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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 901–950 of 2111 papers

TitleStatusHype
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs—0
Graph RAG for Legal Norms: A Hierarchical and Temporal Approach—0
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)—0
Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models—0
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain—0
Graphy'our Data: Towards End-to-End Modeling, Exploring and Generating Report from Raw Data—0
Context Tuning for Retrieval Augmented Generation—0
ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model—0
Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications—0
GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights—0
Grounded in Context: Retrieval-Based Method for Hallucination Detection—0
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation—0
Grounding Language Model with Chunking-Free In-Context Retrieval—0
BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, including case law—0
GTR: Graph-Table-RAG for Cross-Table Question Answering—0
An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education—0
Gumbel Reranking: Differentiable End-to-End Reranker Optimization—0
Habit Coach: Customising RAG-based chatbots to support behavior change—0
Hacking, The Lazy Way: LLM Augmented Pentesting—0
Enhancing Retrieval Processes for Language Generation with Augmented Queries—0
Hallucination Detection in LLMs via Topological Divergence on Attention Graphs—0
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools—0
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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