ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent

Understand

Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance.

• However, their application to spoken language understanding (SLU) remains challenging, particularly for token-level tasks, where the autoregressive nature of LLMs often leads to misalignment issues.
• They also struggle to capture nuanced interrelations in semantic-level tasks through direct fine-tuning alone.
• To address these challenges, we propose the Entity-level Language Model (ECLM) framework, which reformulates slot-filling as an entity recognition task and introduces a novel concept, \textit{Chain of Intent}, to enable step-by-step multi-intent recognition.