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TRAC: Trustworthy Retrieval Augmented Chatbot
Shuo Li · Sangdon Park · Insup Lee · Osbert Bastani

Although neural conversational AIs have demonstrated fantastic performance, they often generate incorrect information, or \textit{hallucinations}. Retrieval augmented generation has emerged as a promising solution to reduce these hallucinations. However, these techniques still cannot guarantee correctness. Focusing on question answering, we propose a framework that can provide statistical guarantees for the retrieval augmented question answering system by combining conformal prediction and global testing. In addition, we use Bayesian optimization to choose hyperparameters of the global test to maximize the performance of the system. Our empirical results on the Natural Questions dataset demonstrate that our method can provide the desired coverage guarantee while minimizing the average prediction set size.

Author Information

Shuo Li (University of Pennsylvania)
Sangdon Park (Georgia Institute of Technology)
Insup Lee (University of Pennsylvania)
Osbert Bastani (University of Pennsylvania)

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