Kg Rag

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge bases to improve accuracy and reduce hallucinations, a significant challenge in current LLMs. Research focuses on improving retrieval methods, such as optimizing query rewriting and employing knowledge graphs, and enhancing the integration of retrieved information with LLM generation, including exploring novel architectures like interleaved retrieval and generation. These advancements are crucial for building more reliable and trustworthy LLMs, with applications ranging from improved customer service chatbots to more accurate question-answering systems in specialized domains.

Papers

January 6, 2025

QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
Binita Saha, Utsha Saha, Muhammad Zubair Malik
Text Modality Retrieval Augmented Generation Large Corpus Question Answering Domain Specific Kg Rag Span Answer

December 27, 2024

Long Context vs. RAG for LLMs: An Evaluation and Revisits
Xinze Li, Yixin Cao, Yubo Ma, Aixin Sun
Medical LLM Global Evaluation Retrieval Augmented Generation Context Information Long Context Chunk Wise Context Dependent Question Kg Rag

December 23, 2024

RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer, Farzad Naderi, Viktor Hangya, Andreas Foltyn, Luzian Hahn, Fabian Kuech
Knowledge Graph SPARQL Query Conversational Question RDF Schema Kg Rag ConvQA Face Challenge RDF Knowledge Graph Iterative Retrieval

December 22, 2024

The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States
Fabian Ridder, Malte Schilling
Large Language Model Financial Application Stronger Generalizability Internal State Model Hallucination Type II Hallucination Kg Rag

December 3, 2024

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation
Sepand Dyanatkar, Angran Li, Alexander Dungate
Vision Language Model Computer Vision Visual Concept Kg Rag Scalable Model Marine Environment Environmental Conservation Ocean Data

November 29, 2024

Generating a Low-code Complete Workflow via Task Decomposition and RAG
Orlando Marquez Ayala, Patrice Béchard
genAI System Design Pattern Task Decomposition Workflow Composition Kg Rag GenAI Model

November 26, 2024

Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering
Awais Naeem, Tianhao Li, Huang-Ru Liao, Jiawei Xu, Aby M. Mathew, Zehao Zhu, Zhen Tan, Ajay Kumar Jaiswal, Raffi A. Salibian, Ziniu Hu, Tianlong Chen, Ying Ding
Visual Question Answering Pathology Image Kg Rag VQA Dataset Medical VQA Pathology Specific

November 23, 2024

Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG
Peng Xu, Hongjin Wu, Jinle Wang, Rongjia Lin, Liwei Tan
Style PROMPT Clinical Note High Level Kg Rag Ranking Method Traditional Chinese Medicine

November 20, 2024

DMQR-RAG: Diverse Multi-Query Rewriting for RAG
Zhicong Li, Jiahao Wang, Zhishu Jiang, Hangyu Mao, Zhongxia Chen, Jiazhen Du, Yuanxing Zhang, Fuzheng Zhang, Di Zhang, Yong Liu
Large Language Model Information Retrieval Kg Rag

November 7, 2024

Enhancing classroom teaching with LLMs and RAG
Elizabeth A Mullins, Adrian Portillo, Kristalys Ruiz-Rohena, Aritran Piplai
Medical LLM Answer Correctness Kg Rag Educational Resource Expert Driven Cybersecurity Instruction

October 21, 2024

Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
Ayman Asad Khan, Md Toufique Hasan, Kai Kristian Kemell, Jussi Rasku, Pekka Abrahamsson
Retrieval Augmented Generation Information Retrieval Response Generation eXperience Report Generative AI System LLM Integrated Application PDF Document Kg Rag Augmented Generation

October 15, 2024

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
Jinyoung Kim, Dayoon Ko, Gunhee Kim
Knowledge Base Continual Adaptation Kg Rag Entity Typing Textual Mention

October 3, 2024

A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
Shailja Gupta, Rajesh Ranjan, Surya Narayan Singh
Natural Language Processing Retrieval Augmented Generation Comprehensive Survey Generative Language Model Future Direction Retrieval Augmented Language Model Current State Kg Rag

September 30, 2024

Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG
Chenhao Fang, Derek Larson, Shitong Zhu, Sophie Zeng, Wendy Summer, Yanqing Peng, Yuriy Hulovatyy, Rajeev Rao, Gabriel Forgues, Arya Pudota, Alex Goncalves, Hervé Robert
Continual Pre Training Kg Rag Reducing Hallucination

September 5, 2024

RAG based Question-Answering for Contextual Response Prediction System
Sriram Veturi, Saurabh Vaichal, Nafis Irtiza Tripto, Reshma Lal Jagadheesh, Nian Yan
Retrieval Augmented Generation Context Aware Question Answering System Kg Rag Question Answering Framework

August 30, 2024

MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models
Yujing Wang, Hainan Zhang, Liang Pang, Binghui Guo, Hongwei Zheng, Zhiming Zheng
Large Language Model Query Information Response Generation Kg Rag Multi Point Feedback

August 12, 2024

A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning
Chih-Wei Song, Yu-Kai Lee, Yin-Te Tsai
Large Language Model Language Model Fine Tuning Domain Specific Pipeline System Instruction Dataset Domain Corpus Kg Rag

August 6, 2024

FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder
Federated Learning Social Medium LLM Based Social Network Federated Prompt Cooperation Kg Rag Query Processing

July 30, 2024

From Feature Importance to Natural Language Explanations Using LLMs with RAG
Sule Tekkesinoglu, Lars Kunze
Large Language Model Question Answering Natural Language Explanation Feature Importance Semantic Feature Kg Rag Natural Language Expression Human Explanation

July 10, 2024

Examining Long-Context Large Language Models for Environmental Review Document Comprehension
Hung Phan, Anurag Acharya, Rounak Meyur, Sarthak Chaturvedi, Shivam Sharma, Mike Parker, Dan Nally, Ali Jannesari, Karl Pazdernik, Mahantesh Halappanavar, Sai Munikoti, Sameera Horawalavithana
Language Model Long Context Long Context Large Language Model Question Answering System Policy Adaptation Kg Rag

Kg Rag

Papers

QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance

Long Context vs. RAG for LLMs: An Evaluation and Revisits

RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG

The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation

Generating a Low-code Complete Workflow via Task Decomposition and RAG

Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering

Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG

DMQR-RAG: Diverse Multi-Query Rewriting for RAG

Enhancing classroom teaching with LLMs and RAG

Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions

Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG

RAG based Question-Answering for Contextual Response Prediction System

MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models

A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning

FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG

From Feature Importance to Natural Language Explanations Using LLMs with RAG

Examining Long-Context Large Language Models for Environmental Review Document Comprehension