Embedding-Based Model Fine-Tuning With Out-of-Domain Data
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Solution Overview
Problem
Machine learning models trained on insufficient or non-domain-specific data fail to provide accurate predictions, leading to poor outcomes and requiring significant resource expenditure to generate new training data, often missing edge cases and introducing unintended biases.
Innovation Solution
Utilize out-of-domain training data labeled with text and background characteristics to generate vector embeddings, train another machine learning model to predict these characteristics, and tune a textual content generation model based on distances between in-domain and out-of-domain embeddings, selecting and ordering data for tuning to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If general-purpose models are trained on large sets of non-domain-specific data, then the model can be trained without extensive target domain data, but the model performance in the target domain becomes insufficient and inaccurate
Solution Approach 1:
The patent uses vector embeddings as an intermediary representation to bridge the gap between out-of-domain training data and in-domain target data. The embeddings capture semantic relationships that allow the model to transfer knowledge effectively, enabling accurate predictions in the target domain without requiring extensive domain-specific training data.
Solution Approach 2:
The patent transforms data from its original form into vector embedding space, changing the representation parameters to enable better generalization. This parameter transformation allows the model to learn from out-of-domain data while maintaining applicability to the target domain, resolving the contradiction between ease of training and prediction accuracy.
2Reliability
If new training data is generated for the target domain to improve model accuracy, then prediction accuracy improves, but the cost and time required for data generation increase considerably
Solution Approach 1:
The patent creates a representation (vector embedding) that copies the essential semantic information from the target domain data, allowing the model to learn from out-of-domain data that replicates the necessary patterns. This copying approach enables accurate predictions without requiring time-consuming generation of extensive target domain training data.
Solution Approach 2:
The patent performs preliminary extraction of vector embeddings from out-of-domain data before fine-tuning the model. This preliminary action prepares the model with useful representations in advance, reducing the subsequent time and resources needed to achieve accurate predictions in the target domain.
3Adaptability or versatility
If existing models are fine-tuned on limited in-domain data, then the model can be adapted to the target domain, but the model fails to address edge cases and introduces unintended biases
Solution Approach 1:
The patent segments the training process into two distinct phases: pre-training on out-of-domain data to learn general patterns, and fine-tuning on in-domain data to adapt to the target domain. This segmentation allows the model to benefit from both the broad knowledge of out-of-domain data and the specificity of in-domain data, improving performance on edge cases while maintaining adaptability.
Solution Approach 2:
Vector embeddings serve as an intermediary that enables the model to bridge out-of-domain and in-domain data effectively. This intermediary mechanism allows the model to maintain adaptability to the target domain while incorporating useful patterns from out-of-domain data, thereby improving edge case performance without introducing unintended biases.
4Reliability
If extensive target domain training data is generated to ensure comprehensive coverage, then edge cases are addressed, but the cost and complexity of data generation increase significantly
Solution Approach 1:
The patent uses vector embeddings as an intermediary that captures the essential structure and relationships of the target domain data. This intermediary representation allows the model to infer and generalize to edge cases without requiring extensive targeted data generation, thereby reducing the complexity of the data generation process while maintaining comprehensive coverage.
Solution Approach 2:
The patent trains a universal model using out-of-domain data that can serve multiple functions: learning general patterns, adapting to the target domain, and handling edge cases. This multi-functionality reduces the need for separate data generation processes for different purposes, simplifying the overall system while ensuring comprehensive coverage.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for accessing out-of-domain training data that includes items of non-textual digital media content. Each of the items is labeled with text and background characteristic(s) that indicate an origination category of candidate origination categories for the item. A pre-trained model is used to generate vector embeddings of the out-of-domain training data and a particular vector embedding of a particular item of in-domain data that is labeled with text but is not labeled with any background characteristic(s) that indicate any origination categories. The generated vector embeddings are used to train another machine learning model to predict the background characteristic(s) based on vector embeddings of non-textual digital media content. The other machine learning model is further used to determine out-of-domain vector embeddings corresponding to the vector embeddings of the out-of-domain training data and in-domain vector embedding(s) corresponding to the in-domain data. Distances are determined between out-of-domain and in-domain vector embedding(s). Based on the distances, a textual content generation model is tuned on item(s) of the out-of-domain data. The item(s) of out-of-domain data to use for tuning may be selected and/or ordered based on the distances. A resulting model may be stored and used to transform unlabeled item(s) of non-textual content to textual content.