Conversational Search Prompting for In-Prompt Hallucination Control
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Solution Overview
Problem
Generative language models (LLMs) often produce inaccurate or nonsensical information with high confidence, known as AI hallucination, undermining trust and reliability, and conventional approaches for updating models based on user feedback suffer from latency and resource-intensive re-engineering.
Innovation Solution
Implementing a chain of thought reasoning (COTRO) approach that focuses on refining the model-generated output rather than refining the model input, which includes a chain of thought reasoning on the model-generated output rather than refining the model-generated output rather than on refining the model input, to reduce the risk of hallucination in the output during performance of the content generation task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches update models based on user feedback, then model accuracy improves, but latency increases and resource consumption increases
Solution Approach 1:
The system performs hallucination detection and correction during the content generation process itself, rather than waiting for user feedback. The chain-of-thought reasoning is executed preliminarily to identify and correct potential hallucinations before they reach the user, eliminating the need for post-deployment model updates and reducing latency.
Solution Approach 2:
An intermediary hallucination detection mechanism is introduced between the generative model and the user. This intermediary layer analyzes the generated content using chain-of-thought reasoning to detect hallucinations and generates correction prompts, avoiding the need for direct model updates while maintaining accuracy.
2Measurement precision
If conventional approaches update models based on user feedback, then model accuracy improves, but computational resources increase
Solution Approach 1:
The generative model performs self-correction through chain-of-thought reasoning and hallucination detection during the generation process. The system autonomously identifies and corrects its own hallucinations without requiring external re-engineering or additional computational resources for model updates.
Solution Approach 2:
An intermediary correction mechanism handles hallucination detection and correction tasks, preventing the need for resource-intensive model retraining. The intermediary layer processes generated content and generates correction prompts, reducing overall computational resource consumption.
3Productivity
If generative language models produce content quickly, then productivity increases, but reliability decreases due to hallucinations
Solution Approach 1:
The system executes chain-of-thought reasoning preliminarily during content generation to detect and correct hallucinations before final output. This preliminary action maintains rapid content generation while ensuring reliability through real-time hallucination management.
Solution Approach 2:
The hallucination detection and correction process operates continuously during content generation rather than as a separate post-processing step. The chain-of-thought reasoning is integrated into the generation flow, maintaining continuous useful action that preserves both speed and reliability.
Data Source
AI summary
A device may provide a prompt to a first machine learning model. The prompt may include at least one instruction to cause the first machine learning model to use at least first natural language input associated with a use of a conversational search system to rank data sources, generate a first search query and reasoning, and use the first search query and the reasoning to generate a second search query. The first search query may include data obtained from at least one of the ranked data sources. The reasoning may include an explanation of how the first machine learning model generated the first search query. A second machine learning model may synthesize a response determined via execution of the second search query. The synthesized response may be provided for presentation via the conversational search system.