Exploiting Presentative Feature Distributions for Parameter-Efficient Continual Learning of Large Language Models
Authors: Xin Cheng
Jiabo Ye
Haiyang Xu
Ming Yan
Ji Zhang
Feng Liu
Fei Huang
Lei Feng
Venue: ICML 2025 poster (2025)
Keywords: large language model
continual learning
low-rank adaptation
Primary area: deep_learning->large_language_models
TL;DR: We propose a novel CL method for LLMs that avoids a critical limitation called information leakage while maintaining outstanding CL performance.

Abstract: Endowing large language models (LLMs) with continual learning (CL) capacities is practically important, which enables them to dynamically acquire new knowledge over time. Although many effective methods have been proposed for CL of LLMs, they did not consider online scenarios, thereby sharing a common problem: information leakage (IL), where the task-related information of learned tasks is accessed or reused again. IL not only imposes potential risks on data privacy protection but also significantly hinders the deployment of LLMs in real-world scenarios. To avoid IL while maintaining outstanding CL performance, we propose a novel CL method for LLMs, which first characterizes a parameter-efficient fine-tuning (PEFT) block by a presentative feature distribution, and then dynamically selects the appropriate PEFT blocks for each instance based on its similarity with the presentative feature distributions. Extensive experiments validate the effectiveness of our method on the CL of LLM, showcasing its potential to enhance both privacy and adaptability in practical applications.

Exploiting Presentative Feature Distributions for Parameter-Effiieet Continual
Learning of Large Language Models
Xin Cheng 1* Jiabo Ye?
Haiyang Xu²
Ming Yan? 2 Ji Zhang?
Feng Liu Fei Huang? Lei Feng'
Abstract
2020; Ouyang et al., 2022). However, updates to LLMs typically involve adding new data and retraining the model, which is highly inefficient and inflexible. Therefore, en- dowing LLMs with continual learning (CL) (Lopez-Paz & Ranzato, 2017; Chaudhry et al., 2019; Wu et al., 2021; Ke & Liu, 2022; Wang al., 2023a; Chen al., 2023; Zhao et al., 2024; Wu al., 2024) capabilities is crucial for their deployment in real-world scenarios, which enables them to dynamically adapt to new tasks and acquire additional knowledge over time (Luo et al., 2023; Zhai et al., 2023).
Endowing large language models (LLMs) with continual learning (CL) capacities is practically important, which enables them to dynamically ac- quire new knowledge over time. Although many effective methods have been proposed for CL of LLMs, they did not consider online scenarios, thereby sharing common problem: information leakage (IL.,, where the task-related information of learned tasks is accessed or reused again. IL not only imposes potential risks on data privacy protection but also significantly hinders the de- ployment of LLMs real-world scenarios. To avoid IL while maintaining outstanding CL perfor- mance, we propose novel CL method for LLMs, which first characterizes parameter-efficien fine- tuning (PEFT) block by presentative feature distribution, and then dynamically selects the ap- propriate PEFT blocks for each instance based on its similarity with the presentative feature distri- butions. Extensive experiments validate the effec- tiveness of our method on the CL of L.LM, show- casing its potential to enhance both privacy and adaptability in practical applications. Our source code is available at https:/githubbcom/ZRRR- 921 We--OOnine-CL-LLM..
The vast number of parameters in LLMs imposes a signifi- cant computational burden on CL (Wu et al., 2024), render- ing traditional CL methods unsuitable for direct application. To avoid exorbitant training overhead, recent research has begun exploring CL for LLMs using parameter-efficient fine- tuning (PEFT) (Houlsby et al., 2019; Lester et al., 2021; Hu et al., 2021). A mainstream approach is parameter isolation- based methods (Rusu et al., 2016; Fernando et al., 2017: Zhao et al., 2024). These methods allocate a new PEFT block for each new task to capture task-specific knowledge, which ensures that the parameters learned by each task do not interfere with each other and are reorganized later. Al- though many effective CL. methods for LLMS using PEFT have been proposed, they are not designed for online learn- ing scenarios where tasks arrive in a stream, meaning that accessing or reusing task-related information (c.g.. training data and task identifiers) from previously learned tasks is not allowed. Consequently, previous methods share common problem: Information Leakage (IL) from previously learned tasks to downstream tasks
1. Introduction
In recent years, large language models (LLMss) (Brown et al., 2020; Raffel et al., 2020; Touvron et al., 2023; Achiam et al., 2023) have made remarkable progress in their ability ad- dress variety of problems. At the same time, LLMs need to be updated regularly to accurately reflect the evolving knowledge, needs, and values of humanity (Biesialska et al.,
Many CL methods (McCloskey & Cohen, 1989; Kirkpatrick et al., 2017; Kotha et al., 2024) simplify the CL setting to avoid forgetting by assuming that the task identity of test instances (i.e., which task they come from) is known during testing. However, in the real world, we typically have access only to model weights, and often it is unclear which tasks have been learned, let alone which tasks the test instances come from. Some other methods (Lopez-Paz & Ranzato, 2017; Shin et al., 2017: Isele & Cosgun, 2018; Yin et al.,
"Work done during internship at Alibaba Group. 'School of Computer Science and Engincering, Southeast University, China 2 "Alibaba Group 'TMLR Group, University of Melbourne. Corre- spondence to: Lei Feng <fenglei @ @ew..dd..cn...
2022: Scialom et al., 2022; Mok et al., 2023; Feng et al.,
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
2024) to mitigate catastrophic forgetting assume that the model can access and reuse the training data of learned

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Exploiting Presentative Feature Distributions for Parameter-Effiiice Continual Learning of Large Language Models
SuperNt Benchmark
Long Sequence Benchmark
Our contributions CL can be summarized follows:
Avoid IT.
SAPT
TaSL.
SAPT
TaSI.
We empirically found significant performance differences between previous CL methods with and without IL and identified the cause of this difference as the introduction of a new risk of forgetting during the selection process.
x
51.54
53.01
82,02
84.33
11.12
27.43
10.18
72.29
Gaps
-40.42
-25.58
-71.84
-12.04
We propose a simple and effective online CL method for L.Mss, which utilises the feature representation capabili- ties of pre-trained LLMs to characterize each PEFT block by encoding a presentative feature distribution.
Table I: The performance of the two best-performing CL methods (SAPT and TaSL) using the T5-Large model on
two CL benchmark (SuperNI Benchmark and Long Se- quence Benchmark). x indicates methods with II, while / signifies methods without IL.
