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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 1476–1500 of 2111 papers

TitleStatusHype
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models—0
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks—0
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings—0
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques—0
Integrating Temporal Representations for Dynamic Memory Retrieval and Management in Large Language Models—0
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models—0
How Does Knowledge Selection Help Retrieval Augmented Generation?—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented GenerationCode0
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
Is Semantic Chunking Worth the Computational Cost?—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
Retrieval Augmented Spelling Correction for E-Commerce Applications—0
Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters—0
De-jargonizing Science for Journalists with GPT-4: A Pilot StudyCode0
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented GenerationCode0
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data—0
Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models—0
DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAGCode0
Self-adaptive Multimodal Retrieval-Augmented GenerationCode0
On the Capacity of Citation Generation by Large Language Models—0
Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specifications—0
AIC CTU system at AVeriTeC: Re-framing automated fact-checking as a simple RAG taskCode0
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