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
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 2600 words long and is recommended to be read in 8 minutes. For hallucinations in large language models (LLM), knowledge graphs have proven to be superior to vector databases. When using large language models (LLMs), hallucination is a common issue. LLMs generate fluent and coherent text but often … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic community, industry, and enthusiasts in machine learning and natural language processing, especially for beginners. … Read more
Source: DeepHub IMBA This article is about 3000 words long and is recommended to be read in 5 minutes. This article will explore three effective techniques to enhance document retrieval in applications based on <strong>RAG</strong>. By combining these techniques, it is possible to retrieve documents that closely match user queries, thus generating better answers. The … Read more
Source: DeepHub IMBA This article is about 2700 words long, and it is recommended to read it in 5 minutes. This article mainly introduces 3 commonly used methods in Langchain. There are various methods to enhance the capabilities of Retrieval-Augmented Generation (RAG), one of which is called query expansion. Here we mainly introduce 3 commonly … Read more
1. Why Introduce LightRAG? Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, allowing LLMs to generate more accurate and contextually relevant responses, significantly improving utility in practical applications. • By adapting to domain-specific knowledge, RAG systems ensure that the information provided is not only relevant but also meets user needs. … Read more
Author: Raunak Jain March 18, 2024 Translator: Chen Zhiyan Proofreader: zrx This article is approximately 3700 words long and is suggested to be read in 9 minutes. How to build a process-configurable compound AI system using open-source tools. Original Title: Design Patterns for Compound AI Systems (Conversational AI, CoPilots & RAG) Original Link: https://medium.com/@raunak-jain/design-patterns-for-compound-ai-systems-copilot-rag-fa911c7a62e0 What … Read more
Source: DeepHub IMBA This article is approximately 5400 words long and is recommended for a reading time of over 10 minutes. This article discusses 12 pain points encountered during the development of RAG pipelines (7 of which are sourced from papers, and 5 from our own summary), and proposes corresponding solutions to these pain points. … Read more