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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 1251–1300 of 2111 papers

TitleStatusHype
Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph—0
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models—0
Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases—0
Tools in the Loop: Quantifying Uncertainty of LLM Question Answering Systems That Use Tools—0
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models—0
Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task—0
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation—0
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis—0
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight—0
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks—0
Towards Automated Safety Requirements Derivation Using Agent-based RAG—0
Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models—0
Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data—0
Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach—0
Towards Conversational AI for Human-Machine Collaborative MLOps—0
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning—0
Towardseffective teaching assistants: From intent-based chatbots to LLM-poweredteachingassistants—0
Towards Efficient Key-Value Cache Management for Prefix Prefilling in LLM Inference—0
Towards Evaluating Large Language Models for Graph Query Generation—0
Towards Explainable Network Intrusion Detection using Large Language Models—0
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering—0
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective—0
Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval—0
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications—0
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data—0
Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models—0
Towards Requirements Engineering for RAG Systems—0
Towards Retrieval Augmented Generation over Large Video Libraries—0
Towards Robustness of Text-to-Visualization Translation against Lexical and Phrasal Variability—0
Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems—0
Towards Unlocking Insights from Logbooks Using AI—0
TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules—0
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning—0
TPU-Gen: LLM-Driven Custom Tensor Processing Unit Generator—0
Traceback of Poisoning Attacks to Retrieval-Augmented Generation—0
Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG—0
T-RAG: Lessons from the LLM Trenches—0
Training Plug-n-Play Knowledge Modules with Deep Context Distillation—0
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data—0
Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack—0
Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders—0
Trustworthy Answers, Messier Data: Bridging the Gap in Low-Resource Retrieval-Augmented Generation for Domain Expert Systems—0
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster—0
TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization—0
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation—0
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
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