Automated Social Media Comment Generation Using Prompt Selection
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Solution Overview
Problem
Existing methods for automatic commenting on social media platforms are inefficient and unable to generate high-quality comments that match the resources being commented on.
Innovation Solution
A method for information processing that involves obtaining text information about a resource and candidate prompts, selecting an optimal target prompt based on the text information, and generating comment information that matches the resource and prompt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual commenting is used on social media platforms, then comment quality can be maintained, but time consumption and inefficiency increase significantly
Solution Approach 1:
The patent replaces the mechanical manual commenting process with an automated system that uses large language models and prompt engineering. The system automatically generates comments by processing resource information through optimized prompts, eliminating the need for manual human intervention in the commenting process while maintaining scalability and efficiency.
Solution Approach 2:
The system enables self-service commenting where the automatic commenting tool generates comments autonomously based on resource information and pre-designed prompts. The large language model self-adjusts and optimizes prompt selection based on resource characteristics, allowing the system to serve itself without continuous human input or oversight.
2Productivity
If automatic commenting tools are used, then time efficiency improves, but comment quality and relevance to resources deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-designing and preparing multiple candidate prompts before the actual commenting process. These prompts are carefully crafted in advance to cover different commenting scenarios and styles. During operation, the system selects and applies the most appropriate pre-prepared prompt based on the resource characteristics, ensuring both efficiency and quality.
Solution Approach 2:
The system changes parameters by dynamically selecting different prompts based on resource characteristics. The large language model adjusts the prompt parameters (such as tone, style, length, and focus) according to the specific resource being commented on, thereby optimizing comment quality for each individual case while maintaining automated efficiency.
3Manufacturing precision
If multiple candidate prompts are evaluated, then comment relevance improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system evaluates candidate prompts based on their expected effectiveness for the given resource. The large language model assesses how well each prompt matches the resource characteristics and selects the optimal one. This feedback-driven selection process ensures high relevance without requiring complex manual intervention for each comment generation task.
Data Source
AI summary
A computer-implemented method for information processing includes: obtaining text information, in which the text information includes first text information of a resource to be commented on and second text information of a candidate prompt; selecting a target prompt from the candidate prompts based on the text information; and generating comment information of the resource to be commented on, based on the resource to be commented on and the target prompt.