AI Prompt Debiasing via Sentiment-Annotated Utterances
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Solution Overview
Problem
Conventional prompt design approaches for language processing models are resource-intensive and fail to produce consistent results, leading to inefficiencies in training and deployment.
Innovation Solution
The method involves extracting and annotating utterances from a dialogue corpus with sentiment polarity values, using these annotated utterances to train AI models, and applying diagonal matrix parameters to adjust output probabilities, thereby debiasing prompts and improving consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional prompt design approaches are used for language processing models, then model training can be performed, but the process is resource-intensive and fails to produce consistent results
Solution Approach 1:
The patent applies preliminary action by pre-extracting and annotating utterances from dialogue corpora before model training. Sentiment polarity annotations are prepared in advance, creating a structured and biased dataset that guides the training process, thereby improving result consistency while reducing the need for resource-intensive trial-and-error prompt design
Solution Approach 2:
The patent changes parameters by introducing diagonal matrix parameters (W) that adjust output probabilities based on sentiment polarity. This parameter transformation modifies the model's output distribution to favor consistent and accurate responses, resolving the contradiction between reliability and resource consumption by providing a mathematical mechanism for bias correction
2Productivity
If conventional prompt design approaches are used, then models can be trained, but training efficiency is low and deployment is complex
Solution Approach 1:
The patent segments the training process into distinct phases: extracting utterances from dialogue corpora, annotating them with sentiment polarity values, and then using these annotated utterances for model training. This segmentation improves productivity by creating a systematic workflow that can be efficiently automated, while reducing deployment complexity through modular design
Solution Approach 2:
The patent introduces an intermediary mechanism using diagonal matrix parameters (W) that mediate between the raw model outputs and the final predictions. This intermediary layer simplifies deployment by providing a clear mathematical transformation that corrects biases without requiring complex prompt engineering or post-processing
Data Source
AI summary
Methods, systems, and computer program products for debiasing prompts in connection with artificial intelligence techniques are provided herein. A computer-implemented method includes extracting multiple utterances from at least one dialogue-related corpus; annotating each of at least a portion of the multiple extracted utterances with a sentiment polarity value; selecting at least a portion of the annotated utterances to be used in training one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the selected annotated utterances.