Advice Generation System Using LLM and Reinforcement Learning
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Solution Overview
Problem
Current advice systems provide generic advice, fail to adapt to user interactions, and often present complex information, leading to irrelevant or impractical recommendations that do not align with individual user needs and circumstances.
Innovation Solution
A machine learning advice generation system that uses a large language model, reinforcement learning, and problem classification to provide hyper-personalized advice by classifying user intents into specific domains and generating actionable guidance tailored to individual circumstances, with continuous refinement through user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic advice systems are used, then system complexity is reduced, but advice relevance to individual users deteriorates
Solution Approach 1:
The system segments the advice generation process into distinct components: a classification module that categorizes user queries into specific domains, and a retrieval module that fetches domain-specific advice. This segmentation allows the system to maintain simplicity in each component while achieving high overall relevance through specialized processing for different user needs.
2Stability of the object's composition
If static models are used, then system stability is improved, but adaptability to user interactions deteriorates
Solution Approach 1:
The system implements dynamic adaptability through a feedback mechanism where user interactions and feedback are continuously incorporated to refine and update the advice models. This allows the system to maintain stability in its core architecture while adapting to changing user needs and interactions over time, resolving the contradiction between stability and adaptability.
3Measurement precision
If technical jargon is used in advice output, then precision of information is improved, but user comprehension deteriorates
Solution Approach 1:
The system applies local quality by tailoring the output format and language complexity to match the specific domain and user context. Advice in different domains (e.g., healthcare, finance, technology) is presented with appropriate terminology levels, ensuring that each piece of advice is both precise for its domain and comprehensible to the target user audience.
4Device complexity
If one-size-fits-all advice is provided, then system simplicity is improved, but user satisfaction deteriorates
Solution Approach 1:
The system changes key parameters including user context, domain classification, and feedback metrics to generate personalized advice rather than one-size-fits-all recommendations. By dynamically adjusting these parameters based on user profiles and interactions, the system maintains operational simplicity while significantly improving user satisfaction and advice reliability through personalization.
Data Source
AI summary
The one or more embodiments provide for a method, system, and computer program product, an intent, generated by a large language model from a text, is received from a user device as a first input to an advice planner. A state of an account is received as a second input to the advice planner. The advice planner classifieds the intent into a domain corresponding to the intent, and generates, as output, a plan comprising a first set of action logic associated with the domain. Each action logic is a discrete step in an ordered sequence for achieving a desired state of the account. The advice planner forwards the plan to the large language model (LLM). The large language model receives the plan as input and generates advice in a natural language format as output. The advice is then forwarded to the user device.