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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 701–750 of 2111 papers

TitleStatusHype
Knowing When to Ask -- Bridging Large Language Models and Data—0
Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces—0
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method—0
A Proposed Large Language Model-Based Smart Search for Archive System—0
Flooding edge or node weighted graphs—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks—0
Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course—0
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models—0
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model—0
Evaluating the Effect of Retrieval Augmentation on Social Biases—0
Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets—0
Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
Evaluation of RAG Metrics for Question Answering in the Telecom Domain—0
Can Language Models Enable In-Context Database?—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—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
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation—0
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
EvoWiki: Evaluating LLMs on Evolving Knowledge—0
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—0
A RAG Approach for Generating Competency Questions in Ontology Engineering—0
Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software—0
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models—0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
Exploring Advanced Large Language Models with LLMsuite—0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation—0
Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment—0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
AppAgent v2: Advanced Agent for Flexible Mobile Interactions—0
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization—0
Flippi: End To End GenAI Assistant for E-Commerce—0
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