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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 1051–1075 of 2111 papers

TitleStatusHype
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?—0
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation—0
RAPID: Retrieval Augmented Training of Differentially Private Diffusion ModelsCode0
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
RAG vs. GraphRAG: A Systematic Evaluation and Key Insights—0
Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User ControlCode0
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark—0
Revisiting Robust RAG: Do We Still Need Complex Robust Training in the Era of Powerful LLMs?—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models—0
SmartLLM: Smart Contract Auditing using Custom Generative AI—0
Does RAG Really Perform Bad For Long-Context Processing?—0
QuOTE: Question-Oriented Text Embeddings—0
TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules—0
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information—0
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs—0
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based TasksCode0
Investigating Language Preference of Multilingual RAG Systems—0
Gumbel Reranking: Differentiable End-to-End Reranker Optimization—0
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization—0
Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation—0
MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation—0
IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System—0
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