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Researchers from Microsoft Research Asia, Singapore University of Technology and Design, and Tsinghua University have developed a method called Instruction Tuning for open-source Large Language Models (LLMs). The method involves fine-tuning LLMs using feedback to improve their performance. The researchers have published a paper on this topic, which will be presented in 2023. Meanwhile, experts in the field are discussing the need for customization in LLMs and the challenges of using RAG and finetuned models. Additionally, there are guides available for building advanced RAG models and handling text data effectively using LLMs. Text summarization is highlighted as one of the most powerful use cases for LLMs, and there are practical insights available for designing the best-possible prompts for this task. Lastly, an article explores how to measure the success of RAG-based LLM systems.
In this article, Nicholaus Lawson explores how to measure the success of RAG-based LLM system https://t.co/j7zTKdVijX
In a beginner-friendly guide, Sofia Rosa explains how to handle text data effectively using LLMs. https://t.co/lCDG8Biubp
Text summarization has emerged as one of the most powerful use cases for LLMs; @labriataphd shares practical insights for designing the best-possible prompts for this specific task. https://t.co/DExB0C4srf
A new technique called "Prompt Tuning" has been proposed to enhance the performance of LLMs. By adding adjustable prompts to pre-trained models, task-specific performance is improved. π This is recommended for those interested in training LLMs. π¨βπ» https://t.co/QoOz7Cs6gp
Learn to build *advanced* RAG from scratch π€― Know the RAG basics already? Strengthen your understanding of retrieval, and tackle more sophisticated use cases with LLMs + data π οΈ: β LLM routing β Retrieval re-writing/ensembling/fusion Full guides π§βπ«: https://t.co/5YbN97gvYT https://t.co/FalFMjGsp0
The deeper I go into LLM use cases, the more the need for customization. RAG and finetuned models bridge that gap. But these solutions are not easy to get right. RAG only works if your retriever is effective and finetuning only makes sense if the data quality is good. That⦠https://t.co/ZzSTx1ItnE
[CL] Tuna: Instruction Tuning using Feedback from Large Language Models H Li, Y Liu, X Zhang, W Lu, F Wei [Microsoft Research Asia & Singapore University of Technology and Design & Tsinghua University] (2023) https://t.co/pChzbOsq8C - Instruction tuning of open-source LLMs like⦠https://t.co/XyG3VYrpS8 https://t.co/uloqvSEziF