REVIEW 8 cited by
Searching for Best Practices in 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
read the original abstract
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times. Typically, a RAG workflow involves multiple processing steps, each of which can be executed in various ways. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. Through extensive experiments, we suggest several strategies for deploying RAG that balance both performance and efficiency. Moreover, we demonstrate that multimodal retrieval techniques can significantly enhance question-answering capabilities about visual inputs and accelerate the generation of multimodal content using a "retrieval as generation" strategy.
Forward citations
Cited by 8 Pith papers
-
ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
ERank trains a pointwise reasoning-LLM reranker with fine-grained integer scoring and a listwise reinforcement-learning reward, reporting nDCG@10 of 40.2 on BRIGHT for its 32B variant with BM25 hybrid scoring.
-
An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
EASI-RAG is a structured agile method for deploying RAG tools in industrial SMEs, validated by one case study where a no-experience team built a working assistant in three weeks.
-
MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks
AVHaystacks is a new 3100-question benchmark for audio-visual QA across 500 videos, and the MAGNET multi-agent pipeline beats current baselines on it.
-
Climate Finance Bench
Climate Finance Bench releases 330 expert-validated QA pairs on 33 climate reports and shows that retrieval quality, not model capacity, is the main accuracy bottleneck.
-
GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Framework
GOSU globally merges semantic units from text chunks into a unit-centric knowledge graph and uses three-tier keyword retrieval to improve RAG generation quality, according to LLM-judge win rates.
-
Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
A new RL framework that rewards fine-grained reasoning steps, called Atomic Thoughts, claims better agentic deep research on seven benchmarks.
-
MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems
The authors claim their MultiFluxAI orchestration framework achieves 95% accuracy and 0-10 ms responses by combining rule-based routing, caching, and graph knowledge stores for multi-service RAG queries.
-
RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
RAGOps frames RAG operations as the intertwined management of a query processing pipeline and a data lifecycle, with design considerations, challenges, and two anecdotal use cases.
Discussion (0). Sign in to comment.