Mockingbird is a RAG-Specific LLM that Beats GPT 4, Gemini 1.5 Pro in RAG Output Quality

Mockingbird is a newly launched fine-tuned language model by Vectara, specifically designed for retrieval-augmented generation (RAG) with a strong emphasis on data security and response quality. It outperforms major models such as OpenAI's GPT-4 and Google's Gemini 1.5 Pro in RAG output quality, citation accuracy, multilingual performance, and structured output accuracy. The model is integrated into Vectara’s secure infrastructure, ensuring that sensitive data remains private and is never used for model training, addressing enterprise concerns about data security associated with third-party AI providers. Mockingbird provides reliable and grounded answers with citations, enhancing its trustworthiness and utility in enterprise applications. It can be deployed on customers' Virtual Private Clouds or on-premise, offering flexibility and control over data. The model achieves superior performance metrics, including a high BERT F1 score, and is available for Vectara customers to switch their summarizer to Mockingbird through the API or console.