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

Abstract

Large language models (LLMs) are increasingly capable of completing knowledge intensive tasks by recalling information from a static pretraining corpus. Here we are concerned with LLMs in the context of evolving data requirements. For instance: batches of new data that are introduced periodically; subsets of data with user-based access controls; or requirements on dynamic removal of documents with guarantees that associated knowledge cannot be recalled. We wish to satisfy these requirements while at the same time ensuring a model does not forget old information when new data becomes available. To address these issues, we introduce AdapterSwap, a training and inference scheme that organizes knowledge from a data collection into a set of dynamically composed low-rank adapters. Our experiments demonstrate AdapterSwap's ability to support eficient continual learning, while also enabling organizations to have fine-grained control over data access and deletion.

Keywords

• eol>LLM
• adapter
• access-control
• data removal
• forgetting

1. Introduction

A motivating application of AdapterSwap is displayed in Figure 1. In this fictitious example, AdapterSwap has been fit to hospital data with subsets of the data subject to various access-controls. Both a cardiologist and member of the finance ofice submit the query ‘ How much does the average cardiac visit cost?’ The retriever model selects the most relevant adapters to the query for which each user has access. The cardiologist’s unique access to appointment notes and patient records enables the model to access specific payments from cardiac patients and respond accordingly. In contrast, the finance ofice’s access to payroll and supply expenditures results in a response from the hospital’s perspective without leaking the private patient information to the unauthorized employee.

The rest of this paper is structured as follows. In Section 2 we discuss challenges which motivate our research. In Section 3 we provide context with prior works from which we build upon. Section 4 details AdapterSwap, our proposed approach. We demonstrate and quantify benefits of AdapterSwap through careful experimentation in Section 5. We discuss additional related works in Section 6. Finally, we conclude and suggest future research in Section 7.

Specifically, in this work we: • Develop an eficient approach for continuous knowledge acquisition through the training and dynamic composition of multiple LoRA adapters; • Demonstrate our method’s ability to guarantee data access-control and handle data removal in an eficient manner; • Quantify our performance via a document completion task across a diverse set of LLMs: Falcon-7B,

Gemma-7B, Llama-2-7B, and Mistral-7B; and • Show that our approach mitigates forgetting better than iterative fine-tuning and retraining.

2. Motivation

Our work is primarily motivated by three issues faced when fine-tuning and deploying LLMs in realworld organizations. Namely, how to utilize data with access-controls, how to remove knowledge from a model retroactively, and how to update knowledge over time as new data becomes available.

2.1. Data Access-control

It is common for organizations to apply access-control to their sensitive data [ 6 ]. For example, only certain employees at a hospital have access to patient records to protect privacy. Similarly, employees at a law firm are concerned with attorney-client privilege. Access-control can also be an important aspect of business models such as a news aggregator giving access to users and their personalized chatbots based on their paid subscriptions [ 7 ]. We would like a language model trained on these data to inherit and guarantee these access restrictions.

2.2. Data Protection and Removal

Organizations can unexpectedly lose the rights to maintain certain data. For example, data protection policies such as the General Data Protection Regulation (GDPR) [ 4 ] allow users to recall their data from corporations. The Stack, a popular dataset comprised of source code repositories, allows users to opt-out of having their code in future versions of the dataset but ofers no solution for models trained on previous versions [ 8 ]. Similarly, the removal of training data later found to be copyright protected or unlicensed might be mandated through legal action, a growing concern for LM producers [ 9, 5 ]. Existing models provide no mechanism to remove all knowledge from individual training examples, so to comply with these mandates would require the entire model to be retrained. Therefore, we would like a more eficient approach to guarantee the removal of data from models.

2.3. Catastrophic Forgetting

Organizations often have access to continuous or evolving streams of data. Catastrophic forgetting is an issue that arises when a machine learning model forgets previously seen information as it learns from new data [ 10 ]. LLMs have been shown to sufer from forgetting during the fine-tuning process [ 11 ]. We would like a method that addresses this issue and guarantees the ability to recall old information as new knowledge is continuously acquired.

