Dense Retrieval Progressive Distillation Training
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Solution Overview
Problem
Computer-based search and retrieval technologies, particularly those relying on machine-learning models, face challenges in identifying the most relevant search results due to mismatched training datasets and objective functions that do not align with the intended task, leading to lower relevance of search results.
Innovation Solution
The implementation of a dense retrieval system that employs a progressive distillation of metric knowledge from a ranker machine learning model to a retriever model, using a dense retriever and a ranker to select and rank search results based on embeddings and similarity scores, with the ranker trained according to an evaluation metric that defines a target ranking rather than direct scores, and the retriever 'warmed-up' by the ranker's output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using conventional methods with mismatched training datasets and objective functions, then the training process is simpler and faster, but the relevance of search results deteriorates
Solution Approach 1:
The training process is segmented into two distinct phases: a pre-training phase where the retriever model is trained on large-scale data to learn basic retrieval capabilities, and a fine-tuning phase where the model is trained on task-specific data with matched objective functions. This segmentation allows each phase to optimize for its specific goals, improving overall relevance while managing complexity through structured progression.
Solution Approach 2:
The system performs preliminary training of the retriever model on general data before fine-tuning on task-specific data. This preliminary action establishes a foundation of retrieval capabilities that can be efficiently adapted to specific tasks, reducing the complexity of training from scratch while ensuring high relevance through targeted fine-tuning on matched datasets and objective functions.
2Measurement precision
If machine learning models are trained on large datasets to improve relevance, then the accuracy of search results improves, but the training time and computational resources increase
Solution Approach 1:
The training process is divided into pre-training and fine-tuning stages, allowing the model to learn general patterns from large datasets efficiently in the pre-training phase, then focus on task-specific optimizations in the fine-tuning phase. This segmentation reduces overall training time compared to training on all data at once, while maintaining high accuracy through targeted learning in the fine-tuning stage.
Solution Approach 2:
The system performs preliminary training on large datasets to establish foundational accuracy, then uses this pre-trained model as a starting point for fine-tuning on smaller, task-specific datasets. This preliminary action leverages the computational efficiency of pre-training while reducing the time required for task-specific optimization, achieving high accuracy without proportionally increasing total training time.
Data Source
AI summary
Technologies described herein relate to dense retrieval and ranking of search results. A query indicating a computing context or user input is received. An embedding of the query is computed by way of a first encoder, and candidate results selected from a pool of potential results based upon the embedding of the query and embeddings of the potential results. A similarity score for a first of the candidate results is computed by way of a second encoder trained based upon an order metric that defines a ranking over a training set of potential results. The first encoder is trained based upon output of the second encoder prior to computing the embedding of the query. The candidate results are ranked based upon the similarity score of the first candidate result, and results responsive to the query are identified based upon the ranking. The identified results are output to a computing device.