Policy Label Training with Characteristic Questions for Media Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Efficient training of Multimodal Large Language Models (MLLMs) is challenging due to the difficulty in generating and refining policy labels that accurately represent user interests and content relevance.
Innovation Solution
A method is developed to generate characteristic questions with TRUE or FALSE answers, identify examples from a searchable database, tune these questions to match an interest policy, and use a classifier MLLM to create policy labels, training a classifier model based on these labels to accurately label media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier MLLM is used to generate characteristic labels for content sets, then the accuracy of policy labels is improved, but the training complexity and computational resources required increase
Solution Approach 1:
The training process is divided into multiple stages: first generating characteristic questions from interest policies, then creating content sets based on these questions, and finally training the classifier model incrementally. This segmentation allows the complex training task to be broken down into manageable steps, reducing overall training complexity while maintaining label accuracy.
Solution Approach 2:
Characteristic questions and content sets are generated in advance before the actual model training begins. This preliminary preparation of training data and evaluation criteria allows the training process to focus solely on optimizing the classifier, reducing computational overhead and simplifying the training workflow.
2Measurement precision
If characteristic questions are tuned to match interest policies, then the relevance of policy labels to user interests is improved, but the time required for label generation increases
Solution Approach 1:
The system uses confidence criteria to evaluate the quality of generated characteristic labels and iteratively refines the tuning process. By incorporating feedback mechanisms that assess label quality against predefined confidence thresholds, the system achieves high relevance without requiring excessive manual tuning time, as automated feedback loops optimize the process efficiently.
3Adaptability or versatility
If a searchable database is used to identify characteristic question examples, then the coverage of content sets is improved, but the computational resources required for searching and processing increase
Solution Approach 1:
The system retrieves a sufficient number of characteristic question examples from the searchable database to achieve comprehensive content set coverage, without exhaustively processing every possible example. This partial action approach provides adequate coverage for training while avoiding the excessive computational resources that would be required to process the entire database.
Data Source
AI summary
For generating policy labels, a method generates at least one characteristic question that represents an interest policy. The method identifies characteristic question examples from a searchable database, wherein at least one characteristic question example represents each answer to each characteristic question. The method generates first policy labels for each characteristic question of the characteristic question examples. The method generates a content set of search results from the searchable database for search terms generated from each characteristic question. The method generates characteristic labels for the content set using a classifier Multimodal Large Language Model (MLLM) and a confidence criteria. The method generates second policy labels from the characteristic labels for the content set. The method trains a classifier model based on the content set and the second policy labels. The method generates a given policy label for media content using the classifier model.