3. Background

3.1. Parameter Eficient Fine-Tuning

As language models have become more specialized, growing in size and capabilities, fine-tuning an entire model has become unreasonable on commodity hardware. To address these challenges, several methods have been developed for performing parameter eficient fine-tuning .

References [ 12 ] and [ 13 ] present techniques for training a sparse subset of parameters in a multi-task setting. Alternatively, prompt tuning and prefix tuning concatenate learned task-specific embeddings to the sequence of inputs or activations being processed by a model [ 14, 15 ].

In the wider context of transfer learning, adapter layers provide a straight forward mechanism to eficiently and efectively generalize a base model to a target task or domain by fine-tuning a new set of parameters (an adapter) on the target data [ 1, 2 ]. Low-rank adapters (LoRA) have emerged as a parameter eficient approach to fine-tuning large language models with reasonable amounts of compute [ 3 ]. In this work, we leverage LoRAs to continuously update and control a language model’s knowledge.

3.2. Model Averaging and Segmentation

Several approaches have been suggested for combining model weights or outputs with demonstrated increases in performance or eficiency in certain scenarios. Reference [ 16 ] proposed Model Soups which average the weights of multiple models trained on the same data with diferent hyper-parameters. While they show that the soups increase performance and robustness of language models, they do not address combinations of models trained on separate data.

AdapterFusion was introduced by [ 17 ] as an approach to multi-task learning that segments taskspecific knowledge into separate adapters that are then combined via an attention mechanism. While segmenting tasks is similar to segmenting data based on access controls, the attention mechanism adds additional complexity and the model dependency prevents the eficient removal of data.

Reference [ 18 ] extended ideas from AdapterFusion with their approach AdapterDrop. AdapterDrop prunes adapters for increased eficiency but still lacks the ability to address eficient data removal or continual training.

AdaMix was proposed by [ 19 ] and uses a mixture-of-experts approach to combining adapters at each layer for the purpose of parameter sharing but not for specific knowledge segmentation.

Hierarchical Adapters by [ 20 ] and AdapterSoup by [ 21 ] are the most similar to our approach as they train individual adapters on segmented domains in the training data, but their focus is on combining the adapters to perform well with out-of-domain queries, while our focus is on in-domain access-control and eficient knowledge deletion.

4. AdapterSwap

4.1. Data Segmentation and Adapter Training

The first stage of our approach is choosing a data partitioning scheme. Data is segmented into separate access-control categories and further sharded based on desired per-shard compute requirements2. A pretrained LLM is then used as a base model for fine-tuning a separate LoRA adapter per data partition. As the information from each partition is isolated to a single adapter, the adapter inherits the accesscontrol categories of the data. This partitioning and fine-tuning stage can be done once for a static dataset, or on a continual basis as data arrives.

4.2. Retrieval Model

Similar to AdapterSoup, we use a Gaussian Mixture Model (GMM) to retrieve the subset of adapters relevant to a given query during inference. To fit the GMM we use a pretrained SBERT [ 22 ] model to embed randomly held-out samples from each partition into vectors of dimension 7683. We further reduce the dimension of these vectors by applying linear discriminant analysis (LDA). While previous approaches such as [ 21 ] and [ 23 ] use principal components analysis (PCA), in this work we compare PCA to LDA. LDA has the theoretical benefit of maximizing the linear separability of the clusters in the lower dimensional space by using their labels in a supervised manner. We found that using LDA over PCA significantly improved our downstream retrieval accuracy which we discuss in Section 5.3. Finally, the GMM is fit on the lower dimensional vectors with the number of components equal to the number of adapters. The eficiency of LDA and GMMs allows for cheap retraining of the retriever if new adapters are added over time.

4.3. Inference

During inference, queries are embedded using the same approach, and the GMM is used to rank potential adapters. The user’s access control categories are applied so that restricted adapters are prevented from being selected. We explore several retrieval modes where either the top-1, top-2, or top-3 adapters with the highest GMM density are selected and combined with the base model. We also attempted averaging all non-restricted adapters with equal weight or by weighting according to GMM density, but the results for those scenarios were poor and are therefore omitted. In practice, the choice of retrieval mode could vary by context. For example, our experiments show that top-1 results are likely best conditioned on knowledge that the information being retrieved was isolated to a single adapter. However, combinations of multiple adapters have been shown to perform better for out of domain queries [ 21 ].

