Unlabeled Dialogue Flow Generation via LLM Fine-Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for generating dialogue flows require significant human expertise and effort, and machine learning approaches using predefined labels are limited in scalability and adaptability across various domains and scenarios.
Innovation Solution
A machine learning model, such as a large language model, is fine-tuned with a small set of manually annotated conversations to automatically generate dialogue flows, using canonical forms to construct graphs from unlabeled conversations, enabling extraction of multiple paths for different tasks and scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual design process with domain experts is used to construct dialogue flows, then the quality and accuracy of dialogue flows are improved, but the resource intensity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary analysis of conversation data to automatically extract user intents, responses, and conversation pathways before human experts intervene. This preliminary structuring reduces the manual effort required while maintaining high dialogue flow quality, as experts only need to refine pre-generated content rather than create everything from scratch.
Solution Approach 2:
The system extracts and replicates effective conversation patterns from existing labeled conversations to generate dialogue flows for new domains. By copying and adapting proven conversation structures, the system maintains high quality without requiring extensive manual design for each new scenario, thereby improving resource efficiency.
2Ease of manufacture
If predefined labels are used in process mining to extract information from conversations, then the extraction process is simplified, but the adaptability to various domains and scenarios is reduced
Solution Approach 1:
The system employs a unified machine learning model that can process and extract information from conversations across multiple domains and scenarios using the same underlying architecture. The model learns domain-general conversation patterns while adapting to specific domains through training data, eliminating the need for separate predefined label sets for each domain while maintaining extraction simplicity.
Solution Approach 2:
The system changes the parameters of the extraction process by using learned representations from machine learning models instead of fixed predefined labels. This allows the extraction mechanism to adapt its behavior based on the input domain and scenario while maintaining a consistent, simple extraction interface, thus achieving both simplicity and adaptability.
3Extent of automation
If machine learning models are trained with manually annotated data to generate dialogue flows, then the automation level increases, but the initial resource investment and time requirements increase
Solution Approach 1:
The system uses a small subset of manually annotated conversations (partial action) rather than comprehensively annotating all training data. This partial annotation approach provides sufficient training signal for the machine learning model to learn effective dialogue generation, significantly reducing the time and resources required for data preparation while maintaining high automation levels.
4Manufacturing precision
If traditional manual methods are used to construct dialogue flows, then the control and precision over dialogue structure are improved, but the scalability to accommodate diverse user needs is reduced
Solution Approach 1:
The system transitions from static, manually固定的 dialogue structures to dynamic, data-driven dialogue flows that automatically adapt to different domains and user needs. The machine learning model learns conversation patterns from data and generates appropriate dialogue structures on-the-fly, maintaining precision through learned constraints while achieving scalability through automated adaptation.
Data Source
AI summary
In various examples, a technique for generating dialogue flows includes inputting a plurality of conversations into a machine learning model. The technique also includes generating, based at least on the machine learning model processing the plurality of conversations, a plurality of annotations comprising a plurality of constrained semantic representations for respective messages of sequences of messages included in the plurality of conversations. The technique further includes generating one or more dialogue flows from the plurality of constrained semantic representations and causing a conversational output to be generated based on the one or more dialogue flows.