Beijing ZhiYuan Artificial Intelligence Research Institute, in collaboration with Renmin University of China's Gaoqing School of Artificial Intelligence, has launched an innovative AI model framework called MemoRAG. This framework, based on long-term memory, aims to advance Retrieval-Augmented Generation (RAG) technology to handle more complex tasks beyond simple question answering.

Nvidia's ChatQA2 model, optimized from Llama3, addresses key challenges in AI long-text context understanding and retrieval-augmented generation by extending the context window to 128K tokens and employing a three-phase instruction tuning process. It significantly enhances instruction adherence, RAG performance, and long-text comprehension. In the InfiniteBench evaluation, ChatQA2 demonstrated accuracy comparable to GPT-4-Turbo-2024-0409 and surp....

On July 11th, Kuaikan Comics announced that they are exploring fine-tuning of open-source large models to train a vertical large model for the two-dimensional (2D) domain. Kuaikan Comics utilizes large language models (LLM) and retrieval-augmented generation (RAG) technology to improve the conversion rate of work searches and inject new vitality into the search process. They have adopted a "fine-tuned large model + RAG enhancement" strategy, building internal PGC (Professional Generated Content)

Recently, two Chinese scholars from the Georgia Institute of Technology and NVIDIA have proposed a new fine-tuning framework called RankRAG. This framework greatly simplifies the complex RAG pipeline, enabling the same Large Language Model (LLM) to perform retrieval, ranking, and generation tasks, resulting in a significant performance improvement.RAG (Retrieval-Augmented Generation) is a commonly used technique in LLM deployment, especially suitable for text generation tasks that require a larg