4.4. Data Removal

Finally, if circumstances arise that require permanently removing data from our training set, only the weights associated with the LoRA adapter trained on the removed data need to be retrained; less than 0.1% of the base model’s parameters in our experiments. This contrasts with emerging approaches such as [ 24 ] for eficiently fine-tuning over the entire base model; which ofers no savings as all fine-tuned parameters would require discarding if data is removed. 2See Section 5.2 for a discussion on this topic. 3Specifically, we use the all-mpnet-base-v2 model from https://huggingface.co/sentence-transformers/all-mpnet-base-v2.

In Section 5 we demonstrate AdapterSwap using several models under both access-control and data removal scenarios. We also compare AdapterSwap’s ability to prevent forgetting with alternative methods for continuous learning. An overview of AdapterSwap training is illustrated in Figure 2.

5. Experiments

We demonstrate the efectiveness by AdapterSwap through multiple experiments. First, we describe the datasets and models used for our experiments in Section 5.1. In Section 5.2 we establish the training times and baseline performance when fitting multiple adapters over a sharded dataset.

Next, in Section 5.3 we quantify our ability to retrieve and compose mixtures of adapters during inference. We demonstrate AdapterSwap’s ability to conform to data access-controls in Section 5.4 and efectiveness when removing data in Section 5.5. Finally, we compare AdapterSwap’s resilience to forgetting against two alternative approaches for continuous learning in Section 5.6

For all of our experiments, we evaluate AdapterSwap by segmenting documents into equal halves and measuring the perplexity of the model while force decoding the second half given the first. This document completion task is suited for determining if a model has trained on and remembered particular samples from the datasets. We also report training times in GPU Hours using a single 80GB A100 GPU.

5.1. Data and Models

We use two datasets for our experimentation. First, we use the subset of C4 [ 25 ] utilized by [ 21 ] which contains 21 website domains where unique pages from the domain represent separate documents. We treat each domain as a separate LoRA training group for our experiments involving access-control and data purging. The list of training domains and their corresponding document counts are shown in Table 1.

We also use an English subset of the WMT News Crawl Dataset [ 26 ]. Passages from articles published in the year 2020 were extracted and deduplicated following [27]. These passages were then segmented into LoRA training groups based on their month of publication. The chronological nature of this dataset makes it suitable for measuring a model’s ability to recall previous training data as subsequent months are trained.

We leverage a diverse set of LMs as base models to ensure our approach generalizes. We replicate experiments across Falcon-7B [28], Gemma-7B [29], Llama-2-7B [30] and Mistral-7B-v0.1 [31].

5.2. Shard Size, Time, and Performance Trade-ofs

For each model, we trained a separate adapter on all training groups. Because our groups naturally difer in size we are able to measure average training time and performance for diferent sharding strategies. Figure 3 displays diferences in training time and document completion performance as a function of shard size. Partitioning the dataset into smaller groups results in the need for more adapters but enables faster training of each. The individual training time is an important characteristic when rs60 u oH40 PU20 G s 60 r tep 40 ad 20 A # 0

We utilize the HuggingFace [32] library to access the pretrained models and fit LoRA adapters using Parameter Eficient Fine-Tuning [ 33]. All adapters were trained on a single 80GB A100 GPU to standardize timing comparisons. In practice, AdapterSwap can be trained in parallel across several devices. Each adapter was trained for 10 epochs with a batch size of 20. Rank 32 LoRA adapters were applied to all attention layers for the News Crawl data and rank 64 adapters on all linear layers for C4. All adapters were initialized with the same random seed, which was identified by [ 21 ] as being necessary for adapter mixing. A detailed list of hyperparameters are included in appendix. y itx 4 e l p re 2 P rs900 u o lH800 a t o T700 faced with the need to retrain adapters if data is later purged. We observe that smaller partition sizes tend to result in better perplexities, likely due to adapters having a higher ratio of parameters to training tokens. Overall, the total GPU hours is roughly equivalent across partitioning schemes, with a slight overhead resulting from adding each additional adapter.

