Long Context and RAG aren't competitors - they're complementary tools for different content types.
Think of Long Context as a book reader, RAG as a fact-checker - both useful, just different.
This paper comprehensively evaluates Long Context and RAG approaches for handling long contexts in LLMs, highlighting their complementary strengths and providing recommendations for their effective use.
https://arxiv.org/abs/2501.01880
Original Problem 🤔:
→ There's no clear consensus on which approach works better for different types of questions and knowledge sources.
Solution in this Paper 🔧:
→ The researchers filtered questions that require external context, ensuring fair comparison.
→ They evaluated multiple retrieval methods including BM25, Contriever, and RAPTOR on 12 diverse QA datasets.
→ They expanded datasets to 19,188 questions for statistical significance.
→ They analyzed performance across different knowledge sources and question types.
Key Insights 💡:
→ Long Context excels with Wikipedia and story-based content
→ RAG performs better with dialogue and fragmented information
→ RAPTOR outperforms traditional chunk-based retrievers
→ Context relevance significantly impacts performance
Results 📊:
→ Long Context correctly answers 56.3% questions vs RAG's 49.0%
→ Long Context exclusively answers 2,000+ questions RAG misses
→ RAG shows 10% unique correct answers Long Context can't handle
→ RAPTOR achieves 38.5% accuracy, leading other retrievers
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