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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 951–1000 of 2111 papers

TitleStatusHype
LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation—0
Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark—0
Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS—0
Talking to GDELT Through Knowledge Graphs—0
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving—0
Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation—0
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach—0
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
Quantifying the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding DataCode0
TPU-Gen: LLM-Driven Custom Tensor Processing Unit Generator—0
Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational HistoryCode0
ORANSight-2.0: Foundational LLMs for O-RAN—0
Personalized Text Generation with Contrastive Activation Steering—0
Automatic Teaching Platform on Vision Language Retrieval Augmented Generation—0
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
In-depth Analysis of Graph-based RAG in a Unified Framework—0
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning—0
LeRAAT: LLM-Enabled Real-Time Aviation Advisory ToolCode0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
PennyLang: Pioneering LLM-Based Quantum Code Generation with a Novel PennyLane-Centric Dataset—0
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Query Processing—0
Wikipedia in the Era of LLMs: Evolution and RisksCode0
Optimizing open-domain question answering with graph-based retrieval augmented generation—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
SAGE: A Framework of Precise Retrieval for RAG—0
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation—0
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph—0
Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks—0
Optimizing Multi-Hop Document Retrieval Through Intermediate Representations—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-HaystackCode0
Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration—0
TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval—0
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation—0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
RuCCoD: Towards Automated ICD Coding in RussianCode0
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research—0
LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty—0
Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice—0
Trustworthy Answers, Messier Data: Bridging the Gap in Low-Resource Retrieval-Augmented Generation for Domain Expert Systems—0
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering—0
Leveraging Retrieval-Augmented Generation and Large Language Models to Predict SERCA-Binding Protein Fragments from Cardiac Proteomics Data—0
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