Based on our proposed method, we provide an extension that is able to combine PEFT blocks and presentative feature distributions learned by our method using the same model architecture without additional training.
tasks, for example, by regularly replaying this data during training. These assumptions not only require overhead for storing data from learned tasks, but also necessitate periodic computational costs for replay, which pose scalability chal- lenges for CL. Furthermore, these assumptions also hinder the application of CL in scenarios involving data-sensitive or specialized tasks, where accessing data-related informa- tion is not allowed. In table 1, we show that even for the current sfate-of-the-ant CL methods, there is a performance gap ranging from about 12.04% to 71. 71.84% between with and without IL. Therefore, developing more effective online CL methods for LLMs avoiding IL is crucial for the safe and effective application and deployment of CL for LLMs.
Extensive experiments validate that our method avoids IL. while maintaining outstanding CL performance.
2. Background
2.1. Continual Learning
Let T = {{ y:)] be the target task with the size of n, and each example (æ;, yi) E X x y is assumed to be sampled from an unknown data distribution with probabil- ity density p(z, y). The goal of continual learning (CL) (Wu et al., 2024) is train a single model f adapt sequence of tasks (Ti, T2.... .2....Tk! Tk} that arrive in stream- ing fashion, where model can only access the k-th task To = {< yf ) in the k-th time step. Then, the opti- mal objective of CL is the following:
To address the problem of II. we propose novel on- line method exploiting presentative feature distributions for parameter-efffcient CL of LLMs. Our method first lever- ages the feature representation capability of well-developed pre-trained LLMs trained on large-scale data to encode data domain information of tasks into a presentative feature dis- tribution. This presentative feature distribution is used to characterize learned PEFT blocks relevant to the task. Then, we calculate the similarity between instances and the stored presentative feature distributions, using this similarity to dy- namically select the associated PEFT blocks. During train- ing, these blocks are treated as prior knowledge to learn new tasks, while during testing, the selected PEFT blocks will be combined to predict. At all times, our method only ac- cesses the high-ding -iggeniinnii and human inmmaaaenncomeheeniibbleee presentative feature distributions statistically derived from the pre-trained LLMs, hence it ensures that no information is leaked. Moreover, because the presentative feature dis- tribution only associated with the pre-trained LLMs and the related PEFT block, our method demonstrates strong expandability. It allows learned PEFT blocks and presenta- tive feature distributions on different tasks using the same model framework to be directly expanded without additional training. Without IL, our method achieves state-of -f-te-----he-rtt performance, approaching or even surpassing the perfor- mance of methods with II..
K max Ex
Erta yn) log logp(y"
(I)
Previous CL research posits that an effective CL method should address two primary challenges: 1) Catastrophic Forgetting (CF) (McCloskey & Cohen, 1989; Kirkpatrick et al., 2017: Kotha et al., 2024), where the performance on learned tasks significantly deteriorates when learning new tasks; and 2) Knowledge Transfer (KT), which includes both forward transfer and backward transfer. Forward transfer refers to the ability to leverage knowledge from learned tasks to achieve better performance on a new task. Backward transfer refers to the ability of learned tasks to benefit from the knowledge acquired while learning new tasks, thereby improving their performance.
2.2. Erramiter-Efficient Fine-Tuning
Pre-trained LLMs developed using large-scale pre- pre-training datasets have become powerful foundation for address- ing a variety of target tasks. When faced with a specific downstream task, pre-trained LLMs can be adapted to effec- tively solve the task through parameter-efficieet fine-tuning (PEFT) (Houlsby et al., 2019; Lester et al., 2021; Li & Liang,
2

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Exploiting Presentative Feature Distributions for Parameter-Efficien Continual Learning of Large Language Models
2021; He et al., 2021; Ding et al., 2022: Zaken et al., 2022). Low-Rank Adaptation (LoRA) (Hu al., 2021) is the most popular PEFT method. It assumes that when the pre-trained model is adapted to the new target task, the changes in model parameters reside within a low-rank space. Consequently, we only need to update the low-rank weights associated with the parameters during adaptation, eliminating the need to update all parameters. Specifically, for a pre-trained weight matrix W Routxin and input feature h(a:)) E R LoRA adds low-rank decomposition block BA for output Wh(ar) as follows:
W E Routxin as the weight matrix for layer I of model I and use A E Rddin and B E Routtd to represent the LoRA blocks learned for task Tx in layer L.
3.1. Revisiting the Causes of Performance Differences
For parameter-based isolation CL methods, the CL process can be divided into two stages: 1) Selection: choosing relevant PEFT blocks for the instance, and 2) Merging: combining the selected PEFT blocks. The state-of-the-art parameter- based isolation method, SAPT (Zhao et al., 2024), designs a shared attention framework to learn how to select and merge LoRA blocks. However, this shared attention framework introduces new trainable parameters, known as query projections, which may also introduce new for- getting problem. In Figures I.(a) and 1.(b), we show the heatmap of SAPT selecting LoRA blocks with and without IL. using query projection. We can observe that without II, SAPT extremely selects the LoRA of the last task for all task instances, indicating the occurrence of forgetting, which val- idates our hypothesis. Although SAPT uses reflection module to replay the -ampless in order to mitigate the new forgetting, this can also lead to II. To solve the prob- lem, we should avoid introducing new trainable parameters in the selection stage.
Wh(ar)= = Wh(z) + BAN()),
(2)
where A E Indxin, B E Routxd and the rank d << min (in, out). During weight updates, the pre-trained weight matrix W remains fixed, and only the LoRA block A and B are updated.
2.3. Continual Learning for LLMs with PEFT
Large language models (LLMs) with billions of parame- ters impose a significant computational burden on CL (Wu et al., 2024). To avoid exorbitant training overhead, recent research has begun exploring CL for LLMs using PEFT.
A key technique is knowledge ioolation-based method, which requires the model to learn new knowledge with- out modifying what has already been learned. Orthogonal Low-Rank Adaptation (O-LoRA) (Wang et al., 2023a) en- forces that the LoRA parameters learned for new tasks are orthogonal to the existing LoRA parameters. TaSL (Feng et al.. 2024) decomposes LoRA blocks into d sub-blocks and selectively updates these sub-blocks when learning new tasks to avoid conflicts with learned tasks.
Inspired by the technique of fine-tuning (Howard & Ruder, 2018; Kumar et al., 2022: Parthasarathy al., 2024), zero- shot (Radford et al., 2021; Kojima et al., 2022), in-context learning (Dong et al., 2022) and pre-training model initial- ization (Touvron et al., 2023; Dubey et al., 2024), where well-developed pre-trained LLMs are repurposed, we can similarly leverage pre-trained LLMs for selection without introducing new trainable parameters. Following this idea, we propose a novel online CL method, which leverages the feature representation capabilities of pre-trained LLMs to characterize each PEFT block by encoding a presentative feature distribution. This method mainly consists of three key components: Feature Distribution Module, Similarity Module, and Dynamic Selection Module. The overall archi- tecture of our proposed method is illustrated in Figure 2.
