RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
RAG+ is a novel framework that extends Retrieval-Augmented Generation (RAG) by explicitly incorporating application-aware reasoning. It constructs a dual corpus of factual knowledge and aligned application examples, which are jointly retrieved during inference. This approach enables large language models (LLMs) to not only access relevant information but also apply it within structured, goal-oriented reasoning processes, leading to significant performance improvements across various knowledge-intensive domains. ✨
Article Points:
1
RAG+ addresses RAG's limitation of lacking an application-aware reasoning step.
2
It jointly retrieves factual knowledge and aligned usage examples for better reasoning.
3
RAG+ builds a dual corpus: domain knowledge and application instances.
4
The framework is modular and retrieval-agnostic, integrating into existing RAG pipelines.
5
Consistently outperforms standard RAG variants across math, legal, and medical domains.
6
Bridges passive knowledge access with task-oriented reasoning for capable LLMs.
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
Core Idea
Enhances RAG with application-aware reasoning
Bridges retrieval with actionable application
Methodology
Dual Corpus
- Knowledge
- Application Examples
Construction Stage
- Application Generation
- Application Matching
Inference Stage
- Retrieves knowledge & applications
- Forms comprehensive prompt
Modular & Retrieval-Agnostic
Benefits
Improved Reasoning Accuracy
Consistent Performance Gains
Scales well with model size
Evaluation
Math Domain
Legal Domain
Medical Domain
Multiple LLMs
Limitations
Resource-intensive corpus construction
Potential for alignment mismatches
Doesn't optimize retrieval quality
Future Work