GLMDST: Chinese Dialogue State Tracking Framework Driven by LLM
Zixi Jia, Bingze Li · 2024
Dialogue State Tracking (DST) is an important component in task-oriented dialogue systems. Accurate dialogue state tracking is essential for generating correct dialogue actions and appropriate natural language responses. However, annotating conversation states is notoriously difficult, leading to a scarcity of DST datasets in languages other than English. With the development of DST, high-quality annotated Chinese conversation datasets have gradually emerged. Despite the emergence of available Chinese conversation datasets, research on the Chinese DST task remains relatively limited.The emergence of Large Language Model (LLM) such as GPT-3 and ChatGPT has sparked considerable interest in evaluating their effectiveness across different tasks. However, the performance of LLM in Chinese DST remains to be explored. In this study, we introduce GLMDST, a Chinese DST framework powered by a smaller open-source bilingual model called chat-glm3-6b. GLMDST demonstrates outstanding performance in experiments on mainstream Chinese DST benchmarks such as CrossWOZ and RiSAWOZ, showcasing excellent adaptability and generalization capabilities.