Another important technique is parameter isolation- based methods (Rusu et al., 2016; Fernando et al., 2017), which allocate a new PEFT block for each new task. SAPT (Zhao et al., 2024) designed shared attention framework to dy- namically learn and select LoRA blocks. However, this framework introduces new shared parameters (Query Pro- jection), which still poses a risk of forgetting. To address this issue, Zhao et al. (2024) further proposed data replay method using pseudo-samples.
3.2. Feature Distribution Module
We assume that pre-trained LLMs trained on a large-scale general dataset already possess robust feature representation capability, which has been demonstrated in zero-shot, few- shot, and in-context learning. Therefore, the feature space of the pre-trained LLMs itself can be utilized to distinguish and categorize different tasks and knowledge, such that we do not need to train a new classifier. Specifically. we denote by Dj the presentative feature distribution of task Ty at layer I, and Dj is computed as follows:
3. Proposed Method
In this section, we first reflect on why previous CL methods show performance differences with and without IL. We then propose a novel and expandable method that avoids IL while maintaining outstanding CL performance.
Notations. In this paper, we use LoRA fine-tuning as our main PEFT implementation. For a given pre- pre-trd
model f and a target task T = ((af, y ? we denote
D/_ Epiee .t)) (W")(
(3)
3

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Exploiting Presentative Feature Distributions for Parameter-Efficinn Continual Learning of Large Language Models
be81572
ba81250
6ak1520
tok3729
bck1550
(a) SAPT without Data Leakage
(b) SAPT with Data Leakage
(c) Ours with L2 Distance
(d) Ours with Dot Product Similarity
The heatmap demonstrates the selection of LoRA on the SuperNI Benchmark across 15 tasks. Figures (a) and (b) illustrate the results of the wate-of-theean parameter- based isolation CL method, SAPT, with and without IL, utilizing the query projection for selecting LoRA, Figures (c) and (d) display the results of our proposed method using 1.2 distance and dot product similarity for selecting LoRA (averaged across all model layers). For all results, we scaled the values by factor of 100 and highlighted the highest value in each row with a yellow box.
Figure 1:
where h (x*h)) denotes the hidden state input of instance x in layer The presentative feature distribution DJ, primarily depends on the weight matrix W of the pre-trained LLMs and the data distribution p(z*, )²). Since the weight matrix w is frozen during LoRA fine-tuning, the presentative fea- ture distribution Dí varies only with the data distribution, thereby being regarded as a mapping of the data distribution by the pre-trained LLMs. Additionally, based on parameter- based isolation techniques, we allocate new LoRA block for each streaming arrival task. Consequently, the presenta- tive feature distribution can also be used to characterize the corresponding LoRA block.
put for instance x of k-th task in layer / by normalizing the similarities of instance x with existing LoRA blocks (B A1, Bi,A} ... A.).). The output (x) of in- stance æ at layer is given as follows:
exp(0(x, Di)/T) ()), (6)
W')((a) + Lum(cxp(#//1)
where ( = = (x)( D/)/T) and T is the temperature coefficient used to control normalization. During the training for the k-th task Te. only the LoRA blocks A B and presentative feature distribution D associated with the current task Th are updated, while the other parameters (learned task-specific LoRA blocks A). B/ and presentative feature distributions D and the pre- trained model matrix W remain frozen. It is worth noting that the update of the presentative feature distribution D is accomplished statistically as described in Eq. 3, without introducing any additional trainable parameters, thus not introducing a new forgetting problem. Additionally, the dynamic selection module can selectively activate some LoRA blocks rather than activating all of them, as shown in the Top-K selection method below:
3.3. Similarity Module
For reliable presentative feature distribution Dj and arbitrary instance æ, if æ is related (e.g., instruction or knowledge) to task Te, then the feature of x at layer I (i.e., (x**()))) will be close to Dj_ that is, with greater simi- larity. Building on this idea, we denote the similarity be- tween instance x and presentative feature distribution D as (xx D(.). In this paper, we explore the following two classic methods for computing the similarity:
Negative L2 Euclidean Distance:
exp (x), B B)))
$ (z. D)) WW)()>) D,1;;
(4)
Sum (ex((epppp)/)e /T), Top-K)
Dot Product Similarity:
W')(x) DI_
(5) where Sum(exp(0//T (T), Top-K) = En X ("), exp (2(x, Dz)/T). Selective activation can filter out some ir- relevant LoRA blocks and focus on the most relevant ones. We will discuss the selection of Top-K in the following experiments section.
De D()
Vout!
where out' donates the output dimension of layer 1.
3.4. Dynamic Selection Module
Through feature distribution, similarity, and dynamic selec- tion modules, our method achieves online CL for LLMs, effectively avoiding II. while addressing two key challenges
Using the feature distribution and similarity module, we can easily obtain the dynamically selected and merged out-
4

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Exploiting Presentative Feature Distributions for Enrameter-Efficiee Continual Learning of Large Language Models
Parametric Unit
Frozen Parameter
Statistical Parameter
Task
3
Calculation
Trainable Parameter
Calculated Parameter
Large Language Model
Output Sequence Features
Expansion
Transformer Block
Similarity
Dynamic Selection
Transformer Block
Transformer Block
Combination
LoRA Blocks
Feature Distribution
Weight Matrix
Transformer Block
LoRA Blocks
LoRA Blocks
Input Sequence of Features
D
Transformer Block
Festure Distributions
Feature Distributions
The overall architecture of proposed method. We assume that tasks and 2 have already been learned, and are currently learning task 3. The central part of the figure illustrates the continual learning process of our proposed method: 1) The features of the input instances first pass through the frozen weight matrix of the pre-trained LLMs to obtain hidden states, which are then used to update the presentative feature distribution relevant to Task 3; 2) The resulting hidden states are compared with existing feature distributions to calculate similarity; 3) Based on the calculated similarity, LoRA blocks are dynamically selected and output obtained. The rightmost part of the figure shows the expansion of LoRA blocks and feature distributions across different trained models with the same architecture.
Figure 2:
that affect CL performance. 1) IL: During both training and testing, our method only accesses the presentative feature distributions and LoRA blocks, completely avoiding access to any data information from learned tasks, including pseudo or synthesized data. Furthermore, the presentative feature distributions are high-dimensional and incomprehensible to humans. Moreover, presentative feature distributions rep- resent averaged information across populations and cannot reconstruct individual information, ensuring that no related information is exposed. 2) CL: our method is a parame- ter isolation-bart based method. It ensures that task-relevant knowledge is fully retained while dynamically selecting ap- propriate knowledge prevent forgetting. Additionally, our method does introduce any new trainable parameters, as the presentative feature distribution is derived statisti- cally, thereby avoiding new forgetting issues. 3) KT: For forward transfer, when learning a new task, our method can effectively learn the new task by selecting relevant learned task knowledge as prior knowledge through the dynamic selection module. During testing. our method compares the features of instances with the presentative feature distribu- tions to extract knowledge relevant the test instance. This enables learned tasks benefit from the knowledge gained in new tasks, achieving backward transfer.
