REVIEW 19 cited by
A Survey on Knowledge-Oriented Retrieval-Augmented Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
A Survey on Knowledge-Oriented Retrieval-Augmented Generation
read the original abstract
Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models. RAG leverages external knowledge sources, such as documents, databases, or structured data, to improve model performance and generate more accurate and contextually relevant outputs. This survey aims to provide a comprehensive overview of RAG by examining its fundamental components, including retrieval mechanisms, generation processes, and the integration between the two. We discuss the key characteristics of RAG, such as its ability to augment generative models with dynamic external knowledge, and the challenges associated with aligning retrieved information with generative objectives. We also present a taxonomy that categorizes RAG methods, ranging from basic retrieval-augmented approaches to more advanced models incorporating multimodal data and reasoning capabilities. Additionally, we review the evaluation benchmarks and datasets commonly used to assess RAG systems, along with a detailed exploration of its applications in fields such as question answering, summarization, and information retrieval. Finally, we highlight emerging research directions and opportunities for improving RAG systems, such as enhanced retrieval efficiency, model interpretability, and domain-specific adaptations. This paper concludes by outlining the prospects for RAG in addressing real-world challenges and its potential to drive further advancements in natural language processing.
Forward citations
Cited by 19 Pith papers
-
When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems
PlanRAG models exploratory reasoning problems as logical query trees, uses dynamic programming with a cost model to build them, and executes iterative retrieval-generation over the trees, outperforming prior RAG metho...
-
NormAct: A Benchmark for Hidden Social Norm Compliance in Embodied Planning
MLLM embodied planners hit explicit goals ~67% of the time but hidden social norms only ~26%; scene-grounded cues, not generic knowledge, close much of the gap.
-
Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings
Precomputing hypothetical question embeddings for each text chunk at indexing time shifts retrieval to question–question matching and improves context precision and claim recall in RAG.
-
MERIT: Efficient In-Place Deletion for Dynamic Graph-Based Approximate Nearest Neighbor Indexes
MERIT makes vector-graph deletions cheap by repairing only a bounded local neighborhood via k_r-MST and invalidating all leftover stale edges with per-target version stamps.
-
When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems
PlanRAG models natural language exploratory reasoning problems as logical query trees, optimizes them via dynamic programming with a multi-dimensional cost model, and executes iterative retrieval-generation over the t...
-
NormAct: A Benchmark for Hidden Social Norm Compliance in Embodied Planning
NormAct shows MLLMs reach explicit goals in 67.3% of cases but comply with hidden norms in only 26.4%, with NormPerceptor raising task success from 24.2% to 46.7%.
-
GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings
GRACE-RAG is a governed graph-augmented RAG architecture that moves structural reasoning to retrieval, reporting up to 20% quality gains on mid-scale models in closed-domain settings.
-
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
A retrieval approach identifies anomalous dimensions in a set of query vectors and retrieves database vectors that are anomalous across those dimensions, with performance improving as query set size grows to around 8.
-
HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling
A working memory represented as a hypergraph, whose hyperedges are updated, inserted, and progressively merged by the LLM, improves multi-step RAG on long-context sense-making benchmarks.
-
Supervising the search process produces reliable and generalizable information-seeking agents
Process supervision via RAG-Gym produces more reliable and generalizable search agents, with gains driven by higher-quality queries on out-of-domain multi-hop tasks.
-
Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
Hybrid RAG over UK public health guidance sharply raises MCQA accuracy and free-form faithfulness, letting smaller open models match larger closed models without retrieval.
-
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
Multi-query vector retrieval via anomalous pattern detection on shared dimensions improves retrieval as query-set size grows, especially from 1 to 8 queries.
-
Context-KG: Context-Aware Knowledge Graph Visualization with User Preferences and Ontological Guidance
Context-KG uses LLMs to extract user preferences and context from natural language, driving ontology-guided layouts and insights for knowledge graph visualization that improve interpretability and task performance ove...
-
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs
GroupRank uses groupwise LLM reranking with answer-free data synthesis and a group-ranking reward to reach 65.2 NDCG@10 on BRIGHT while providing 6.4x faster inference than listwise baselines.
-
Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering
Hybrid-IR combines graph and dense retrieval with iterative retrieve-reason loops and shows gains on three medical QA benchmarks.
-
Differentially Private Datastore Generation for Retrieval-Augmented Inference
Hashing-based framework adds DP noise to LSH bucket votes to release private probability distributions for datastores with 2.6% average accuracy loss at epsilon=5.
-
Multi-Dimensional Knowledge Profiling with Large-Scale Literature Database and Hierarchical Retrieval
Large-scale profiling of recent AI literature shows growth in safety, multimodal reasoning, and agent studies alongside stabilization in neural machine translation and graph methods.
-
Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
A modular, algorithm-agnostic framework for multi-turn agentic RL that masks policy updates to agent tokens and validates on multi-hop QA with five RL algorithms.
-
A Survey of Context Engineering for Large Language Models
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.