Machine Learning Prompt Ensembles for Grounded Text Responses
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Solution Overview
Problem
Conventional language generation systems suffer from inaccuracies such as hallucinations and operational inflexibility, generating responses that are not grounded in pertinent documents and relying on rigid training data.
Innovation Solution
A machine learning prompt ensemble system that iteratively generates text responses, identifies misalignment portions, and uses them as negative examples to reduce hallucinations by utilizing supporting digital documents and alignment scores to improve accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional language generation systems use pre-defined machine learning inputs, then they can generate text responses, but they suffer from operational inflexibility and cannot adapt to dynamic external resources
Solution Approach 1:
The system dynamically adjusts prompts based on alignment scores between generated responses and supporting documents. When alignment is low, the system modifies prompts to incorporate negative examples from misaligned portions, enabling adaptive operation with external resources while managing complexity through iterative refinement
2Reliability
If conventional systems generate text responses without grounding in pertinent documents, then text generation is simplified, but hallucinations occur and accuracy decreases
Solution Approach 1:
The system implements feedback by calculating alignment scores between generated responses and supporting documents, then using misaligned portions as negative examples in subsequent prompts. This feedback loop continuously improves response accuracy by preventing hallucinations while maintaining manageable system complexity through automated evaluation
Solution Approach 2:
The system performs preliminary actions by selecting and preparing supporting documents before generating responses. It pre-identifies relevant documents and prepares them as context for the language model, ensuring grounded responses from the outset while managing complexity through structured document selection
3Reliability
If the system generates multiple text responses and uses iterative prompt generation, then hallucination reduction improves, but computational time increases
Solution Approach 1:
The system applies partial action by generating a limited number of responses (e.g., top-k responses) rather than exhaustively exploring all possible generations. It uses alignment scoring to evaluate only the most promising responses and selectively uses misaligned portions as negative examples, reducing computational time while maintaining effective hallucination reduction
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that iteratively generates, utilizing a machine learning model, text responses to reduce hallucinated content. In particular, in some embodiments, the disclosed systems receive a digital query and selects one or more supporting digital documents for the digital query. Furthermore, in some embodiments the disclosed systems generate a first text response from a first text prompt generated by using the digital query. Moreover, in some embodiments the disclosed systems extract a misalignment portion of the first text response by comparing the first text response and the one or more supporting digital documents. Additionally, from the misalignment portion of the first text response and the digital query, the disclosed systems further generate a second text response.