Messaging Prompt Generation Using User Attributes and Intent
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Solution Overview
Problem
Conventional machine learning models face challenges in generating domain-specific conversational text with varying tones, semantics, and syntaxes, requiring significant human intervention and resource-intensive training, and struggle to produce outputs that align with natural human conversational patterns.
Innovation Solution
A content generation system for generative language models that includes an attribute extraction component, prompt generation component, and deep learning model, utilizing domain-specific data to generate professional and casual conversations by leveraging attribute data and user profiles, and incorporating feedback mechanisms to refine outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional machine learning models are used to generate conversational text, then basic text generation is possible, but the quality of domain-specific conversational text with varying tones and semantics deteriorates
Solution Approach 1:
The system applies different processing strategies to different parts of the input data. The attribute extraction component identifies specific domains and tones in the input message, then the prompt generation component creates domain-specific prompts tailored to those characteristics. This allows the model to generate high-quality conversational text adapted to specific domains while maintaining the ability to handle various tones and semantics.
Solution Approach 2:
The system dynamically changes parameters based on the input characteristics. The prompt generation component adjusts prompt parameters (such as domain-specific instructions, tone specifications, and semantic constraints) based on the extracted attributes from the input message. This enables the model to adapt its behavior to different domains and conversational contexts, improving text quality without requiring complete retraining.
2Manufacturing precision
If human intervention is increased to improve conversational text quality, then text quality improves, but system complexity and resource consumption worsen
Solution Approach 1:
The system implements automated attribute extraction and prompt generation that operates without human intervention. The attribute extraction component automatically identifies domains, tones, and semantic characteristics from input messages, and the prompt generation component automatically creates optimized prompts based on these attributes. This self-service mechanism maintains high text quality while minimizing the need for human input and reducing system complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated conversational text can be evaluated and used to refine future generation. The attribute extraction and prompt generation components learn from the effectiveness of generated responses, automatically adjusting their parameters to improve text quality over time without requiring additional human intervention or increasing system complexity.
3Manufacturing precision
If domain-specific training data is used to improve conversational quality, then text quality improves, but training resource consumption worsens
Solution Approach 1:
The system performs preliminary attribute extraction and prompt generation before the actual text generation process. By pre-processing the input message to extract domain-specific attributes and generate optimized prompts in advance, the system reduces the computational burden during the main generation phase. This preliminary action enables high-quality domain-specific text generation without requiring extensive real-time training resources.
Solution Approach 2:
The system introduces an intermediary prompt generation component that translates domain-specific training data into optimized prompts. Instead of directly using large amounts of domain-specific training data during generation, the system uses the attribute extraction component to identify relevant characteristics and the prompt generation component to create condensed, domain-specific prompts that capture the essence of the training data. This intermediary approach maintains text quality while reducing training resource consumption.
Data Source
AI summary
Methods, systems, and apparatuses include receiving input from a client device to facilitate electronic messaging between a first user associated with first attribute data and a second user, where the client device provides a messaging interface that facilitates the electronic messaging. A messaging intent is determined based on the first attribute data of the first user, where the messaging intent corresponds to a purpose of the electronic messaging. A set of attributes of the first attribute data is mapped to prompt inputs based on the messaging intent. A generative language model is applied to the prompt inputs. Suggestions for adding messaging content in the messaging interface are output by the generative language model based on the prompt inputs. The suggestions are presented on the messaging interface.