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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 951–975 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
Personalized Text Generation with Contrastive Activation Steering—0
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation—0
TPU-Gen: LLM-Driven Custom Tensor Processing Unit Generator—0
ORANSight-2.0: Foundational LLMs for O-RAN—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach—0
Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational HistoryCode0
Quantifying the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding DataCode0
Automatic Teaching Platform on Vision Language Retrieval Augmented Generation—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning—0
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster—0
In-depth Analysis of Graph-based RAG in a Unified Framework—0
LeRAAT: LLM-Enabled Real-Time Aviation Advisory ToolCode0
Optimizing open-domain question answering with graph-based retrieval augmented generation—0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
Wikipedia in the Era of LLMs: Evolution and RisksCode0
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Query Processing—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
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