Online LLM Data-Source Management for Hallucination Reduction
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Solution Overview
Problem
State-of-the-art machine learning based language models (LLMs) provide generic and often misleading answers due to hallucinations, making them inadequate for specific contexts, particularly in domains requiring accurate information, and lack the ability to manage conversation flow and data sources effectively.
Innovation Solution
An online system that utilizes a machine learning based language model to generate replies by integrating conversation flow routing, data source management, and critical analysis, leveraging metadata and prompts to enhance response relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic training data is used to train language models, then the model can provide answers applicable to a wide context, but the answers become generic and not helpful for specific contexts
Solution Approach 1:
The system segments the knowledge base into multiple data sources with different levels of generality. It separates generic training data from context-specific data sources, allowing the model to first learn general patterns and then retrieve specific contextual information when needed, resolving the contradiction between wide applicability and context-specific accuracy
Solution Approach 2:
The system introduces an intermediary retrieval mechanism that sits between the language model and the data sources. This intermediary retrieves relevant context-specific information from external data sources and provides it to the model, enabling the model to maintain its generic training benefits while accessing specific contextual information when required
2Adaptability or versatility
If language models are trained on large amounts of generic data, then they can process a wide variety of requests, but they suffer from hallucination and provide misleading information
Solution Approach 1:
The system performs preliminary retrieval of relevant information from external data sources before generating the final response. By pre-fetching and verifying context-specific data from reliable sources, the system reduces the likelihood of hallucination while maintaining the model's ability to handle diverse requests
Solution Approach 2:
The system implements a feedback mechanism where the generated response is verified against retrieved information from external data sources. This feedback loop ensures that the model's generative capabilities are constrained by factual information from reliable sources, reducing hallucinations while preserving versatility
3Measurement precision
If the system integrates multiple data sources and conversation flow routing, then response accuracy and relevance improve, but system complexity increases
Solution Approach 1:
The system employs a universal architecture where a single language model handles multiple functions: understanding user requests, retrieving relevant information from various data sources, and generating responses. This multi-functional approach improves response accuracy without proportionally increasing system complexity, as the same core model is leveraged across different operations
Data Source
AI summary
A system manages data sources used in an online conversation. The system stores data obtained from a plurality of data sources in a vector database. Each data source stores information associated with users of an organization. The system receives a natural language request and generates a prompt including metadata describing the data sources and requests the machine learning based language model to generate queries for extracting relevant data from the data sources. The system receives a response from the machine learning based language model including queries for extracting data relevant to the natural language request from the data sources. The system executes the queries to extract the relevant information relevant and uses the information for generating a reply to the natural language request using the machine learning based language model and sends the reply to the user via the user interface.