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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 2001–2025 of 2111 papers

TitleStatusHype
Fine Tuning vs. Retrieval Augmented Generation for Less Popular KnowledgeCode0
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots—0
DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases—0
Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines—0
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification—0
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering CapabilityCode0
From RAGs to riches: Using large language models to write documents for clinical trials—0
Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent SetupCode0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
Fine-tuning Large Language Models for Domain-specific Machine Translation—0
Assessing generalization capability of text ranking models in Polish—0
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning—0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering—0
Dense Passage Retrieval: Is it Retrieving?—0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
Grounding Language Model with Chunking-Free In-Context Retrieval—0
T-RAG: Lessons from the LLM Trenches—0
Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models—0
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language ModelsCode0
Enhancing Retrieval Processes for Language Generation with Augmented Queries—0
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