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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 1751–1775 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
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