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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 926–950 of 2111 papers

TitleStatusHype
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
HalluMix: A Task-Agnostic, Multi-Domain Benchmark for Real-World Hallucination Detection—0
A Survey on Retrieval-Augmented Text Generation for Large Language Models—0
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning—0
Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction—0
Enhancing Retrieval Performance: An Ensemble Approach For Hard Negative Mining—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation—0
HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks—0
How Does Knowledge Selection Help Retrieval Augmented Generation?—0
HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables—0
BRIT: Bidirectional Retrieval over Unified Image-Text Graph—0
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System—0
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings—0
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS—0
Annotating Speech, Attitude and Perception Reports—0
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification—0
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor—0
Bridging the Preference Gap between Retrievers and LLMs—0
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