"RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering"

Adding rule-based guidance doubles RAG's performance in document retrieval and answer generation.

Basically, RAG gets a proper manual on how to use its knowledge.

It's like giving RAG a GPS instead of letting it wander around blindly.

📚 https://arxiv.org/abs/2410.22353

🎯 Original Problem:

Current Retrieval-Augmented Generation (RAG) frameworks face two major limitations: retrievers can't guarantee fetching the most relevant information, and LLMs lack specific guidance on using retrieved content effectively.

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🔧 Solution in this Paper:

→ Introduces RuleRAG, which uses symbolic rules to guide both retrieval and generation processes.

→ Guide retrievers to fetch logically related documents following rule directions

→ Help generators uniformly generate answers attributed by the same set of rules

→ Use queries and rules combined as supervised fine-tuning data to improve rule-based instruction following

→ RuleRAG-ICL: Uses in-context learning with rule guidance during retrieval and inference

→ RuleRAG-FT: Fine-tunes both retrievers and generators using rule-guided fine-tuning

→ Created five rule-aware QA benchmarks (three temporal, two static) to evaluate performance

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💡 Key Insights:

→ Rules can explicitly guide both document retrieval and answer generation

→ Combining rules with queries improves retrieval quality significantly

→ Rule-guided fine-tuning enhances both retrieval and generation performance

→ The method scales well with increasing numbers of retrieved documents

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📊 Results:

→ RuleRAG-ICL improved retrieval quality by +89.2% in Recall@10 scores

→ Generation accuracy increased by +103.1% in exact match scores

→ RuleRAG-FT achieved even better performance improvements across all benchmarks

→ Method showed strong generalization ability for untrained rules