Multi-Phase Machine Learning Training for Summarization
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Solution Overview
Problem
Existing computer-based text interpretation and summarization techniques face challenges such as input limits for long-form dialogs, context-dependent transcripts, hallucinations in summaries, incomplete summaries, and high memory footprints, making them unsuitable for real-time inferencing.
Innovation Solution
A multi-phase training process for machine learning models that includes an initialization phase and a training phase. The initialization phase generates an initial weight set through various initialization operations, optimizing the model for a specific prediction domain. The training phase uses reinforcement learning with an aggregated reward metric to optimize the model, addressing deficiencies such as hallucinations and missing key features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If traditional transformer-based pretrained models are used for text summarization, then the model structure is simple and easy to implement, but the input length is limited which reduces applicability for long form dialogs
Solution Approach 1:
The patent divides the long transcript processing into multiple segments or phases. The model processes input text in manageable chunks rather than requiring the entire long transcript to be processed at once, thereby extending the effective input length capability while maintaining reasonable model architecture complexity.
Solution Approach 2:
The patent introduces multi-phase training as an additional dimension to the model processing framework. Instead of simply increasing model size to handle longer inputs, it adds a temporal dimension through sequential training phases (pre-training, fine-tuning, refinement) that enable the model to progressively learn from long-form data.
2Length of moving object
If models are trained on long transcript size and short summary sized datasets, then the model can handle long inputs, but the summaries become incomplete with missing information
Solution Approach 1:
The patent implements feedback mechanisms during the multi-phase training process where the model's summarization output is evaluated and used to adjust subsequent training. This feedback loop ensures that important information is preserved and that summaries remain complete while handling long transcripts.
Solution Approach 2:
The patent performs preliminary actions by pre-training the model on extensive long-form data to learn contextual relationships and information preservation patterns before fine-tuning on specific summarization tasks. This preliminary learning phase enables the model to maintain information completeness when processing long inputs.
3Device complexity
If loss functions consider entire sentences during loss calculation, then the calculation is simple, but important aspects such as key words or phrases are missed and summaries are inflated with unimportant words
Solution Approach 1:
The patent segments the loss calculation from sentence-level to word-level or phrase-level components. By breaking down the evaluation into smaller units (individual words, key phrases, important terms), the model can precisely assess which elements are truly important versus filler content, improving summary quality without excessive complexity.
Solution Approach 2:
The patent applies different quality standards to different parts of the summary. Instead of uniform sentence-level evaluation, it applies local quality assessment to identify and weight key words and phrases differently from common stop-words, enabling precise measurement of summary quality while maintaining computational feasibility.
4Measurement precision
If techniques with increased accuracy are used to reduce hallucinations and improve summary quality, then the summary accuracy improves, but the memory footprint increases making them unsuitable for real-time inferencing
Solution Approach 1:
The patent applies partial action by implementing selective refinement - not all model parameters are updated with equal intensity during training. Certain critical parameters receive more attention and refinement while others maintain their pre-trained values, achieving high accuracy without requiring excessive memory resources for real-time operation.
Solution Approach 2:
The patent employs parameter changes through multi-phase training where model parameters are progressively adjusted and refined. The model transitions from general pre-trained parameters to task-specific fine-tuned parameters, achieving high accuracy while managing memory footprint through efficient parameter utilization during inference.
5Ease of manufacture
If language models are deployed for real-time inferencing with maximum likelihood estimation based training, then the training approach is straightforward, but the latency increases due to data preprocessing and model inferencing times
Solution Approach 1:
The patent performs preliminary data preprocessing and model preparation during the multi-phase training period. By pre-processing data and pre-warming model states during training phases, the model is optimized for rapid inference at deployment, reducing latency without complicating the training approach.
Solution Approach 2:
The patent introduces dynamic elements to the training process through multi-phase adaptation where the model learns to efficiently process inputs at varying speeds. The model adapts its processing dynamics during training to optimize for real-time performance, balancing computational thoroughness with speed requirements.
Data Source
AI summary
Various embodiments of the present disclosure provide machine learning training techniques for implementing a multi-phase training process to holistically train a machine learning summarization model. The multi-phase training process may include generating, using the machine learning summarization model, a training summary for a training transcript. A first, second, and/or a third reward metric may be generated based on the training summary. Each reward metric may be tailored to a different aspect of the machine learning summarization model. For example, the first reward may be based on a comparison between the training summary and a target summary corresponding to the training transcript. The second reward may be based on the training summary and a positive/negative summary. The third reward may be based on training key phrases from the training summary. The model may be trained by optimizing an aggregated reward metric derived from the first, second, and/or third reward metrics.