5.3. Retrieval

An optimal retriever should return the adapters which have knowledge related to a given query. In our case we know each sample was seen by only a single adapter, which we refer to as the oracle adapter, but multiple adapters might be necessary in general information retrieval scenarios. Table 2 displays the document completion performance across all models when using the oracle adapter, as well as an average of the top-1, top-2, and top-3 adapters as ranked by the retriever model. We also show the top-1 adapter with PCA as the projection method to compare with previous works [ 21, 23 ]. For all models, using the top-1 adapter with LDA resulted in the best completions and retrieved the oracle adapter with accuracy varying from 69% to 81%. With the exception of the Gemma-7B model, results for the top-2 and top-3 schemes are reasonable and [ 21 ] show that higher mixtures work well when the query is out of domain. The accuracy for retrieving the oracle adapter in the top-3 ranged from 93% to 95%, suggesting a potential inference scheme like [34]’s, where output is selected from the mixture and individual adapters via a separate content-selector. AdapterSwap can be directly applied to scenarios where data is organized into access-control categories. We simulate this with the C4 dataset by assigning each domain to a diferent category. For each document completion, we use our retrieval model to return the top-1 adapter under two scenarios: with access to all adapters and with access to all but the adapter trained on the restricted domain.

The results are summarized in Table 3. As expected, the best completion was achieved using the adapter with access to the relevant data, and performance dropped significantly when the retriever did not have access to the domain. This demonstrates AdapterSwap’s ability to enforce access-control at the adapter level, providing the best results to users while simultaneously preventing unauthorized access to restricted training data. ↓No Access

↓With Access

5.5. Data Removal

We also measure our ability to eficiently purge documents from our dataset after training. When a piece of data is removed from the corpus, only the adapter fine-tuned on that data requires retraining. We show this by attempting to complete documents for each adapter before and after removing them and retraining the adapter.

Table 4 summarizes the results for this experiment. We see a significant performance drop when trying to complete documents that have been purged from their adapter as the model is now guaranteed to have lost access to that data. Referring back to Figure 3, retraining a single adapter is up to 80x more eficient than if you had to fine-tune over the entire dataset. Purging with AdapterSwap provides the guarantee that removed documents will not contribute to later inferences without the huge cost of full retraining.

5.6. Catastrophic Forgetting

Finally, we compare AdapterSwap’s ability to recall past information with two alternative strategies. A naive approach to handling new streams of data is to iteratively fine-tune a LM as new data becomes available. This workflow can cause older information to be ‘overwritten’ within the architecture’s parameters. Alternatively, as new data is added, the model can be fine-tuned from scratch on all available data at once. While this mitigates some ‘forgetting’ it requires longer training times as models are discarded and retrained. We use News Crawl to evaluate both methods and compare to AdapterSwap. We iteratively measure performance on the first month of our dataset as we add subsequent months of data to our models. For this experiment we only use Falcon-7B as the baseline due to the increased computational demand required by the alternative approaches.

Figure 4 displays the performance of AdapterSwap compared to chronological fine-tuning and full retraining. Chronological fine-tuning quickly degrades as more data is presented to the model. Retraining performs better, but sufers from the fixed capacity of a single adapter. AdapterSwap maintains a static performance when recalling data from the first month as that adapter remains unchanged over time.

y 14 t i x lrep 12 e P 10

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5 10

Months of Training

6. Additional Related Work

6.1. Knowledge Editing

Recent work in knowledge editing of LLMs has considered approaches which either add additional parameters to a model, or directly edit existing parameters to update information [35]. Directly updating the existing parameters is attractive as it does not require any additional parameters, and updates can be applied whenever new knowledge is available [36, 37]. Knowledge vectors, in combination with hidden representations of specific entities, have also been proposed as a tool to update or remove knowledge [38]. In all cases, these approaches lack the ability to guarantee that any specific training example can be removed entirely from the model.

