Unlocking Hidden Capabilities: A Self-Improving Workflow for Chatbots to Utilize Unintegrated Services

Can Wang, Hao Ding, Yongchao Xing, Bohai Zhao, Dianbo Sui, Zhiying Tu

Abstract: Chatbots have advanced from basic conversational agents to versatile tools by integrating external services. However, traditional chatbots are constrained by predefined service boundaries, limiting their ability to handle complex tasks with unintegrated services. While most research focuses on improving service discovery and invocation through data-intensive pretraining, only 13.29% of services are well-documented, hindering practical deployment. This paper proposes a self-improving workflow for chatbots, using a “wide in, strict out” self-supervised learning approach to acquire domain knowledge efficiently and generate high-quality service documents. Compatible with existing methods, it eliminates the need for dataset collection or pre-training. Experiments demonstrate that our workflow significantly improves the pass and success rate of chatbots in utilizing unintegrated services, offering a powerful solution for real-world applications where service integration is limited.

External IDs:dblp:conf/icws/WangDXZST25