Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

In this paper, we introduce a novel and simple method for obtaining high-quality text
embeddings using only synthetic data and less than 1k training steps. Unlike existing …

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating
up-to-date information, mitigating hallucinations, and enhancing response quality …

In traditional RAG framework, the basic retrieval units are normally short. The common
retrievers like DPR normally work with 100-word Wikipedia paragraphs. Such a design …

In long context scenarios, large language models (LLMs) face three main challenges: higher
computational/financial cost, longer latency, and inferior performance. Some studies reveal …

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 …

This survey explores the synergistic potential of Large Language Models (LLMs) and Vector
Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferation …

Dense retrieval calls for discriminative embeddings to represent the semantic relationship
between query and document. It may benefit from the using of large language models …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …