Dynamic Language Model Boosting for Fast Trending-Term Updates
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Solution Overview
Problem
Existing language models are static and costly to update, making it difficult to incorporate trending terms and phrases, which can lead to reduced relevance and missed opportunities for content providers.
Innovation Solution
Implement a dynamic, lightweight update mechanism using a class-based language model and a boosting model, trained with an entity weight tuner, to dynamically incorporate trending entities without full retraining, utilizing AutoML for weight optimization and domain-specific tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static language model is used, then the model structure is simple and cost-effective, but the model cannot adapt to trending terms and phrases
Solution Approach 1:
The language model is segmented into a base language model and a boosting language model. The base model maintains the original static structure, while the boosting model handles trending entities separately. This segmentation allows the system to adapt to trending terms without retraining the entire model, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system introduces dynamic updating mechanisms through the boosting model, which can be retrained independently with trending entities. This dynamic component allows the overall system to adapt to changing trends while the base model remains static, balancing adaptability with structural simplicity.
2Adaptability or versatility
If the language model is retrained to incorporate new terms, then the model relevance improves, but the time and resource cost increases significantly
Solution Approach 1:
By segmenting the model into base and boosting components, only the boosting model needs retraining when new terms emerge. This avoids the time-consuming full retraining process while maintaining model relevance through targeted updates with trending entities.
Solution Approach 2:
Instead of retraining the entire language model (excessive action), the system retrains only the boosting model with trending entities (partial action). This partial updating approach achieves the necessary relevance improvement without incurring the full time and resource cost of complete retraining.
3Productivity
If a boosting language model is implemented, then the model can dynamically update with trending entities, but the computational resources required increase
Solution Approach 1:
The computational workload is segmented between the base model (no retraining needed) and the boosting model (selective retraining). This segmentation reduces overall computational resource usage compared to retraining the full model, while still enabling dynamic updates through the boosting component.
Solution Approach 2:
The boosting model acts as a lightweight, disposable component that can be quickly retrained with trending entities and then discarded or updated. This approach uses fewer computational resources than maintaining a fully retrainable model, enabling fast updates with lower energy consumption.
Data Source
AI summary
Language models may be dynamically updated for trending entities based on tuning data for particular users. A user may provide specific tuning data associated with trending entities within a class to generate a weight map for a language model. A class based model may be trained using the weight map specific for the user for the trending entities. Additionally, weights may be further boosted using a boosting language model to emphasize the trending entities.