Retrieval-Augmented Generation (RAG) has emerged as a promising solution to enhance Large Language Models (LLMs) by incorporating knowledge from external databases. This survey paper provides a comprehensive examination of the progression of RAG paradigms, including Naive RAG, Advanced RAG, and Modular RAG. …
Multi-label classification problems with thousands of possible classes are extremely challenging, especially when using in-context learning with large language models (LLMs). Demonstrating every possible class in the prompt is infeasible, and LLMs may lack the knowledge to precisely assign the correct labels. …
EcoAssistant, built on the principles outlined in the paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately", showcases an advanced application of AutoGen in AI-driven question answering. The system's implementation hinges on three pivotal features: …
In the ever-evolving landscape of artificial intelligence, the recent paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately" emerges as a groundbreaking study. This research paper delves into the complexities of utilizing Large Language Models (LLMs) in a cost-effective and accurate manner, specifically for code-driven question answering. This innovation builds on the capabilities of Autogen, a key component in enhancing the effectiveness of the model. …
In the rapidly evolving landscape of large language models (LLMs), enhancing their capabilities and performance is pivotal. Three prominent techniques that stand out in achieving this are: …
The machine learning community stands at the precipice of another significant transformation. While language model pipelines have garnered attention, the introduction of DSPy promises to reshape the landscape. Let's dive into this groundbreaking paper and its implications. …