Task-Oriented Dialogue Flow Using a Single LLM to Cut Latency
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Solution Overview
Problem
Current task-oriented dialogue systems face challenges in operational costs, rigidity of generated answers, high latency, and complexity in implementation due to reliance on Natural Language Understanding (NLU), Dialog Management (DM), and Natural Language Generation (NLG) modules, as well as the need for specialized knowledge in using large language models.
Innovation Solution
A method involving a pre-written dialogue flow in text form as model configuration for a task-oriented dialogue model, allowing direct answer generation with a single large language model call, reducing latency and enhancing user experience while lowering human resource and time costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional NLU, DM, and NLG modules are used for task-oriented dialogue, then dialogue functionality can be achieved, but operational costs increase and system complexity increases
Solution Approach 1:
The patent merges the traditional NLU, DM, and NLG modules into a single large language model. The LLM performs all three functions (understanding user intent, managing dialogue state, and generating responses) through its unified architecture, eliminating the need for separate modular components and their complex interconnections.
Solution Approach 2:
The large language model serves multiple functions simultaneously - it understands natural language input, manages dialogue flow and state, and generates appropriate responses. This multi-functional approach replaces the specialized single-purpose traditional modules with a single versatile system.
2Reliability
If traditional dialogue systems with multiple modules are used, then dialogue processing can be performed, but latency increases due to multiple processing steps
Solution Approach 1:
By combining NLU, DM, and NLG into a single LLM inference process, the system eliminates the sequential processing delays between multiple modules. The unified model processes understanding, state management, and response generation in one integrated forward pass, significantly reducing end-to-end latency.
3Adaptability or versatility
If specialized knowledge in large language models is required, then advanced dialogue capabilities can be achieved, but ease of operation decreases due to complexity in using LLMs
Solution Approach 1:
The system changes the configuration parameter from complex modular architectures to simple text-based dialogue flow descriptions. Users specify dialogue logic through natural language text rather than configuring multiple technical modules, making the system accessible to non-experts while maintaining advanced capabilities.
4Reliability
If multiple modules (NLU, DM, NLG) are implemented, then comprehensive dialogue functionality is achieved, but human resource and time costs increase
Solution Approach 1:
The patent consolidates three separate module implementations into a single LLM-based system. This merger reduces the total development time, fewer components need to be configured and integrated, and requires fewer specialized resources, thereby improving productivity while maintaining comprehensive dialogue functionality.
Data Source
AI summary
Provided is a task-oriented dialogue implementation method relating to artificial intelligence fields such as deep learning, large language models, natural language processing and intelligent agents, which can be applied to intelligent interaction scenarios such as intelligent customer service, intelligent outbound calling, and intelligent marketing. The task-oriented dialogue implementation method may include: acquiring a question to be answered; generate an answer corresponding to the question by using a task-oriented dialogue model, the answer is generated by the task-oriented dialogue model according to model configuration information, the model configuration information includes a dialogue flow corresponding to the task-oriented dialogue model, and the dialogue flow is written in text form.