Thread-Based User Assistance System Reducing AI Hallucination
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Solution Overview
Problem
Conventional search engines and chatbots face challenges in accurately interpreting user queries due to unique communication styles and the risk of AI hallucination, particularly in lengthy or multi-threaded dialogs, which limits their effectiveness in providing personalized user assistance, such as in job searches.
Innovation Solution
A directive generative thread-based user assistance system that employs large language models constrained by entity graphs and contextual resources to generate prompts, reducing AI hallucination and enhancing the system's ability to handle multi-threaded dialogs efficiently and scalably.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines and chatbots are used to interpret user queries, then basic information retrieval is possible, but accuracy deteriorates due to unique communication styles and AI hallucination in lengthy or multi-threaded dialogs
Solution Approach 1:
The system segments the dialog into multiple threads, each representing a distinct topic or intent. This segmentation allows the system to track and manage different conversational contexts separately, improving accuracy in interpreting user queries while reducing hallucination risks by maintaining clear boundaries between different discussion topics.
Solution Approach 2:
The system performs preliminary classification of user inputs into predefined categories before generating responses. By pre-defining valid response categories and classifying user intent in advance, the system constrains the generative model to select from validated options, thereby reducing AI hallucination while maintaining accurate query interpretation.
2Ease of operation
If conventional chatbots simulate natural language conversation, then user-friendly interaction is achieved, but effectiveness deteriorates in providing personalized user assistance
Solution Approach 1:
The system incorporates feedback loops where user responses are continuously analyzed and used to refine the classification of subsequent queries. This feedback mechanism enables the system to adapt to individual user communication styles and preferences, enhancing personalized assistance while maintaining natural conversational flow.
Solution Approach 2:
The system dynamically adjusts its classification categories and response strategies based on the evolving conversation context. By making the classification system dynamic rather than static, the chatbot can adapt to different user needs and communication patterns in real-time, improving both ease of operation and personalized assistance capability.
3Adaptability or versatility
If generative models are used to respond to user inputs, then conversational flexibility is improved, but AI hallucination increases in lengthy or multi-threaded dialogs
Solution Approach 1:
The system introduces an intermediary classification layer between the user input and the generative model. This intermediary classifies user queries into predefined categories and constrains the generative model to select responses from validated options, thereby maintaining conversational flexibility while reducing AI hallucination in lengthy or multi-threaded dialogs.
Solution Approach 2:
The system changes the parameter space of the generative model by constraining it to select from predefined response categories rather than generating free-form text. This parameter constraint maintains conversational flexibility within defined boundaries while significantly reducing the risk of AI hallucination.
Data Source
AI summary
Embodiments of the disclosed technologies include generating a first thread classification prompt based on a first thread portion of an online dialog involving a user of a computing device, sending the first thread classification prompt to a first large language model, receiving a first thread classification generated and output by the first large language model based on the first thread classification prompt, formulating a plan execution prompt based on the first thread classification, sending the plan execution prompt to a second large language model, receiving a second thread portion generated and output by the second large language model based on the plan execution prompt and the online dialog, and generating a label for a third thread portion of the online dialog.