Multi-Task Retriever Models for Generalized In-Context Example Selection
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Solution Overview
Problem
Existing retrieval models for neural networks suffer from poor generalization, requiring multiple specialized models for different tasks and consuming significant computational resources due to overfitting to specific output formats, leading to inefficiencies in selecting demonstration examples for new tasks.
Innovation Solution
Generate demonstration embeddings by processing task instructions and queries without including example outputs, and augment training datasets with translated tasks to enhance language-agnostic representations, allowing a single retrieval model to handle diverse tasks across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a separate specialized retrieval model is trained for each task or group of related tasks, then task-specific performance is improved, but computational resources and time required for training increase significantly
Solution Approach 1:
The patent applies universality by training a single retrieval model to handle multiple tasks across different domains. The model is trained on diverse demonstration examples from various tasks, enabling it to generalize and perform well on unseen tasks without requiring separate specialized models for each task, thus reducing computational resource consumption while maintaining task-specific performance
2Reliability
If multiple specialized retrieval models are deployed for different tasks, then task-specific accuracy is improved, but memory requirements to store model parameters increase substantially
Solution Approach 1:
The patent implements a single universal retrieval model that can handle multiple tasks through in-context learning with demonstration examples. This eliminates the need to store multiple separate model parameters, significantly reducing memory requirements while maintaining the ability to perform accurately across different tasks through learned generalization patterns
3Reliability
If multiple retrieval models are managed and served during inference, then task-specific performance is maintained, but system complexity increases
Solution Approach 1:
The patent uses a single retrieval model that generalizes across tasks through in-context learning. This simplifies the system architecture by eliminating the need to manage multiple models during inference, reducing operational complexity while maintaining task-specific performance through effective demonstration example selection and contextual learning
4Reliability
If retrieval models are trained on task-specific demonstration examples, then performance on training tasks is improved, but generalization to new unseen tasks deteriorates due to overfitting
Solution Approach 1:
The patent extracts the output format information from the training process by excluding it from the demonstration examples used for training the retrieval model. This allows the model to learn task-relevant patterns without being constrained by specific output formats, thereby preventing overfitting to training tasks while maintaining the ability to generalize to new unseen tasks with different output requirements
Data Source
AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for performing a task on a query using a generative neural network by making use of demonstration examples, where the demonstration examples are selected using a retrieval model (i.e., a retrieval system that includes a demonstration encoder neural network and a query encoder neural network). By not processing any data identifying the respective example output of a demonstration example when generating the corresponding demonstration embedding for the demonstration example, the generalization of a retrieval model is improved. Further, by augmenting the training data set for the retrieval model using translation of tasks, the generalization of the retrieval model is further improved. As a result, a single, generalized retrieval model can be effectively used for a plurality of tasks, eliminating the need to train, store, and deploy multiple, specialized retrieval models.