Self-Referencing Query Detection for Coherent Chatbot Context
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Solution Overview
Problem
Existing chatbots struggle with context management, particularly in maintaining semantic coherence and providing relevant responses due to difficulties in detecting context switches and self-referencing queries, leading to unsatisfactory automated interactions.
Innovation Solution
A system and method for self-referencing detection in chatbots that involves generating prompts tailored to large language models, using a self-referencing detection model to determine query relationships, and selecting appropriate models to enhance context management, including preprocessing and postprocessing operations to ensure relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If topic identification or key word approach is used for context management, then automated responses can be generated, but the responses are often unsatisfactory and fail to detect context switches
Solution Approach 1:
The patent introduces self-referencing queries as an intermediary mechanism to bridge the gap between simple keyword matching and complex context understanding. By having the chatbot generate queries that reference itself and its previous responses, the system creates a self-validation loop that improves context detection without requiring full human intervention. This mediator approach allows automated responses while improving contextual accuracy through iterative self-reference.
Solution Approach 2:
The system implements feedback loops where the chatbot's own responses are used as input for generating subsequent self-referencing queries. This feedback mechanism allows the system to learn from its previous outputs and improve context management over time. The feedback from self-referencing detection is used to refine keyword extraction and topic identification, creating a continuous improvement cycle that maintains automation while enhancing precision.
2Productivity
If simple keyword extraction is used, then processing is fast, but the chatbot cannot maintain semantic coherence in conversations
Solution Approach 1:
The patent applies preliminary action by pre-generating self-referencing queries based on previous chatbot responses before actual user queries arrive. This pre-computation of contextual references allows the system to quickly match incoming queries against pre-established context patterns, maintaining fast processing speeds while improving semantic coherence through advance context preparation.
Solution Approach 2:
The system segments context management into multiple independent components: keyword extraction, self-referencing query generation, context switching detection, and response coherence validation. Each segment can be processed independently and in parallel, maintaining high productivity while the combined effect of all segments ensures reliable semantic coherence through comprehensive context analysis.
Data Source
AI summary
Prompt engineering and context management in applications. A chatbot is configured to generate a response to a query. Context management in the chatbot uses a prompt instance and a model to determine whether a current query references a reference query. The output of the model may impact the input provided to a model that generates a response to a user's query. A database of queries is used in prompt engineering and to test multiple models to identify a combination of a prompt instance/model to be deployed to the application.