6.2. Retrieval Augmented Generation

Retrieval-Augmented Generation (RAG) has become a popular approach to incorporating new data into a pre-trained LLM without having to rely on retraining or fine-tuning [ 39, 40 ]. While it ofers some advantages, there remain challenges that can make deploying an efective RAG-based solution dificult. For example, RAG is limited by how much retrieved context can be employed based on the underlying LLM’s context window size. This contrasts with AdapterSwap where each adapter in our experiments represented more than 50 million tokens on average. Recent work has shown that all evidence in the context window is not treated equally, with models favoring evidence at the start and end of each window [ 41 ]. Further, RAG is fully dependent on the ability of a retriever model to locate all relevant documents without introducing too much noise into the context [ 42, 40, 43 ]. In addition, as transformers are quadratic in the number of tokens considered, then requiring forced decoding over retrieved content can add significant latency at inference time. While our solution avoids these issues, we note that AdapterSwap does not preclude the use of RAG, and a hybrid approach could be useful in some circumstances.

6.3. Federated Learning

Federated learning [ 44 ] also deals with learning from siloed data, typically aggregating gradients on local data before averaging into a global model. However, federated learning is intended to produce a single centralized model without mixing data silos and thus does not provide any mechanism for access control or deletion. Some work has combined federated learning and PEFT methods [ 45, 46, 47 ], but these do not address adapter mixing, deletion, or data silos as distinct knowledge sources. Furthermore, the privacy benefits of federated learning are unclear when applied to LLMs with large capacity for memorization [ 48, 49, 50, 51 ].

7. Conclusion

In this paper, we introduced AdapterSwap, a parameter eficient approach to continuous learning with access-control and data removal guarantees. We fine-tuned adapters using four modern pretrained language models on separate domains. We showed that knowledge from specific domains can be masked via access-control by preventing a retriever from accessing the controlled adapter at inference. The multiple-adapter scheme also enables eficient knowledge removal via data deletion and adapter retraining. The non-parametric behavior of AdapterSwap enables knowledge from the past to be retained, and we showed that AdapterSwap outperforms both chronological fine-tuning and retraining.

AdapterSwap enables a rich set of future research opportunities. We would like to directly improve the approach by exploring better retrieval and adapter mixing methods. Additionally, AdapterSwap could be further scaled to specific down-stream tasks such as question answering and directly compared or combined with alternative data management schemes such as a RAG framework. in: P. Koehn, L. Barrault, O. Bojar, F. Bougares, R. Chatterjee, M. R. Costa-jussà, C. Federmann, M. Fishel, A. Fraser, M. Freitag, Y. Graham, R. Grundkiewicz, P. Guzman, B. Haddow, M. Huck, A. Jimeno Yepes, T. Kocmi, A. Martins, M. Morishita, C. Monz, M. Nagata, T. Nakazawa, M. Negri, A. Névéol, M. Neves, M. Popel, M. Turchi, M. Zampieri (Eds.), Proceedings of the Seventh Conference on Machine Translation (WMT), Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Hybrid), 2022, pp. 1–45. URL: https://aclanthology.org/2022.wmt-1.1. [27] A. Liška, T. Kočiský, E. Gribovskaya, T. Terzi, E. Sezener, D. Agrawal, C. de Masson d’Autume, T. Scholtes, M. Zaheer, S. Young, E. G.-M. S. Austin, P. Blunsom, A. Lazaridou, Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models, arXiv preprint arXiv:2205.11388 (2022). [28] G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei, J. Launay, The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only, 2023. arXiv:2306.01116. [29] J. Banks, T. Warkentin, Gemma: Introducing new state-of-the-art open models, Google (2024).

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• We trained each adapter for our experiments on a single 80GB A100 GPU for 10 epochs with a batch size of 4 and 5 gradient accumulation steps. We utilized the AdamW optimizer with default settings .

• For adapters trained on C4 domains we used rank 64 LoRAs with = 128 applied to all linear layers .

• For the News Crawl experiment we used LoRA adapters with rank 32 and = 64 applied to just the attention layers .

• For all experiments we initialized adapters using a random seed of 42. We confirm [ 21]'s finding that using the same initialization is critical if mixing adapters .