Hayato Futami

This method integrates the LMs directly into the parameters of an LLM-based ASR model, requiring no additional computational cost at inference, and consistently improved the ASR performance in the target domains, without degrading inference speed or memory footprint.

Forget optimizing for just one thing: multi-reward RLAIF dramatically improves both semantic quality and audio naturalness in spoken dialogue systems, where single-reward methods fall flat.