Automated Language Model Term Prioritization and Pruning
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Solution Overview
Problem
Existing language models and ontologies face challenges in efficiently expanding and pruning terminology to adapt to domain-specific vocabularies and evolving user communication, leading to increased computational overhead and latency in understanding user intentions.
Innovation Solution
A system and method for automatically prioritizing language model and ontology expansion and pruning by monitoring text from multiple platforms, identifying new terms, and recompiling the model based on frequency and context, with human input for validation and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If language model and ontology are continuously expanded to adapt to domain-specific vocabularies and evolving user communication, then the model's adaptability and relevance improve, but computational overhead and latency increase
Solution Approach 1:
The system performs preliminary actions by proactively monitoring text from multiple platforms and identifying new terms before they become critical. Terms are added to the training data with priority flags based on frequency thresholds and contextual relevance, allowing the model to adapt in advance rather than reacting to performance degradation, thus reducing the latency of actual model updates
Solution Approach 2:
The system implements self-service through automated monitoring and term identification capabilities that continuously scan multiple platforms for new terminology. The automated prioritization mechanism evaluates term frequency and context relevance without human intervention, enabling the model to self-update based on observed usage patterns, reducing the need for manual model maintenance while maintaining adaptability
2Adaptability or versatility
If language model and ontology are continuously expanded to capture new terminology, then the model's relevance to domain-specific vocabularies improves, but model size and computational complexity increase
Solution Approach 1:
The system applies local quality by assigning different priority levels to different terms based on their frequency and contextual relevance. High-priority terms that meet threshold criteria are added to the training data, while lower-priority terms are monitored but not immediately incorporated. This selective approach ensures the model focuses computational resources on the most relevant vocabulary, maintaining vocabulary coverage without uniformly increasing model complexity across all potential terms
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting priority thresholds and frequency cutoffs based on observed usage patterns. As term frequency increases or contextual relevance is confirmed, terms transition from monitoring status to active training data inclusion. This parameter-based control mechanism allows flexible adjustment of vocabulary coverage versus model complexity trade-offs without requiring complete model retraining
3Measurement precision
If manual review is used to determine whether terms should be added to training examples, then term selection accuracy improves, but processing time and operational complexity increase
Solution Approach 1:
The system implements partial action by applying manual review only to a subset of terms that meet specific priority criteria, rather than reviewing all candidate terms. The automated prioritization mechanism filters and ranks terms based on frequency thresholds and contextual analysis, presenting only the most promising candidates for human review. This partial review approach maintains high term selection accuracy for critical terms while significantly reducing the time and operational burden compared to comprehensive manual review
Data Source
AI summary
A system and method for updating computerized language models is provided that automatically adds or deletes terms from the language model to capture trending events or products, while maximizing computer efficiencies by deleting terms that are no longer trending and use of knowledge bases, machine learning model training and evaluation corpora, analysis tools and databases.