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The application of artificial intelligence (AI) in enterprises is becoming increasingly widespread, but the inherent risk of 'hallucination'—generating information that is not true or unsupported—has always been a key challenge hindering large-scale deployment. Although various techniques and methods aimed at reducing hallucinations have emerged in the industry, such as retrieval-augmented generation (RAG), improving data quality, guardrails, and reasoning verification, the results are often limited. Recently, a company called Vectara has launched a new solution: the 'Vectara Hallucination Corrector,' designed to correct errors through a guardian agent.

OpenAI GPT-4 stands out on the Vectara leaderboard with only a 3% hallucination rate in document summarization and an accuracy rate of 97%. The Vectara evaluation reveals that GPT-4 Turbo and GPT-4 perform the best, followed by GPT-3.5 Turbo, while the highest-rated non-OpenAI model is Meta Llama. Vectara has released open-source models that can check the hallucination rate of language models to enhance the credibility of generative AI systems. Google's models performed moderately on the leaderboard.

AI startup Vectara has launched a new technology called Boomerang, which enhances AI accuracy through vector embeddings. Boomerang is a neural information retrieval model that generates a new method for vector embeddings that allows cross-lingual retrieval. Vectara aims to reduce the risk of AI hallucinations and provide more accurate and interpretable results. The complexity of Boomerang's vector embedding approach will be elaborated in future research papers.