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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 801–825 of 2111 papers

TitleStatusHype
Al-Khwarizmi: Discovering Physical Laws with Foundation Models—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models—0
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models—0
GFM-RAG: Graph Foundation Model for Retrieval Augmented GenerationCode3
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
LLM-based event log analysis techniques: A survey—0
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented GenerationCode1
Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning—0
RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval DefectsCode1
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method—0
Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach—0
Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains—0
GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback—0
Implementation of a Generative AI Assistant in K-12 Education: The CyberScholar Initiative—0
Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers—0
Balancing Content Size in RAG-Text2SQL System—0
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language ModelCode2
ASTRAL: Automated Safety Testing of Large Language Models—0
Multiple Abstraction Level Retrieve Augment Generation—0
Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models—0
Characterizing Network Structure of Anti-Trans Actors on TikTok—0
Provence: efficient and robust context pruning for retrieval-augmented generation—0
LemmaHead: RAG Assisted Proof Generation Using Large Language Models—0
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT—0
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