Efficient Continuous Training of Large Language Models with Flexible Data Access and Removal

Core Concepts

AdapterSwap enables efficient continuous training of large language models while providing fine-grained control over data access and removal.

Abstract

The paper introduces AdapterSwap, a parameter-efficient approach for continual learning of large language models (LLMs) that addresses challenges around data access control and retroactive data removal.
Key highlights:

AdapterSwap organizes knowledge from a data collection into a set of low-rank adapters, which are dynamically composed during inference.
This allows for efficient fine-tuning of the model on new data without catastrophic forgetting of old information.
The adapter-based approach also enables organizations to have fine-grained control over data access and deletion, as removing a specific data source only requires retraining the corresponding adapter.
Experiments demonstrate AdapterSwap's ability to support efficient continual learning, while also enabling access control and data removal guarantees.
The authors compare AdapterSwap's performance to alternative approaches like iterative fine-tuning and full model retraining, showing that it mitigates forgetting better.
AdapterSwap leverages low-rank adapters (LoRA) and a retrieval model to dynamically compose the most relevant adapters for a given query, optionally filtered by access control.

Stats

The paper presents several key metrics to quantify the benefits of AdapterSwap:

Training time for a single adapter scales linearly with the data partition size.
Perplexity on the document completion task improves as the number of adapters increases, up to a point.
Total GPU hours required to train all adapters on the full dataset is roughly equivalent across different partitioning schemes.
Accuracy in retrieving the "oracle" adapter (the one trained on the relevant data) ranges from 69% to 81% for the top-1 retrieval, and 93% to 95% for the top-3.
Perplexity increases significantly when trying to complete documents that have been removed from the dataset, demonstrating the efficiency of adapter-level data removal.

Quotes

"AdapterSwap organizes knowledge from a data collection into a set of low-rank adapters, which are dynamically composed during inference."
"Removing a specific data source only requires retraining the corresponding adapter, in contrast to full model retraining."
"Experiments demonstrate AdapterSwap's ability to support efficient continual learning, while also enabling access control and data removal guarantees."

Key Insights Distilled From

AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees

by William Fles... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08417.pdf

Deeper Inquiries

How could AdapterSwap be extended to handle out-of-domain queries more effectively, beyond the current retrieval-based approach

To enhance AdapterSwap's effectiveness with out-of-domain queries, we can explore a few key strategies. One approach is to incorporate a more sophisticated retriever model that can better discern relevant adapters for a given query. This could involve leveraging more advanced techniques like semantic similarity measures or contextual embeddings to improve the adapter selection process. Additionally, implementing a feedback loop mechanism where the system learns from user interactions and adapts its adapter selection strategy over time could enhance performance with out-of-domain queries. By continuously refining the retrieval process based on user feedback, AdapterSwap can become more adept at handling a wider range of queries effectively.

What are the potential drawbacks or limitations of the adapter-based approach compared to alternative continual learning methods like gradient-based techniques

While AdapterSwap offers several advantages for continual learning, there are some potential drawbacks and limitations to consider. One limitation is the need to train and maintain multiple adapters, which can increase computational overhead and complexity compared to gradient-based techniques that update a single model. Additionally, the adapter-based approach may require more manual intervention and fine-tuning to optimize adapter selection and composition, which could be challenging in dynamic or rapidly changing environments. Moreover, the performance of AdapterSwap heavily relies on the quality of the retriever model, and inaccuracies in adapter selection could impact overall system performance. Lastly, the need to retrain individual adapters when data is removed could introduce latency and additional computational costs, especially in scenarios with frequent data purges.

Could AdapterSwap be combined with other data management schemes, such as retrieval-augmented generation, to further improve its capabilities

Combining AdapterSwap with other data management schemes like retrieval-augmented generation (RAG) could lead to a more comprehensive and robust system for handling knowledge in large language models. By integrating RAG capabilities, AdapterSwap could leverage the strengths of both approaches to enhance knowledge retrieval and generation processes. For example, AdapterSwap could use RAG to retrieve relevant context for a given query and then utilize the adapters to fine-tune the response generation based on the retrieved information. This hybrid approach could improve the system's ability to handle complex queries, incorporate external knowledge sources, and generate more accurate and contextually relevant responses. By synergizing AdapterSwap with RAG, organizations can benefit from a more versatile and adaptive system for managing and utilizing knowledge in language models.