains but careful
comparison between different approaches is challenging. Training is computationally …

This technical report describes the training of nomic-embed-text-v1, the first fully
reproducible, open-source, open-weights, open-data, 8192 context length English text …

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 …

In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP)
to the input improves the instruction-following ability of various Large Language Models …

Large Language Models (LMs) are known to encode world knowledge in their parameters
as they pretrain on a vast amount of web corpus, which is often utilized for performing …