Text Generation Feedback Loop for Role-Play Accuracy
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Solution Overview
Problem
Current text generation technologies face challenges in generating accurate and consistent text for role-playing tasks, often resulting in low accuracy and inconsistency between the generated text and the scene, where the text does not match the style of the role being played or exceeds the role's ability.
Innovation Solution
A text generation method that involves obtaining a candidate text based on a prompt and role information, correcting it using a feedback mechanism to ensure accuracy, and rewriting the prompt to guide the generation of text that matches the role's style and ability, using a combination of language models for efficient and comprehensive feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text generation is performed based on prompt and role information, then text generation speed is improved, but text accuracy and consistency with role style deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the generated text is evaluated against the role information and prompt requirements. The evaluation results are fed back to correct and optimize the prompt, which then guides subsequent text generation. This closed-loop feedback ensures that the generated text maintains high accuracy and consistency with the role style while preserving efficient generation speed.
Solution Approach 2:
The patent performs preliminary actions by pre-evaluating the generated text against the role information before final output. The prompt is optimized in advance based on the evaluation feedback, ensuring that subsequent generation processes produce accurate and consistent text without requiring repeated trial-and-error iterations.
2Manufacturing precision
If correction mechanism is added to improve text accuracy, then text accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges the correction mechanism with the existing text generation system by integrating the evaluation and optimization functions into the prompt processing workflow. The correction mechanism does not operate as a separate standalone system but is combined with the prompt generation and text generation modules, thereby improving text accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The patent designs the correction mechanism to serve multiple functions: it evaluates text accuracy, checks consistency with role information, optimizes the prompt, and guides subsequent generation. This multi-functionality reduces the need for separate specialized components, thereby improving accuracy without linearly increasing system complexity.
3Manufacturing precision
If prompt rewriting is performed based on corrected text, then text consistency with role style is improved, but processing time increases
Solution Approach 1:
The patent ensures continuity of useful action by making the prompt rewriting process an integrated part of the continuous generation loop. The optimized prompt is immediately used in the next generation cycle without idle waiting time, and the evaluation-feedback-correction-optimization cycle continues uninterrupted. This continuous operation minimizes total processing time while maintaining high consistency with role style.
Solution Approach 2:
The patent applies parameter changes by adjusting the prompt based on evaluation feedback regarding text consistency with role style. The optimized prompt parameters (such as role constraints, style guidelines, and content requirements) are modified to better align with the desired output, enabling faster and more accurate generation in subsequent iterations without requiring manual intervention or extensive reprocessing.
Data Source
AI summary
The present application discloses a text generation method and apparatus, a device, a storage medium and a program product. The text generation method includes: obtaining a first candidate text based on a first prompt and first role information; correcting the first candidate text based on first information to obtain a first corrected text; determining a second prompt based on the first corrected text and the first prompt, and obtaining a first text based on the second prompt and the first role information.