To Memorize or to Retrieve: Scaling Laws for RAG-Considerate Pretraining

1. 1Identify the pretraining corpus size and retrieval budget constraints
2. 2Analyze the trade-off between parametric knowledge acquired during pretraining and non-parametric knowledge accessed via retrieval
3. 3Develop strategies to optimize the balance between pretraining and retrieval for improved language model performance
4. 4Evaluate the effectiveness of RAG-considerate pretraining in knowledge-intensive situations

ML researchers and engineers working on language models can benefit from understanding the scaling laws for RAG-considerate pretraining to improve model performance, especially in knowledge-intensive situations

💡 The relationship between parametric and non-parametric knowledge in RAG-considerate pretraining is crucial for improving language model performance

💡 New research on RAG-considerate pretraining reveals trade-offs between pretraining corpus size & retrieval budget #LLMs #RAG