3.5. Flexible Expansion Advantage
Our proposed method characterizes each LoRA block with the presentative feature distribution based on the robust feature representation capability of the well-developed pre- trained LLMs, retaining only the presentative feature distri- butions and the LoRA blocks. Because the feature represen- tation capability is similar for identical pre-trained LLMs. This allows for the expansion of any trained presentative feature distributions and LoRA blocks using the same model architecture on our proposed method simply by putting them together, regardless of who carried out the training. Notably, this expansion requires no additional training, meaning that the model gains associated knowledge simply by integrating the feature distributions and LoRA blocks. This expansion not only achieves plug-in continual learning but also pro- motes the development of the open-source community. We illustrate this flexible expansion in Figure 2, and we will further validate the effectiveness of the expansion in the subsequent experiments section.
4. Experiments
In this section, we provide extensive experiments demon- strate the effectiveness of our proposed method.
5

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Exploiting Presentative Feature Distributions for Earaccter-Effiiient Continual Learning of Large Language Models
Table 2:
The test performance (%) on two continual learning benchmarks using the LLaMA-2-7B model. x indicates methods with IL, while signifies methods without IL. All evaluation metrics are reported after the training on the last task of the benchmarks. In methods without II., the best performances are highlighted bold. The last column Avg. the average of the performance on the two benchmarks.
SuperNI Benchmark F.Ral
Long Sequence Benchmark
Methods
Avg.
APT
FWTT
BWTt
APT
F.Ray
FWT1
BWT1
Replay
X
39.48
14.86
0.19
-26.47
71.43
13.64
0.97
-12.73
55.46
ProgPrompt
X
40.54
0.05
-14.18
0.0
70.64
0.0
-14.72
0.0
55,59
SAPT-LoRA
x
56.23
1.07
0.81
-0.65
81.75
2.81
1.09
-2.53
68.99
SeqLoRA
28.65
28.21
-0.18
-27.73
26.80
60.53
-2.11
-60.48
27.73
ProgPrompt
14.34
28.12
-14.18
-28.07
10.67
64.25
-14.72
-64.25
12.51
LFPT5
38.71
16.81
0.32
-15.42
70.31
5.63
0.51
-4.32
54.51
EPI
72.27
5.04
-3.12
-0.50
O-LoRA
37.17
19.01
0.08
-8.88
54.47
26.65
-3.03
-26.63
45,82
SAPT-LoRA
30.28
26.97
0.74
-26977
7.08
82.15
0.05
-82.12
18.68
TASL-LoRA
42.00
75.00
58,50
Ours-Dot
54.10
1.47
0.04
-0.66
81.13
3.11
-0.77
-2.86
67.62
Ours-I.2
55.60
0.23
0.08
-0.14
83.01
0.57
-1.07
-0.41
69.31
4.1. Experimental Setup
(Chaudhry et al., 2018) measures how much knowledge has been forgotten across the stream of incoming tasks, i.e., K- Ej=1 - (max K
We conducted experiments on two CL bench- marks, including SuperNI Benchmark (Wang et al., 2022a) and Long Sequence Benchmark (Razdaibiedina et al., 2023).
Datasets.
q=j Owi - j Forward Transfer (FWT) (Lopez-Paz Ranzato, 2017) evaluates how much knowledge from previous tasks transfers to new task, i.e.. Σド= ajj agj. where any refers to the performance of training task j individually; Backward Transfer (BWT) (Ke & Liu, 2022) measures how much learned tasks benefit from newly learned tasks, i.e., R-. Ei akj Ajj-
The SuperNI Benchmark is a comprehensive benchmark of various NLP tasks, with instructions crafted by experts. It encompasses tasks like dialogue generation, information extraction, question answering, summarization, and senti- ment analysis, and is used to evaluate the performance of the model in general scenarios. Following previous CL set- tings (Zhao et al., 2024), we selected three tasks from each category, creating a total of 15 task sequences for evaluation. For training, 1,000 instances are randomly sampled from the dataset for each task, with an additional 100 instances selected for validation and testing.
Compared Methods. We evaluate our method alongside the following PEFT-based CL methods: 1) Replay mitigates forgetting by periodically replaying samples from previ- ously learned tasks, but it has the risk of IL; 2) SeqLoRA sequentially trains the LoRA on the task orders. 3) LFPTS (Wang et al., 2022b) continuously trains a soft prompt for each task using generative replay and an auxiliary loss; 4) ProgPrompt (Razdaibiedina al., 2023) trains sequence of prompts in the order tasks arrive. Assumes the task-related ID is known during testing, allowing for direct selection of the corresponding prompt for evaluation. We evaluated the performance of ProgPromp with and without II. 5) EPI (Wang et al., 2023b) trains a prompt for each task and se- lects the prompt based on the distance between the input and the distributions of different classifier task labels; O- LoRA (Wang et al., 2023a) ensures the newly learned LoRA components for each task are orthogonal to the previously learned ones to prevent interference with already acquired knowledge. 7) SAPT (Zhao et al., 2024) uses an attentive reflection module and shared attention framework to adjust PEFT block learning and selection. However, the attentive
The Long Sequence Benchmark is CL benchmark compris- ing 15 classification tasks. Following previous CL settings (Razdaibiedina al., 2023; Wang et al., 2023a), we selected 1,000 random samples for training in each task and reserved 500 samples per category for validation and testing.
Evaluation Metries. We denote the testing performance on the j-th task after training on the i-th task as aij, us- ing Accuracy for classification tasks and Rouge-L (Lin, 2004) for other tasks. Following previous CL studies, we use widely adopted CL evaluation metrics: Average Per- formance (AP) (Chaudhry et al., 2018) evaluates the av- erage performance of all tasks after training on the last task, i... 1 Σ akj. which is the most important met- ric for evaluating CL methods; Forgetting Rate (F.Ra)
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Exploiting Presentative Feature Distributions for Errameter-Effcienn Continual Learning of Large Language Models
Table 3:
The test performance (%) on two continual learning benchmarks using the LLaMA-2-13B model. x indicates methods with IL, while v/ signifies methods without IL. All evaluation metrics are reported after the training on the last task of the benchmarks. In methods without II., the best performances are highlighted bold. The last column Avg. the average of the performance on the two benchmarks.
