Multimodal Prompt Generation for Policy-Aware Content Moderation
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Solution Overview
Problem
Current content moderation systems are inefficient and computationally expensive due to the need for re-training on policy updates, lack interpretability, and struggle with maintaining multiple policy versions across regions, leading to ambiguous decisions.
Innovation Solution
A dynamic multimodal prompt generation system that segments policy documents into chunks, dynamically generates prompts based on content and policy similarity, and uses a large language model to make moderation decisions without re-training, providing interpretability through traceable policy chunks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current content moderation systems are used, then content can be moderated, but the system is computationally expensive and inefficient due to re-training requirements
Solution Approach 1:
The system segments policy documents into multiple policy chunks or blocks, allowing the LLM to process only relevant portions of policies rather than entire policy documents. This segmentation reduces the computational burden and enables more efficient content moderation by focusing processing power on specific policy sections that are relevant to the content being evaluated.
Solution Approach 2:
The system performs preliminary actions by pre-processing policy documents into structured chunks and pre-computing embeddings for these chunks. This preparation work is done in advance, so when content moderation is needed, the system can quickly retrieve and compare relevant policy chunks without performing expensive re-training operations, thereby reducing computational costs and improving efficiency.
2Reliability
If traditional content moderation systems are used, then moderation decisions can be made, but interpretability is lacking
Solution Approach 1:
The system implements feedback mechanisms that provide explanations for moderation decisions by tracing back to specific policy chunks that influenced the decision. The LLM generates rationale that references the relevant policy sections, allowing users to understand why certain content was flagged or approved. This feedback loop enhances interpretability while maintaining reliable decision-making.
Solution Approach 2:
The system introduces an intermediary layer that acts as a bridge between the LLM's internal decision-making process and the user's need for interpretability. This intermediary component translates the LLM's decisions into human-understandable explanations by referencing specific policy chunks and generating rationale text, thereby recovering the interpretability information that would otherwise be lost in the black-box LLM process.
3Adaptability or versatility
If multiple policy versions are maintained across regions, then regional compliance can be achieved, but device complexity increases
Solution Approach 1:
The system segments policies into modular chunks that can be independently managed and retrieved. Different regional policy versions are divided into comparable units, allowing the system to selectively apply relevant chunks based on the user's location and the content being moderated. This segmentation reduces complexity by avoiding the need to manage entire policy documents as monolithic units.
Solution Approach 2:
The system creates a universal framework using LLMs and embeddings that can handle multiple regional policy versions through a common interface. The same core moderation infrastructure works across different regions by retrieving and applying relevant policy chunks, making the system multi-functional without requiring separate complex systems for each region. This universality reduces overall system complexity while maintaining regional adaptability.
4Measurement precision
If policy updates are implemented frequently, then content moderation accuracy improves, but re-training requirements increase computational expense
Solution Approach 1:
The system performs preliminary processing of policy updates by converting new or updated policy chunks into embeddings and storing them in the vector database before they are needed for moderation. When policies are updated, only the affected chunks are re-processed and added to the database, rather than re-training the entire system. This preliminary action enables frequent policy updates with minimal computational expense.
Solution Approach 2:
The system dynamically adapts to policy updates by retrieving relevant policy chunks from the vector database during content moderation, rather than relying on static pre-trained models. When policies are updated, the system can immediately incorporate new policy chunks without re-training, allowing frequent updates that maintain high moderation accuracy while avoiding the computational cost of repeated re-training operations.
Data Source
AI summary
Aspects of the disclosure include methods and systems for content moderation, and specifically dynamic multimodal prompt generation for efficient content moderation. A method includes receiving, by a prompt generation system, a request for a decision corresponding to content. The method includes generating, by an encoder of the prompt generation system, an embedding of the content, and retrieving, by an embedding based retrieval (EBR) module of the prompt generation system, K retrieved chunks from a database, the K retrieved chunks having a Kth closest distance to the embedding in an embedding space. A dynamic prompt comprising a prompt template, multiple retrieved chunks of the K retrieved chunks, and the content is generated and input to a pre-trained large language model. The LLM generates the decision, which is returned responsive to the request.