Introduction In the field of artificial intelligence, building an intelligent assistant that can understand and respond to user needs is a challenging task. PhiData, as an open-source framework, provides developers with the possibility to create AI assistants with long-term memory, rich knowledge, and powerful tools. This article will introduce the core advantages of PhiData, application … Read more
BioRAG: A RAG-LLM Framework for Biological Question Reasoning The question-answering systems in the life sciences face challenges such as rapid discovery, evolving insights, and complex interactions of knowledge entities, necessitating a comprehensive knowledge base and precise retrieval. To address this, we introduce BioRAG, a retrieval-augmented generation framework that combines large language models. First, we parse, … Read more
Follow our public account to discover the beauty of CV technology This article shares the ACL 2024 paper PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers, open-sourced by the Cambridge University team, empowering multi-modal large model RAG applications, and is the first pre-trained general multi-modal late-interaction knowledge retriever. Paper link: https://arxiv.org/abs/2402.08327 Project homepage: https://preflmr.github.io/ Introduction The … Read more
Source: Biological Large Models This article is approximately 3000 words long and is suggested to be read in 5 minutes. This article introduces an innovative biological question reasoning system that combines Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). In today’s rapidly advancing life sciences field, efficiently processing and answering complex biological questions has always … Read more
MLNLP community is a well-known machine learning and natural language processing community, covering NLP graduate students, university teachers, and corporate researchers both domestically and internationally. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the progress of beginners. … Read more
Source: DeepHub IMBA This article is about 2500 words long and suggests a reading time of 9 minutes. This paper proposes a new scheme that utilizes the multi-head attention layer of the decoder model instead of the traditional feed-forward layer activation. The existing RAG solutions may suffer because the embeddings of the most relevant documents … Read more
Vector distance is crucial in various fields such as mathematics, physics, engineering, and computer science. They are used to measure physical quantities, analyze data, identify similarities, and determine the relationships between vectors. This article provides an overview of vector distance and its applications in data science. What Is Vector Distance? Vector distance, also known as … Read more
Vector distances are crucial in various fields such as mathematics, physics, engineering, and computer science. They are used to measure physical quantities, analyze data, identify similarities, and determine relationships between vectors. This article will provide an overview of vector distances and their applications in data science. What Is Vector Distance? Vector distance, also known as … Read more
Reported by Machine Heart Editor: Chen Chen A 2.4B Memory3 outperforms larger LLM and RAG models. According to a message from the WeChat public account of Machine Heart: In recent years, large language models (LLMs) have gained unprecedented attention due to their extraordinary performance. However, the training and inference costs of LLMs are high, and … Read more
Source: DeepHub IMBA This article is approximately 3600 words long, and it is recommended to read it in 7 minutes. In this article, we will use the visualization library renumics-spotlight to visualize the multi-dimensional embeddings of the FAISS vector space in 2-D, and explore the possibility of improving the accuracy of RAG responses by changing … Read more