GEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking
Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models (LLMs) struggle despite their impressive general capabilities. We present GEM (Graph-Enhanced Mixture-of-Experts), a novel framework that combines language models and graph-structured dialogue understanding with ReAct agent-based reasoning for superior DST performance. Our approach dynamically routes between specialized experts: a Graph Neural Network that captures dialogue structure and turn-level dependencies, and a finetuned T5-Small encoder-decoder for sequence modeling, coordinated by an intelligent router. For complex value generatio...
| Authors | Ziqi Zhu, Adithya Suresh, Tomal Deb et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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