Adapter Layers in Transformer Models for Efficient Neural Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural retrieval models require significant computational resources and storage for fine-tuning, leading to inefficiencies and scalability issues, particularly in adapting to out-of-domain datasets, and conventional parameter-efficient methods do not effectively address these challenges in information retrieval tasks.
Innovation Solution
Incorporation of adapter layers into transformer layers of pretrained language models to create sparse representations for documents, allowing for efficient training and adaptation with a fraction of the parameters, using techniques like L1 regularization and FLOPS regularization to maintain effectiveness and sparsity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full fine-tuning of pretrained language models is used for neural retrieval, then model effectiveness is improved, but computational resources and storage requirements increase significantly
Solution Approach 1:
The model parameters are segmented into two distinct groups: frozen pretrained language model parameters and trainable adapter parameters. This segmentation allows the system to maintain the effectiveness of the full model while only training a small subset of parameters (adapters), significantly reducing computational resources and storage requirements for fine-tuning.
Solution Approach 2:
The essential task-specific adaptation capabilities are extracted from the full model and concentrated into compact adapter modules. These adapters contain only the necessary parameters for domain adaptation, separating the core language understanding (frozen) from task-specific adjustments (trainable), thereby reducing the quantity of parameters that need to be stored and trained.
2Adaptability or versatility
If conventional fine-tuning methods are used, then adaptation to out-of-domain datasets is achieved, but scalability and efficiency deteriorate
Solution Approach 1:
The adapter modules are designed to be dynamically configurable and easily interchangeable. Different adapter sets can be quickly swapped to adapt to various out-of-domain datasets without retraining the entire model, enabling efficient domain adaptation while maintaining scalability across multiple domains and tasks.
Solution Approach 2:
The system changes parameters selectively by only training adapter parameters while freezing the pretrained model parameters. This parameter change strategy maintains adaptability to out-of-domain datasets through the trainable adapters while preserving the efficiency and scalability of the frozen pretrained model, avoiding the computational burden of full fine-tuning.
3Reliability
If dense embeddings are used for retrieval, then retrieval effectiveness is improved, but inference speed and efficiency decrease
Solution Approach 1:
The system applies different representation qualities to different parts of the retrieval system: dense embeddings are used where they provide effectiveness (in the neural retriever with adapters), while sparse representations are utilized for efficient candidate generation. This local differentiation of representation quality maintains retrieval effectiveness while improving overall inference speed.
Data Source
AI summary
In a method for training a first-stage neural retriever, adapter layers are inserted into one or more transformer layers of a pretrained language model (PLM) in an encoder configured to receive one or more documents and generate a sparse representation for each of the documents. The first-stage retriever is trained on a downstream task to update one or more parameters of the inserted adapter layers.