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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 1751–1800 of 2111 papers

TitleStatusHype
Better RAG using Relevant Information GainCode0
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Knowledge and Aptitude Augmented Generation: Adaptive Multi-Turn Interaction in LLM SystemsCode0
Evaluation of RAG Metrics for Question Answering in the Telecom Domain—0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Integrating AI Tutors in a Programming Course—0
GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?—0
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond—0
Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation—0
Context Embeddings for Efficient Answer Generation in RAG—0
Lynx: An Open Source Hallucination Evaluation Model—0
Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024—0
Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting—0
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
Examining Long-Context Large Language Models for Environmental Review Document Comprehension—0
FACTS About Building Retrieval Augmented Generation-based Chatbots—0
Attribute or Abstain: Large Language Models as Long Document AssistantsCode0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models—0
Are LLMs Correctly Integrated into Software Systems?—0
RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations—0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning—0
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation—0
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models—0
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questionsCode0
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs—0
Exploring Advanced Large Language Models with LLMsuite—0
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest—0
Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach—0
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation—0
Memory^3: Language Modeling with Explicit Memory—0
Large Language Models Struggle in Token-Level Clinical Named Entity RecognitionCode0
From RAG to RICHES: Retrieval Interlaced with Sequence Generation—0
Answering real-world clinical questions using large language model based systems—0
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical ScienceCode0
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs—0
LLM4DESIGN: An Automated Multi-Modal System for Architectural and Environmental Design—0
Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM KnowledgeCode0
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation—0
Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders—0
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
Poisoned LangChain: Jailbreak LLMs by LangChain—0
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