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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 1951–2000 of 2111 papers

TitleStatusHype
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented GenerationCode2
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
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question AnsweringCode1
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation SystemsCode1
Evaluating Very Long-Term Conversational Memory of LLM AgentsCode1
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
From RAGs to riches: Using large language models to write documents for clinical trials—0
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)Code2
Fine-tuning Large Language Models for Domain-specific Machine Translation—0
Assessing generalization capability of text ranking models in Polish—0
2D Matryoshka Sentence EmbeddingsCode4
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
FinBen: A Holistic Financial Benchmark for Large Language ModelsCode4
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Benchmarking Retrieval-Augmented Generation for MedicineCode4
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
What Evidence Do Language Models Find Convincing?Code1
Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge—0
EVOR: Evolving Retrieval for Code GenerationCode2
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
Dense Passage Retrieval: Is it Retrieving?—0
PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering—0
In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs MissCode4
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language ModelCode2
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
Grounding Language Model with Chunking-Free In-Context Retrieval—0
Generative Representational Instruction TuningCode4
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question AnsweringCode4
T-RAG: Lessons from the LLM Trenches—0
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language ModelsCode3
A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and AdaptationCode1
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity KnowledgeCode2
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
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton—0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
Financial Report Chunking for Effective Retrieval Augmented GenerationCode0
Multi-Lingual Malaysian Embedding: Leveraging Large Language Models for Semantic Representations—0
C-RAG: Certified Generation Risks for Retrieval-Augmented Language ModelsCode1
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation—0
How well do LLMs cite relevant medical references? An evaluation framework and analysesCode1
LitLLM: A Toolkit for Scientific Literature ReviewCode2
Retrieval Augmented End-to-End Spoken Dialog Models—0
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks—0
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA—0
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