SuperNI Benchmark F.Ral
Long Sequence Benchmark
Methods
Avg.1
APT
FWT1
BWTT
APt
F.Ral.
FWTT
BWT1
Replay
x 43.99
11.64
0.72
-9.75
76.63
7.92
0.02
14.86
60.31
ProgPrompt
x 38.93
0.35
-17.35
0.0
72.43
0.0
-14.99
0.0
55.68
SAPT-LORA
x
56.95
1.39
0.81
-0.56
82.32
1.98
0.78
-1.57
69.64
SeqLoRA
30.07
28.50
-0.03
-28.06
50.76
35.96
-2.65
-35.80
40.42
ProgPrompt
11.23
30.03
-17.35
-29.68
8.08
68.95
-14.39
-68.95
9.66
LFPT5
41.26
14.67
-0.52
-12.31
71.61
6.51
-1.34
-3.78
56.44
EPI
76.66
4.91
-0.09
-1.03
O-LoRA
44.49
12.28
-0.65
-11.94
57.34
28.23
-3.13
-28.22
50.92
SAPT-LORA
35.53
23.00
0.66
-22.93
31.37
57.72
-0.05
-57.72
33.45
Ours-Dot
55.28
2.54
0.65
-1.77
82.83
2.35
-1.32
-2.11
69.06
Ours-L2
56.47
0.27
0.05
0.16
84.75
0.39
-0.98
-0.34
70.61
reflection module poses risk of IL. 8) TaSL (Feng et al., 2024) identifies task-related areas through grouped skill lo- calization and consolidates them to prevent forgetting. More complete experiments such as different models, different task orders and visualisations are provided in Appendix C.
for LLMs with PEFT (LoRA: SAPT-LoRA, SeqLoRA, O- LoRA, TASL; Prompt: ProgPrompt, LFPT5, EPI) on two popular CL benchmarks. We use the Top-1 strategy to select LoRA blocks. can be observed that our method significantly outperforms other basclines without IL. For instance, in the SuperNI Benchmark, Ours-Dos and Ours- L2 achieve average performances of 54.10% and 55.60%, respectively, which are approximately 12.85% higher than the best method without IL, TaSL. Furthermore, compared to methods with IL, our proposed method achieves similar or even better CL performance. In the SuperNI Benchmark, our method only 0.63% lower than the SOTA CL method with IL, SAPT-LoRA, while in the Long Sequence Bench- mark, method surpasses it by 1.26%. Moreover, in the comprehensive evaluation across the two benchmarks, our method establishes a new SOTA with 69.31% average per- formance. These results validate the effectiveness of our method for CL of LLMs. By comparing the experimen- results in Table 2 and Table 3, can be observed that the average performances of Ours-Dot and Ours-L22 using I.LaMA--133 are 69.06% and 70.61 %, respectively, both higher than the 67. 62%%%% and 69.31% % achieved using LLaMA- 2-7B. This indicates that more powerful models can achieve stronger CL. performance.
Following previous CL settings (Zhao et al., 2024), all methods performed with instruc- tion tuning (Wei et al., 2021; Ouyang et al., 2022) to lever- age the task instruction provided in the two benchmarks. To validate the effectiveness of our proposed method, we considered pre-trained LLMs with two architectures: the encoder-decoder Ta-large and the decoder-only LLaMA- 2-7B and LLaMA-2-133 models, with model parameters ranging from 770M to 13B. To ensure fair comparison, use LoRA fine-tuning train each task individually auk for all methods. For each transformer layer, we only count the feature distributions of Q and V components. For the temperature coefficient in the softmax function, we set all to 1.0 following previous studies (Zhao et al., 2024). For Top-K activation in the dynamic selection module, we set K In Table 2 and Table while for the remainder of the experiments, K was set to the total number of tasks. It is important to note that the values of K during training and testing do not have to be identical. For simplicity, we chose to keep them the same. Detailed implementation details you can find in the Appendix
Implementation Details.
Performance of Zero-shot Unseen Tasks. Following previous studies (Zhao et al., 2024), we further select 3 tasks from five types of problems (Dialog, IE, QA, Sum, and SA) to evaluate the cross-task generalization ability of CL methods. Detailed information on task selection can be found in Appendix A.1. In Table 4, we can see the performance of our method and some CL. methods on unseen
4.2. Experimental Results
Performance on Continual Learning Benchmarks. Ta- ble and Tabel show the comparison results of our proposed method and recent continual learning methods

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Exploiting Presentative Feature Distributions for Parameter-Efficint Continual Learning of Large Language Models
Table 4: The test performance (%) on the unseen task using the LLaMA-2-7B and LLaMA-2-13B model. x indicates methods with IL, while v signifies methods without IL. The best performances are highlighted in bold.
LLaMA-2-7B
LLaMA-2-13R
Methods
Dialog
IE
QA
Sum
SA
Avg.
Dialog
IE
QA
Sum
SA
Avg.
ProgPrompt X/V
7.16
6.65
17.69
9.86
4.34
9.14
7.92
6.36
16.91
14.64
9.0
10.97
SAPT-LoRA
x
12.22
31.70
41.35
15.65
63.24
32.84
12.32
29.24
41.36
15.53
67.57
33.20
SAPT-LORA
12.42
34.31
49.11
18.24
58.33
34.48
11.84
35.89
47.52
19.80
72.16
37.45
SeqLoRA
4.44
40.78
36.10
10.50
57.98
29.96
6.58
35.84
40.44
7.81
41.07
26.35
O-LoRA
7.43
46.71
28.65
11.22
60.31
30.87
6.46
38.39
57.75
14.30
47.82
32.95
Ours-Dot
9.52
40.55
49.94
21.32
59.57
36.18
12.37
32.91
55.27
20.32
65.31
37.24
Ours-L2
11.54
53.50
57.82
21.46
66.99
42.26
12.00
45.76
61.31
23.20
71.25
42.70
SuperNi Benchmark
SuperNi Benchmark
Long Sequence Benchmark
Long Sequence Benchmark
Average Performance (AP)
12
Performance (AP)
Forgetting Rate (F.Ra)
LLaMA22
Forgetting Rate (F.Ra)
LLaMA
$6
10-
LLaMA-2-138
80
25
LLaMA-2-138
54-
75
20-
52-
15
50
Average
65
LLa MA-2-78
60-
LLaMA-2-78
5-
LLM3422133
LLaMA-2-138
1500110 20%
53%
200%
340ha5% 23%
180%
20h
100%
330p4es 18%
53%
102%
Scale of Model Layers Used
Scale of Model Layers Used
Scale of Model Layers Used
Scale of Model Layers Used
Figure 3: The performance (average performance t and forgetting rate 1) of our method for the LLaMA-2-7B and LLaMA- 2-13B on SuperNI Benchmark and Long Sequence Benchmark as the number of model layers being used increases. Since the number of layers in different models varies, the scales not strictly aligned.
tasks using LLaMA-2-7B and LLaMA-2-130 According to these experimental results, we observe that our method achieves higher performance compared to other methods, whether using dot product similarity or L2 distance. This attributed to the fact that our method uses a dynamic selection of modules choose the appropriate knowledge (i.e., LoRA blocks) for each test instance. These results validate that our method possesses strong generalization capabilities to handle more realistic scenarios.
block) even with all layers, compared to 0.04% for SAPT.
