Top Research Papers on RAG

A systematic literature review of the application of retrieval-augmented generation systems in educational settings suggests that many approaches discussed across studies could be strategically aligned with the integration of DevOps practices and RAG, enhancing their use through automation, continuous improvement, and the agile adoption of technologies within educational processes.

A RAG task categorization method is proposed, classifying user queries into four levels based on the type of external data required and primary focus of the task: explicit fact queries, implicit fact queries, interpretable rationale queries, and hidden rationale queries.

This exploratory study demonstrates technical feasibility and baseline user acceptance for RAG-based chatbots in education, showing promise for addressing information accessibility challenges.

A RAG framework using Pinecone as a vector database, mixedbread- ai embeddings, and Gemini-1.5-pro for generation is presented, showing improved factual accuracy, reduced hallucinations, and enhanced user trust, making the system suitable for real-world enterprise and academic applications.

This study investigates how curated terminology can improve Large Language Model-based Retrieval-Augmented Generation (RAG) systems for industrial knowledge management and outlines future directions towards adaptive, human-centered knowledge systems in manufacturing.

A narrative and thematic review of the evolution of retrieval-augmented generation technologies in maternal health, structured across five axes: technical foundations of RAG, advancements in biomedical LLMs, conversational agents in healthcare, clinical validation frameworks, and specific applications in obstetric telehealth.

A Retrieval-Augmented Generation (RAG) system designed to provide automated assistance in ITS architectural design is proposed, which leverages the capabilities of Large Language Models (LLMs) while integrating knowledge from ITS reference architectures—established and validated frameworks.

A new paradigm for enhancing bibliometric analysis and knowledge retrieval in urban research is introduced, positioning an AI agent as a powerful tool for advancing research evaluation and understanding.

This session will explore how RAG was integrated into wiki edit preparation, highlighting methodological choices, tools used, and key challenges encountered in an educational setting.

The RAG pipeline greatly enhanced overall performance of Llama-3 from 57.50% to 81.50% and GPT-4-turbo' s accuracy increased from 80.38% to 91.92% on BCSC and from 77.69% to 88.65 % on OphthoQuestions.

Retrieval-Augmented Generation (RAG) has gained significant attention from many researchers as an effective solution to address the hallucination issue of Foundational Models (FMs), particularly Large Language Models (LLMs). Although the RAG framework is considered a successful approach for enhancing LLMs by providing a suitable retrieval mechanism to obtain appropriate external knowledge, it still has limitations in acquiring high-quality knowledge from diverse data sources. The complementary integration of RAG and data spaces is proposed to exploit RAG’s capabilities within data spaces. Data...

This paper introduces RAGCap, a retrieval-augmented framework that leverages similarity-based retrieval to select relevant image-caption pairs from the training dataset, and suggests RAG methods like RAGCap offer a scalable, practical alternative to fine-tuning for domain adaptation in RS image captioning.

This work advances dynamic health monitoring for power equipment by balancing interpretability, accuracy, and domain adaptability, providing a cost-effective optimization pathway for scenarios with limited annotated data.

The design, implementation, and evaluation of a complete RAG pipeline for Document Question Answering (DocQA) using FAISSbased semantic retrieval and the Llama3 model running locally through Ollama is presented.

Retail-GPT engages in human-like conversations, interprets user demands, checks product availability, and manages cart operations, aiming to serve as a virtual sales agent and test the viability of such assistants across different retail businesses.

The study explores the basic architecture of RAG, focusing on how retrieval and generation are integrated to handle knowledge-intensive tasks, and examines ongoing challenges such as scalability, bias, and ethical concerns in deployment.

This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems to improve Question Answering (QA) tasks from a target corpus and introduces QuIM-RAG (Question-to-question Inverted Index Matching), a novel approach for the retrieval mechanism in this system.

An Agentic Retrieval-Augmented Generation (RAG) system that enhances chatbot-based academic advising by integrating a BERT-based agent to filter and validate retrieved information, ensuring contextually relevant and factually accurate responses.

An innovative score-based hybrid retrieval strategy is proposed, which demonstrates superior performance in knowledge-based question-answering tasks within the telecom operator domain, significantly improving the accuracy and efficiency of information retrieval.

This work improves upon the original RAGCol paper by expanding the size and quality of COL-KG, adding efficiencies to the automatic video colorizer and incorporating semantic similarity search as opposed to the original Cypher query-based search.