Conversation Critic Prompt Routing for Context-Specific LLM Answers
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Solution Overview
Problem
State-of-the-art machine learning based language models, such as transformer-based neural networks, provide generic and often misleading responses due to hallucinations, making them inadequate for specific contexts and domains requiring accurate information.
Innovation Solution
An online system that manages conversations using a machine learning based language model by configuring a user interface, routing conversation flows, managing data sources, and performing critical analysis to generate context-specific and accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic training data is used to train language models, then the models can provide answers applicable to a wide context, but the answers become generic and not helpful for specific contexts
Solution Approach 1:
The patent segments the training data into generic data for broad applicability and specific domain data for context-specific accuracy. The system separately processes general language patterns and domain-specific knowledge, combining them to achieve both wide adaptability and precise context-specific responses.
Solution Approach 2:
The patent applies local quality by enhancing the language model with domain-specific training data and context-aware processing for specific applications. Different parts of the system handle different quality requirements: general language understanding uses broad training data while domain-specific responses use targeted data and validation.
2Adaptability or versatility
If language models are trained on large generic datasets, then they can process various requests, but they suffer from hallucination and provide misleading information
Solution Approach 1:
The patent implements feedback mechanisms including fact-checking processes, confidence scoring, and validation steps that verify the accuracy of model outputs before presentation. The system continuously monitors for hallucinations and adjusts its responses based on verification results, improving reliability while maintaining versatility.
Solution Approach 2:
The patent introduces intermediary layers between the language model and the output, including fact-checking modules, verification systems, and validation processes. These intermediaries filter and verify the model's responses to eliminate hallucinations and misleading information while preserving the model's ability to handle diverse requests.
3Productivity
If state of the art LLMs are used for specific domains, then they can handle various tasks, but they provide inadequate and inaccurate answers for domains requiring precise information
Solution Approach 1:
The patent merges general-purpose language models with domain-specific knowledge bases, expert systems, and specialized data sources. This combination allows the system to handle various tasks through the general model while ensuring domain-specific accuracy through integrated specialized resources and validation mechanisms.
Solution Approach 2:
The patent creates a composite system combining multiple components: general language modeling capabilities, domain-specific training data, fact-checking mechanisms, and verification processes. This composite structure achieves both high productivity in task handling and high precision in domain-specific answers by integrating diverse strengths.
Data Source
AI summary
An online system performs conversations with users of an organization in relation to the organization. The online system presents a chat interface that allows users to ask natural language questions related to the organization or to other users of the organization. The online system generates prompts and sends to a machine learning based language model to get a response. The online system monitors the conversations to generate critical analysis of the conversation, for example, by analyzing the pacing of the conversation, the types of personalities of the participants of the conversation. The system modifies prompts generated for responding to one or more subsequent natural language requests received from the user to cause the machine learning based language model to generate responses that cause the one or more attributes to change