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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
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
How Does Knowledge Selection Help Retrieval Augmented Generation?—0
AgentOps: Enabling Observability of LLM Agents—0
Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models—0
AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs—0
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation—0
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution—0
Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation—0
Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation—0
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning—0
Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA—0
Augmenting Textual Generation via Topology Aware Retrieval—0
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models—0
Automated Code Generation and Validation for Software Components of Microcontrollers—0
Automated Conversion of Static to Dynamic Scheduler via Natural Language—0
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search—0
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks—0
Automatic Teaching Platform on Vision Language Retrieval Augmented Generation—0
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
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