Autonomous Language Model Adaptation via Self-Training
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Solution Overview
Problem
Existing speech-to-text systems face challenges in producing accurate transcriptions due to the degradation of language models when encountering new vocabulary or slang, as their training materials often differ from actual operational inputs, leading to poor performance.
Innovation Solution
A system and method that selects words from transcriptions based on their probabilities, uses these words to search for additional content objects, and continuously trains a language model by updating it with new textual content, allowing the model to evolve autonomously and adapt to changing language and topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If language models are trained with traditional training materials, then the initial language model can be created, but the model degrades when encountering new vocabulary or slang that differs from training material
Solution Approach 1:
The language model system performs self-training by automatically selecting words from transcriptions, searching for content objects, and updating its own training materials without external intervention. This self-service mechanism enables continuous adaptation to new vocabulary and slang, resolving the contradiction between initial model creation and ongoing adaptability.
Solution Approach 2:
The system implements feedback loops where transcription results are analyzed to identify high-probability words, which then guide content object searches that feed back into language model retraining. This feedback mechanism ensures the model continuously improves its adaptability while maintaining transcription accuracy through iterative self-updates.
2Adaptability or versatility
If language models are continuously updated with new training materials, then adaptability to new vocabulary improves, but system complexity and computational resources increase
Solution Approach 1:
Instead of completely retraining the language model with all possible new content, the system selectively updates the model using only high-probability words from transcriptions and relevant content objects. This partial action approach maintains adaptability improvements while avoiding the computational overhead and complexity of full system retraining.
Solution Approach 2:
The language model undergoes dynamic, incremental updates rather than static batch retraining. The model adapts continuously through small, targeted adjustments based on transcription feedback, allowing adaptability to improve over time without requiring complex batch processing infrastructure or complete system reconfiguration.
3Loss of information
If words are selected based on probabilities from transcriptions, then relevant content objects can be identified for training, but the selection process requires additional computational steps
Solution Approach 1:
The system performs preliminary probability assessment of words during the transcription process itself, before content object search begins. By pre-identifying high-probability words that are most likely to represent accurate vocabulary, the system reduces subsequent processing time while ensuring that only the most informative content objects are retrieved for training, thus minimizing information loss efficiently.
Data Source
AI summary
A computer-implemented method of generating a language model. An embodiment of a system and method may include selecting a set of words from a transcription of an audio input, the transcription produced by a current language model. The set of words may be used to obtain a set of content objects. The set of content objects may be used to generate a new language model. The current language model may be replaced by the new language model.