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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 1601–1650 of 2111 papers

TitleStatusHype
Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented GenerationCode2
Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG SystemsCode1
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Integrating AI Tutors in a Programming Course—0
GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?—0
Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation—0
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond—0
Context Embeddings for Efficient Answer Generation in RAG—0
PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric AgentsCode2
Human-like Episodic Memory for Infinite Context LLMsCode3
Lynx: An Open Source Hallucination Evaluation Model—0
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
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
Examining Long-Context Large Language Models for Environmental Review Document Comprehension—0
Attribute or Abstain: Large Language Models as Long Document AssistantsCode0
FACTS About Building Retrieval Augmented Generation-based Chatbots—0
PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning MethodsCode7
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
ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access NetworksCode1
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models—0
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language ModelsCode2
How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical QuestionsCode2
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
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models—0
TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language ModelsCode2
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI ApplicationsCode1
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs—0
MeMemo: On-device Retrieval Augmentation for Private and Personalized Text GenerationCode2
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questionsCode0
Exploring Advanced Large Language Models with LLMsuite—0
BERGEN: A Benchmarking Library for Retrieval-Augmented GenerationCode3
Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach—0
Searching for Best Practices in Retrieval-Augmented GenerationCode3
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
Summary of a Haystack: A Challenge to Long-Context LLMs and RAG SystemsCode2
Memory^3: Language Modeling with Explicit Memory—0
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation—0
Retrieval-augmented generation in multilingual settingsCode3
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest—0
Large Language Models Struggle in Token-Level Clinical Named Entity RecognitionCode0
From RAG to RICHES: Retrieval Interlaced with Sequence Generation—0
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