Comparison of Similarity Calculation. In Figure 4, we present the results of two similarity calculation methods (1.2 distance and dot product similarity) under different dynamic selection strategies (selecting the top-k most relevant). We observe that L2 distance consistently outperforms dot prod- uet similarity in terms of both performance and stability. This may be because the results of the dot product similarity are too close to each other. As shown in the visualization in Figure 1.(c) and 1.(d), while the dot product can iden- tify the most relevant LoRA block, its values are relatively similar. As result, the performance of the dot product sim- ilarity improves noticcably top-k decreases. Moreover, our method surpasses the best baseline without DL in most cases, further validating the effectiveness of our method.
Performance of Model Layer Usage. For LoRA-basee methods, we add LoRA blocks the Query and Key com- ponents of the attention module at each layer. Consequently, our method requires analysis of the feature distribution in the Query and Key components at every layer. In Figure 3, we present the performance changes of our method, equipped with L2 distance, as the number of utilized layers increases. can be observed that as more layers of the model are adopted, both the average performance and forgetting rate improve. Moreover, good results can be achieved with only small number of layers being used. This indicates the potential of leveraging pre-trained models for feature ex- traction and validates the effectiveness of our method. In addition, our method only needs to increase the parameters for the LLAMA-2-7B model by 0.03% (including the LoRA
Performance of Expansion. In Table 5, we present the results of expansion feature distributions and LoRA blocks trained on two benchmarks. It is evident that models trained a single benchmark can acquire knowledge when inte- grated with the presentative feature distributions and LoRA blocks trained on another benchmark, thanks to our dynamic selection module. Additionally, the combined performance of non-continual training is slightly worse than that of con-
8

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Exploiting Presentative Feature Distributions for Errameter-Effiienn Continual Learning of Large Language Models
Table 5: The test performance (%) on two continual learning benchmarks using the LLaMA-2-7B model and LLaMA-2- 13B model with different LoRA blocks and feature distributions. "SuperNT" and "Long Sequence" indicate the average performance on the benchmarks using our method, where "+" denotes the expansion of LoRA blocks and presentative feature distributions introduced in Section 3.5. "Continual" refers to each task within the benchmark being trained in a continual learning manner. "Non-Continual" refers to each task within the benchmark being trained independently rather than in continual learning manner.
LLaMA-2-7B Long Sequence
LLaMA-2-133B Long Sequene
LoRAs and Distributions
SuperNT
Avg.
SuperNI
Avg.
Continual SuperNT
54.33
29.41
41.87
56.52
31.93
44.23
+ Non-Contin Continual Long Sequence
53.70 (-0.63)
72.78 (+43.37)
63.24 (+21.77)
55.78 (-0.74) 78.10 (+46.17) 66.94 (+22.71)
Continual Long Sequence
28.75
79.82
54.29
37.09
82.53
59.81
+ Mon-Continual SuperNI
52.12 (+23.37)
78.49 (-1.33)
65.31 (+11.02) 54.88
(+17.79)
81.73
(-0.80)
68.31 (+8.50)
Continual SuperNT Long Sequence
53.47
78.30
65.89
55.34
81.72
68.53
distance
with Dot product similarity
Best baseline
baseline without
SuperNI Benchmark
Long Sequence Benchmark
SuperNI Benchmark
Long Sequence Benchmark
Average Performance (AP)
Average Performance (AP)
Average Performance (AP)
Average Performance (AP)
80
36
82.5
54
52
48
20
75.0
65
04
70.0
67.5
15
Top-K Dynamic Selection
Top-K of Dynamic Selection
Top-K Dynamic Selection
Top-K of Dynamic Selection
Figure 4: The performance (average performance 1) of our method for the LLaMA-2-7B and LLaMA-2-13B on SuperNI
Benchmark and Long Sequence Benchmark varies with the top-k selections of the dynamic selection module.
tinual training on two benchmarks separately and then com- bined. This is because the LoRA blocks trained indepen- dently do not have the capacity to utilize prior knowledge, which also validates the effectiveness of our method.
directly without additional training, achieving plug-in CL for LLMs. Our method avoids IL while achieving outstand- ing CL performance. We hope that our in-depth study on IL in CL will inspire further research to consider this important problem, thereby facilitating the better deployment of LLMs with CL capabilities in real-world online scenarios.
5. Conclusion
In this paper, studied a critical problem in continual learning (CL) for large language models (LLMs) called Information Leakage (IL), which not only imposes poten- tial risks on data privacy protection but also significantly hinders the deployment of LLMs in real-world online sce- narios. In order to solve this problem, we first reconsidered the reasons for performance differences with and without IL in previous CL methods, mainly introducing new learn- able parameters that brought about forgetting. Second, utilized well-developed pre-trained LLMs trained scale data and proposed a novel online method exploit- ing presentative feature distributions for ceaumeter-efficienn continual learning of large language models. Finally, we provided an expansion to our method that allows LoRA blocks and presentative feature distributions using the same model architecture trained on different tasks to be integrated
A Acknowledgements
This research work is supported by the Big Data Computing Center of Southeast University. Xin Cheng was also sup- ported by Alibaba Group through Research Intern Program. Feng Liu is supported by the Australian Research Council (ARC) with grant number DE240101089, LP240100101, DP230101 and the NSF&CSIRO Responsible AI pro- gram with grant number 2303037.
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.

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Exploiting Presentative Feature Distributions for Parameter-Effiiien Continual Learning of Large Language Models
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Exploiting Presentative Feature Distributions for Earimettr-Eficient Continual Learning of Large Language Models
A. Details of Benchmark
A.I. SuperNI Benchmark
The Instrrc-Insstucttoons project (Mishra et al., 2022; Wang et al., 2022a) is high-quality NLP benchmark designed to assess a ability of model to generalize unseen tasks. This generalization relies on understanding and reasoning based on natural language instructions, which describe task clearly and comprehensively. A model capable of "understanding" language instructions can successfully tackle any unseen task once provided with the task instructions. Following previous setting (Zhao et al., 2024), we selected three tasks from each of five categories within the Natural-Instruutions project: dialogue generation (Dialog) (Zhang, 2018; Zang et al., 2020; Peskov & Cheng, 2020), information extraction (IE) (Santus et al., 2015; Nye et al., 2018; Mostafazadeh et al., 2020), question answering (QA) (Dasigi et al., 2019; Talmor et al., 2018) 8), summarization (Sum) (Narayan et al., 2018; Gliwa et al., 2019; Kim et al., 2019), and sentiment analysis (SA) (Socher et al., 2013; Saravia et al., 2018), creating a total 15 tasks. For each task, we randomly selected 1,000 instances training and 100 instances for evaluation and testing. In Table we show the details of the 15 tasks that are selected. In addition, we show the details of several different streaming orders in table 7, and all experiments in the main text use Order 1.
