Team Introduction:
Dedicated to building an industry-leading large-model dialogue system, the team serves hundreds of millions of daily active users, with application scenarios covering the entire Douyin e-commerce ecosystem. This includes core business scenarios such as platform customer service, platform merchant service, merchant customer service, influencer customer service, and innovative intelligent shopping guides. Through continuous technological innovation and optimization, the team has successfully established a complete intelligent dialogue solution, delivering significant efficiency improvements and user experience enhancements to e-commerce operations.

Research Objectives:
Develop an LLM-based customer service chatbot for TikTok and Douyin E-commerce, enabling intelligent customer service interactions. The LLM will handle the entire user inquiry process, including request clarification, solution negotiation, and execution.
Necessity:
LLM's strong conversational and reasoning abilities make it especially suitable for intelligent customer service, capable of potentially reaching the service standards of excellent human representatives.
Research Content:
Design a multi-agent framework based on LLM, integrating planning-agent, reply-agent, and tool-agent. Each agent will specialize in different functions, working collaboratively to manage the complete service process—from issue identification and solution negotiation to solution implementation and feedback.
1) Reply-agent ensures the proposed solutions comply with platform policies and service guidelines, avoids excessive improvisation or hallucinations, and maintains smooth communication and negotiation with the user.
2) Planning-agent identifies user demands and problem scenarios, sourcing relevant service guidelines and constraints as well as recognizing risk scenarios.
3) Tool-agent validates the legality of tool usage, accurately interprets the results from tool interactions, and manages execution dependencies of various actions.
Research Challenges:
Compliance with service guidelines: Ensuring the chatbot's solutions adhere to platform service guidelines (such as available refund within xx days of parcel arrival and coupon limits per user per week).
Dynamic feedback adaptation: Static adherence to service rules and providing fixed solutions can limit the flexibility of reply-agents, preventing them from acting like excellent human customer service representatives. By enabling reply-agents to interact in real-time with their environment, considering user's behavioral trends, demands expressed during inquiries, and feedback on proposed solutions, personalized service can be provided. This approach fosters adaptive responses and progressive services and solutions, closely mirroring the flexibility and excellence of human customer service.
Self-reflection: Employing LLM's capabilities to understand, analyze, and evaluate its own behavior, fostering self-supervision and decision refinement through reflection on outputs, particularly with complex and ambiguous tasks.
Complex image processing: Handling scenarios involving numerous complex images (including shipping order photos, bank transaction screenshots, images of damaged goods received, and seller qualification certifications). These images contain key information crucial to enhancing the chatbot's problem resolution capabilities.