Learning-to-Rank Training Using Linear Score Difference Vectors
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Solution Overview
Problem
Existing learning-to-rank frameworks in information retrieval systems face challenges in achieving high efficiency and quality, particularly in latency-constrained services like Smart Reply (SR) and Ambient Clinical Intelligence (ACI), where traditional similarity algorithms sacrifice quality for speed, and fixed batch sizes in neural models lead to inefficiencies.
Innovation Solution
A self-training semantic cross-attention ranking (sRank) framework using linear pairwise loss and mutable batch sizes, optimized for transformer-based models, reduces resource consumption and inference latency by caching document embeddings and applying dual-encoder cross-attention, enabling efficient training and real-time prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing learning-to-rank frameworks are used to match inquiries to documents, then document ranking capability is provided, but quality is sacrificed for speed and latency constraints are not met
Solution Approach 1:
The system segments the document ranking task into two distinct stages: a fast similarity search stage that uses pre-computed document embeddings and cosine similarity to retrieve candidate documents, followed by a more accurate learning-to-rank stage that applies the sRank model to refine the ranking of the retrieved candidates. This segmentation allows the system to achieve both speed (in the first stage) and quality (in the second stage).
Solution Approach 2:
The system performs preliminary actions by pre-computing and caching document embeddings before actual ranking queries are processed. This preliminary computation enables fast similarity search during query time without sacrificing ranking quality, as the pre-computed embeddings can be quickly retrieved and used in the ranking process.
2Measurement precision
If transformer-based models are used for document ranking, then accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The training process is segmented into efficient computation phases where the sRank model computes pairwise score differences and applies linear pairwise loss, avoiding the need for computationally intensive full transformer re-ranking during training. This segmentation enables accurate training while reducing overall training time and resource consumption.
Solution Approach 2:
The system changes the computational parameters by using linear pairwise loss instead of traditional quadratic pairwise loss, and by computing only the necessary score differences between documents rather than all possible pairs. This parameter change maintains accuracy while significantly reducing processing time and resource requirements.
3Measurement precision
If pairwise score difference matrix is computed for all document pairs, then ranking accuracy is improved, but computational complexity increases quadratically
Solution Approach 1:
The system segments the pairwise comparison task by only computing score differences for relevant document pairs rather than all possible pairs. By using the retrieved candidate documents from the first stage as a filter, the system reduces the number of pairwise comparisons needed, maintaining accuracy while reducing computational complexity from O(n²) to a smaller subset of comparisons.
Solution Approach 2:
Instead of computing complete pairwise score differences for all documents (excessive action), the system computes partial pairwise differences only for the retrieved candidate documents (partial action). This partial computation is sufficient to achieve accurate ranking while dramatically reducing computational complexity.
Data Source
AI summary
The disclosure herein describes training a document recommendation model using loss data generated from a linear score difference vector. A training data entry is provided including a query and a set of candidate documents. A document recommendation model generates a set of document prediction scores indicative of a likelihood that the candidate documents are responses to the query and a pairwise score difference matrix is generated using the set of document prediction scores. The pairwise score difference matrix is transformed into a score difference vector using a correct document vector that indicates a correct document among the set of candidate documents. Loss data of the document recommendation model is generated using the score difference vector and the document recommendation model is adjusted using the calculated loss data. Training the document recommendation model based on the linear score difference vector reduces resource usage when compared to training with a difference matrix.