Contrastive Pre-training for Text and Code Embeddings
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Solution Overview
Problem
Existing technologies for generating and working with text and code embeddings require significant computational resources at test time and do not effectively leverage contrastive pre-training methods to improve performance.
Innovation Solution
A method and system that utilize hardware processors to convert training data sets into vector representations by encoding paired data samples, accessing negative example pairs, and training an artificial machine learning model to generate additional vectors, based on both positive and negative example pairs, reducing computational load and improving performance through contrastive pre-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing technologies are used for generating text and code embeddings, then embeddings can be generated, but significant computational resources are required at test time
Solution Approach 1:
The patent applies contrastive pre-training during the training phase to learn robust embedding representations before test time. By pre-training the model to distinguish between positive and negative example pairs, the system prepares the model weights and embedding space in advance, so that at test time only lightweight inference is needed without requiring significant computational resources for contrastive computation.
2Reliability
If contrastive pre-training is implemented, then performance of text and code embeddings is enhanced, but training computational load increases
Solution Approach 1:
The system performs contrastive pre-training in advance during the training phase, where the model learns to differentiate between positive example pairs (semantically similar) and negative example pairs (semantically different). This preliminary learning establishes robust embedding representations and optimal model parameters before deployment, so that the enhanced performance is achieved without proportionally increased ongoing computational load.
Solution Approach 2:
The patent utilizes pre-trained language model parameters as initialization for the embedding model, transferring learned linguistic representations to the embedding task. This parameter transfer approach allows the model to leverage existing computational investments in pre-trained models while adapting to the specific contrastive embedding task, thereby improving performance efficiency.
3Use of energy by moving object
If unsupervised embedding models are used, then computational requirements are reduced, but performance may be limited without supervised fine-tuning
Solution Approach 1:
The system performs unsupervised contrastive pre-training as a preliminary step to learn general embedding patterns and semantic relationships from large corpora. This pre-training establishes a foundation of robust representations that can be efficiently applied to downstream tasks, reducing the need for extensive supervised fine-tuning while maintaining high embedding accuracy.
Solution Approach 2:
The patent initializes the embedding model with parameters from pre-trained language models, transferring learned linguistic knowledge and representations. This parameter initialization allows the model to start with strong semantic understanding, reducing the computational cost of training while achieving high accuracy in embedding generation for text and code.
Data Source
AI summary
Embodiments of the present disclosure may include systems, methods, and computer readable media for generating a vector representation, including receiving a training data set, the training data set including a plurality of paired data samples corresponding to positive example pairs, each positive example pair including a first data unit and a second data unit. Embodiments may also include converting the training data set into at least one first vector of a vector representation. Embodiments may further include accessing one or more negative example pairs to contrast against the positive example pairs. Embodiments may also include converting the one or more negative example pairs into one or more second vectors of the vector representation. Embodiments may further include training an artificial machine learning model to generate additional vectors of the vector representation. Further embodiments may include systems, methods, and media for determining semantic similarity based on one or more vector representations.