Dataset name
Task
Metric
task639_muile
dialogue generation
Rouge-L
lask I 150550000000 htwpommaaceeeet..eertttiiii
dialogue generation
Rouge-L
dialogue generation
Rouge-L
takkiloooutooneeeetttaiiion
information extraction
Rouge-L
information extraction
Rouge-L
information extraction
Rouge-L
tas.002.quoref..sssec..eerrrrooo
question answering
Rouge-L
task0077..cmmmoossssqqq....aav generatiin
question answering
Rouge-L
task599_scig..nswereeennnna
question answering
Rouge-L
summarization summarization summarization
Rouge-L
xsum summarization
Rouge-L
faskl5772.amsum_sunnna
Rouge-L
2ask363_sst2.polarii htt.:/oooartyccassfficaaaii classification
sentiment analysis sentiment analysis
accuracy
task875 emotion classification
accuracy
task mask1687_sentiment/ otsos///eetttteee4440osssssatoot classsifiiia
sentiment analysis
accuracy
Table 6: The details of selected 15 datasets in the SuperNI Benchmark
Order
Task Sequence
task 1572 task363 task 1290 - task181 task002
task1510 + task639 + task + task073 - task 1590 +
task748 - task511 > task591 > task1687 > task875
t48 >> task073 > task 159 > task639 -> task 1572
2
task 687 + task591 + task363 - task 1510 - task 729 -
task 181 + task511 + task002 + task 290
120 + task875
Table 7: The streaming orders of selected 15 datasets in the SuperNI Benchmark
In order to evaluate the performance of the learned model terms of cross-task generalisation ability on the unseen task, we also selected three more tasks from the previous five types of problems; Dialog (Wei et al., 2018; Cho May, 2020:
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Exploiting Presentative Feature Distributions for Parameter-Effiiien Continual Learning of Large Language Models
Aliannejadi al., 2021), IE (Schmitz et al., 2012; Zlabinger al., 2020; Nan et al., 2020), QA (Levy al., 2017; Zhang al., 2018; Min al., 2020), Sum (Henderson et al., 2014; Syed et al., 2020; Hasan al., 2021), and SA (Sheng & Uthus, 2020; Lowphansirikul et al., 2020) for evaluation. The details of the selected unseen tasks are provided in table 8.
Dataset name
Task
Metric
lask360 1el33600ppllin.yesadee hrppliinnyesanddrssposss... response-generaut task574 aalaaairddiaaa dialogue sentence. generation
Dialogue Generation
Rouge-L
Dialogue Generation
Rouge-L
lask1714 convai3 Sentennceeeeeeneeeeeeeeee generation
Dialogue Generation
Rouge-L
task180 intervention extraction
Information Extraction
Rouge-L
task678 www.oc/attaalla.aaoooooaaa...lla actual relationsii celaaionssipp.nsserr..oo ausser-ggneratle
Information Extraction
Rouge-L
task1410 dart relationship extraction
Information Extraction
Rouge-L
task339 record answer gewerrtinnnttonnn
Question Answering
Rouge-L
task670 auusiqquuuusttinnnnnnmmomo. generation
Question Answering
Rouge-L
task task qa zre answer generation from question
Question Answering
Rouge-L
Summarization
Rouge-L
task1356 xlsum title generation
Summarization
Rouge-L.
task1499 mask1499_sstc3.co summarrizatin
Summarization
Rouge-L
lask421 peeeeeeesseeeeeeeeeeeeeeeeeseeee sentence sentiment classification task833 poem sentiment classification
Sentiment Analysis
Accuracy
Sentiment Analysis
Accuracy
task929 prwducts www.eeeiiweccm wwwviews.classifiaation
Sentiment Analysis
Accuracy
Table 8: The details of 15 unseen datasets in the SuperNI Benchmark
A.2. Long Sequence Benchmark
The Long Sequence benchmark is benchmark constructed by selecting five tasks (AG News, Amazon reviews, Yclp reviews, DBpedia and Yahoo Answers) from the standard CL benchmark (Zhang al., 2015), four tasks (MNLI, QQP, RTE, SST2) from the GLUE benchmark (Wang, 2018), five tasks (WiC, CB, COPA, MultiRC, BoolQ) from the SuperGLUE benchmark (Wang et al., 2019) and the IMDB movie reviews dataset (Maas al., 2011).
Dataset name
Category
Task
Domain
Metric
Yelp
CL Benchmark
Sentiment Analysis
Yelp reviews
Accuracy
Amazon
CL Benchmark
Sentiment Analysis
Amazon reviews
Accuracy
DBpedia
CL Benchmark
Topic Classification
Wikipedia
Accuracy
Yahoo
CL. Benchmark
Topic Classification
Yahoo Q&A
Accuracy
AG News
CL Benchmark
Topic Classification
News
Accuracy
MNLI
GLUE
Natural Language Inference
Various
Accuracy
QQP
GLUE
Paragraph Detection
Quora
Accuracy
RTE
GLUE
Natural Language Inference
News, Wikipedia
Accuracy
SST-2
GLUE
Sentiment Analysis
Movie Reviews
Accuracy
WiC
SuperGLUE
Word Sense Disambiguation
Lexical Databases
Accuracy
CB
SuperGLUE
Natural Language Inference
Various
Accuracy
COPA
SuperGLUE
Question and Answering
Blogs, Encyclopedia
Accuracy
BooiQA
SuperGLUE
Boolean Question and Answering
Wikipedia
Accuracy
MultRC
SuperGLUE
Question and Answering
Various
Accuracy
IMDB
SuperGLUE
Sentiment Analysis
Movie Reviews
Accuracy
Table 9: The details of selected 15 datasets in the Long Sequence Benchmark
To evaluate the stability of the method under different task orders, we provide two task orders in Table 10.
