Lipschitz smooth, and further improved by using fewer iterations.

However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels that may mislead the alignment.

Polyphone disambiguation aims to capture accurate pronunciation knowledge fromnaturaltextsequences forreliable Text-to-speech (TTS)systems.

However, it remains a critical challenge to enable unpaired I2I translation with pre-trained DMs while maintaining satisfying consistency.

Wherein, the first step, i.e. gradient estimation, is critical since it provides the essential direction to update variables, which have been explored by many recent works[5,30].