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. …
In the ever-evolving landscape of machine learning, diffusion models have marked their territory as a groundbreaking class of generative models. The paper "Diffusion Models for Reinforcement Learning: A Survey" delves into how these models are revolutionizing reinforcement learning (RL). This blog aims to unpack the crux of the paper, highlighting how diffusion models are addressing long-standing challenges in RL and paving the way for future innovations. …
In the ever-evolving landscape of technology, the fusion of artificial intelligence with software development has opened new horizons. The paper "A Survey on Language Models for Code" provides a comprehensive overview of this fascinating evolution. From the early days of statistical models to the sophisticated era of Large Language Models (LLMs) and Transformers, the journey of code processing models has been nothing short of revolutionary. …
The research paper "A Survey on Large Language Model based Autonomous Agents" from Renmin University of China presents a detailed overview of the advancements in the field of autonomous agents driven by Large Language Models (LLMs). This paper provides insights into various aspects of agent architecture, including profiling, memory, planning, and action modules, along with their applications, evaluation strategies, and future directions. …