Modular Therapy Dialogue Architecture for Explainable Goal Direction
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Solution Overview
Problem
Large language models, despite their advancements, face challenges in computationally inefficient fine-tuning, lack of explainability, and inability to engage in goal-directed conversations, making them unsuitable for applications like automated therapy without significant modifications.
Innovation Solution
A modular dialogue system comprising subject understanding models and a general language model, where the former provides clinical insights and constraints, enabling goal-directed and explainable interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If large language models are used for therapy applications, then human-like dialogue and empathetic responses are achieved, but computational efficiency deteriorates during fine-tuning
Solution Approach 1:
The system divides the therapy application into two separate components: a frozen general language model for generating human-like responses, and a separate subject understanding model for clinical analysis. This segmentation allows the language model to be used without computationally intensive fine-tuning, as it leverages pre-trained knowledge rather than requiring adaptation to specific therapy domains.
Solution Approach 2:
A subject understanding model acts as an intermediary between user input and the language model. This intermediary component processes clinical information and generates structured prompts that guide the language model's responses, eliminating the need for direct fine-tuning of the large language model while maintaining therapy-specific effectiveness.
2Ease of operation
If large language models are used for therapy applications, then varied and nuanced responses are achieved, but explainability deteriorates
Solution Approach 1:
The subject understanding model serves as an explainable intermediary that processes user input through structured clinical frameworks and generates detailed subject profile information. This intermediary provides transparent reasoning about clinical assessments and intervention recommendations, making the system's decision-making process explainable while the language model handles the generation of nuanced responses.
Solution Approach 2:
By separating the explainable subject understanding functions from the generative language model, the system maintains both explainability and nuanced response capability. The subject understanding model provides clear, structured explanations of clinical reasoning, while the language model generates empathetic and varied therapeutic responses.
3Ease of operation
If large language models are used for therapy applications, then coherent long-term text completion is achieved, but goal-directed conversation capability deteriorates
Solution Approach 1:
The subject understanding model continuously monitors the conversation and generates feedback in the form of structured prompts that guide the language model's responses toward therapeutic goals. This feedback mechanism ensures the conversation remains goal-directed and adaptive to therapeutic needs while the language model maintains coherent text generation.
Solution Approach 2:
The system dynamically adjusts the interaction between the subject understanding model and language model based on conversation progress and therapeutic goals. The subject understanding model adapts its prompt generation to align with evolving therapeutic objectives, enabling the system to maintain both coherence and goal-directedness throughout the conversation.
Data Source
AI summary
A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention. [FIG. 14(a)]