Top Research Papers on Retrieval Augmented Generation

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.

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 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.

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.

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.

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.

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.

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 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.

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.

A successful chatbot application was developed to analyze press content related to SDG based on user questions within the framework of Sustainable Development Goals (SDG) such as equality, justice, economy, climate change and gender equality.

Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they often struggle to generalize across diverse and unseen datasets. Moreover, existing Time Series Foundation Models (TSFMs) still face challenges in handling non-stationary dynamics and distribution shifts, largely due to the lack of effective mechanisms for adaptation. To this end, we present TS-RAG, a retrieval-augmented generation framework for time series forecasting that enhances the generalization and interpretabil...

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.

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.

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.