Boosting Language Model Updates for Trending Entity Search
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Solution Overview
Problem
Existing language models are static and costly to update, failing to dynamically incorporate trending entities or phrases, which can lead to reduced relevance and missed opportunities for users.
Innovation Solution
Implement a class-based language model trained with an entity weight tuner using user-provided data and domain classification, combined with a boosting model to dynamically rescore trending entities, enabling lightweight updates without full retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If language models are updated to incorporate new trending terms, then relevance and search accuracy improve, but training time and computational resources increase significantly
Solution Approach 1:
The patent divides the language model into two separate components: a static base language model and a dynamic boosting model. The base model handles general language understanding while the boosting model specifically handles trending entities. This segmentation allows the boosting model to be updated frequently without requiring retraining of the entire language model, thus reducing training time while maintaining search accuracy.
Solution Approach 2:
The patent implements a dynamic boosting model that can be rapidly updated with trending entities without full retraining. The boosting model dynamically adapts to new trends by incorporating updated entity lists and retraining only the boosting component, enabling the system to respond quickly to changing search patterns while minimizing computational overhead.
2Adaptability or versatility
If language models are retrained frequently to capture trending entities, then relevance improves, but computational cost and resources increase
Solution Approach 1:
By segmenting the language model into a static base model and a dynamic boosting model, the patent enables selective retraining of only the boosting component when trends change. This avoids the high computational cost of retraining the entire language model while maintaining adaptability to trending entities.
Solution Approach 2:
The patent applies partial retraining by updating only the boosting model with trending entities rather than retraining the complete language model. This partial action approach provides sufficient adaptability for trending search terms while significantly reducing computational resource requirements.
3Loss of energy
If static language models are used, then computational resources are saved, but the models fail to recognize trending terms until retrained
Solution Approach 1:
The patent introduces dynamic updating capability through the boosting model that can be rapidly retrained with trending entities. This dynamic component allows the system to quickly recognize and adapt to new trending terms without requiring full language model retraining, thus reducing both computational resources and time delays compared to purely static models.
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.