Baidu has developed a general approach called Retrieval-Augmented Generation (RAG) that is gaining interest in the artificial intelligence (AI) community. While many AI advancements focus on benchmark tests and multi-modal capabilities, RAG offers a balanced approach that emphasizes producing valid answers quickly, especially for enterprises using large language models (LLMs).
RAG involves having an LLM send a request to an external data source, such as a vector database, to retrieve authoritative data in response to a prompt. This approach helps reduce the likelihood of LLMs producing false information confidently, known as “hallucinations.” Commercial software vendors are rushing to provide programs that allow companies to connect to databases and retrieve accurate answers based on various data sources.
As RAG becomes more prevalent in AI models, it is clear that it can enhance the accuracy of LLMs but also introduce new challenges. Some LLMs may struggle with handling the information retrieved via RAG, leading to inaccuracies or “hallucinations.” Researchers are exploring ways to improve LLMs’ performance with RAG by studying failure cases and proposing new training methods, such as WeChat’s INFO-RAG, which aims to make LLMs more RAG-aware.
While RAG shows promise in improving LLM performance, there are also alternatives like fine-tuning that have their own set of challenges. Fine-tuning can enhance an AI model’s capabilities by retraining it with a focused dataset, but issues like the “perplexity curse” can hinder its effectiveness. As AI continues to evolve, researchers are looking for ways to optimize the interaction between RAG and LLMs to enhance overall performance and accuracy.