Enhancing Language Models with RAG: Best Practices and Benchmarks
What are the main challenges of RAG techniques?
The main challenges of RAG techniques include integrating up-to-date information, reducing hallucinations, improving response quality, complex implementations, and prolonged response times. These challenges hinder the effectiveness of RAG approaches, making it crucial to optimize them for enhancing large language model (LLM) performance and enabling real-time applications in specialized domains such as medical diagnosis.
How do current methods address RAG challenges?
Current methods addressing RAG challenges include query classification, retrieval using techniques like BM25 and Contriever, reranking, repacking, and summarization. However, these methods face limitations such as computational intensity and slow performance, making them unsuitable for real-time applications.
What are the limitations of query rewriting and decomposition?
Query rewriting and decomposition have limitations such as being computationally intensive, requiring deep knowledge of SQL and query analysis, and involving the rewriting of complex and long queries. These techniques can transform inefficient queries into more optimized forms but may not be suitable for all scenarios and can introduce complexity to query processing.