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Exploiting Presentative Feature Distributions for Fariieter-Efficinnt Continual Learning of Large Language Models
Order
Task Sequence
mnli + cb - wic - copa > qpp - boolqa - rtc + imdb -
yelp - amazon - sst-2 - dbpedia - ag - multire + yahoo
yelp + amazon + mnli + cb - copa + qqp + rtc imdb
sst-2 -> dbpedia -> ag > yahoo - multire -> boolqa - wic
Table 10: The streaming orders of selected 15 datasets in the SuperN1 Benchmark
LLaMA-2-7B
LLaMA-2-13B
Top-K
APt
BWTt
E.Ral
FWT1
APT
FRal
FWT1
BWT1
Dot
80.04
4.43
0.14
-4.16
82.72
2.47
-0.56
-2.14
1
1.2
83.13
0.42
-0.32
-0.35
84.59
0.36
-1.93
-0.32
Dot
75.22
9.10
-1.84
-8.89
79.91
4.98
-2.66
-4.76
3
L2
81.94
1.87
-0.40
-1.54
84.21
0.65
-2.05
-5.88
Dot
68.60
16.22
-1.82
-16.01
75.28
9.73
-2.56
-9.62
5
L.2
81.47
2.37
-1.84
-2.19
83.45
1.68
-2.05
-1.41
Dot L2
66.69
18.01
-1.98
-17.87
73.00
12.74
-2.52
-12.11
7
80.61
3.32
-1.82
-3.13
82.91
2.24
-2.15
-1.87
Dot L2
64.35
20.55
-1.95
-20.42
69.97
15.52
-2.48
15.38
9
79.92
4.05
-1.84
-3.85
82.97
1.72
-2.34
-1.62
Dot
61.72
23.46
-1.90
-23.28
69.00
16.74
-2.38
-16.54
11
79.91
4.13
-1.80
-3.91
82.87
2.29
-1.94
-2.16
Dot L2
60.81
24.40
-1.92
24.24
67.76
18.09
-2.36
-17.89
13
79.46
4.63
-1.79
-4.40
82.58
2.33
-2.28
-2.10
Dot
59.90
25.33
-0.42
-25.13
67.74
17.85
-1.01
-17.71
15
L2
79.77
4.08
-0.42
-3.84
82.51
2.52
-2.17
2.29
Table 11: The test performance (%) the Long Sequence Benchmark using the LLaMA-2-7B and LLaMA-2-13B model with different top-k selections of the dynamic selection module. All evaluation metrics reported after the training on the last task of the benchmarks.
B. Implementation Details
For our proposed method, we utilize the Adam W optimizer to train the models. Specifically, we employ a learning rate of 3e-4 for the T5-Large model, and 5e-5 for both the LLaMA-2-7B and LLaMA-2-133 models. For each transformer layer, only count the feature distributions of Q and V components, For the T5-Large model, only consider the statistical encoder.
The T5- Larger model is trained on single NVIDIA Tesla A100 GPU, with batch size of 32 and low-rank parameter set to 8. We train the model 100 epochs on the SuperNI benchmark and 10 epochs on the Long Sequence benchmarks.
For the LLaMA-2-7B and LLaMA-2- 13B models, training conducted on NVIDIA Tesla A100 GPUs utilizing DeepSpeed repository. The total batch size is 32, and both benchmarks use a low-rank parameter of 4. We train the model 50 epochs on the SuperNI benchmark and 20 epochs on the Long Sequence benchmarks.
The primary experiments reported in the paper are the averages obtained from two different orders in Table 10, while other experiments showcase results based on order 1.
For the temperature coefficient T in the dynamic selection module of our method, we set all to 1.0. For the methods we compared, we reimplemented their method based on the open source code of SAPT (Zhao et al., 2024), which makes their results potentially better than what they reported in their original paper. For TASL (Feng et al., 2024), we directly extracted
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Exploiting Presentative Feature Distributions for Parameter-Efficicn Continual Learning of Large Language Models
SuperNI Benchmark BWTT
Long Sequence Benchmark
Methods
Order
APP
F.Ral.
FWTT
APT
F.Ral
FWTT
BWT1
53.98
1.52
-0.06
-0.72
80.04
4.43
0.14
-4.16
Ours-Dot
2
54.30
1.42
0.14
-0.60
82.22
1.78
-1.68
-1.56
55.82
0.22
0.27
-0.17
83.13
0.42
-0.32
-0.35
Ours-L2
2
55.38
0.24
-0.12
-0.22
82.89
0.72
-1.83
-0.47
Table 12: The test performance (%) on the Long Sequence Benchmark using the LLaMA-2-7B model with different task order. All evaluation metrics are reported after the training on the last task of the benchmarks.
SuperNI Benchmark BWTT
Long Sequence Benchmark
Methods
Order
APM
F.Ral.
FWTT
APT
F.Ray
FWT1
BWT1
55.55
2.56
0.73
-1.56
82.72
2.47
-0.56
-2.14
Ours-Dot
55.01
2.51
0.56
-1.97
82.93
2.22
-2.07
-1.93
56.71
0.05
0.13
0.33
84.59
0.36
-0.39
-0.32
Ours-L2
2
56.22
0.49
-0.03
-0.02
84,91
0,42
-1.57
-0.36
Table 13: The test performance (%) on the Long Sequence Benchmark using the LLaMA-2-13B model with different task order. All evaluation metrics are reported after the training on the last task of the benchmarks.
the results they reported their paper.
C. Additional Results
C.1. Complete Results LLaMA-2-7B
For LLaMA-2-7B model, the main experiments are provided in Table 2. In order verify the effectiveness of our method under different task orders, we provide the results of our method under two orders in Table 12. It can be seen that there is no significant difference in our method under different orders, which indicates the stability of our method.
C.2. Complete Results on I.LMMA2-13B
In Table 3, we show the comparison results of our method and recent continual learning methods for LLMMs with PEFT (LoRA: SAPT-LORA, SegLoRA, O-LoRA; Prompt: Prog-Prompt, LFPTS, EPI) on two popular CL benchmarks, using the LLaMA-2-13B model. Our method uses a Top-1 strategy to select LoRA blocks. As illustrated the table, our method consistently outperforms other baselines without II., regardless of the similarity method used. Notably, when using L.2 distance, our method achieves the best average performance across these two benchmarks, which is the same what we found in the main text. We also show in Table 13 how our method performs under different task orders, and these results again demonstrate the effectiveness of our method.
C.3. Complete Results T5-Large
In Table 14, we show the comparison results of our method and recent continual learning methods for LLMs with PEFT (LoRA: SAPT-LORA, SeqLoRA, O-LoRA, TASL-LoRA; Prompt: Prog-Prompp LFPT5, EPI, L2P) two popular CL benchmarks, using the T5-Large model. Our method uses Top-1 strategy to select LoRA blocks. As illustrated in the table, our method achieves good performance on the SuperNI Benchmark over other baselines without IL and close to the performance of the method with IL. On the Long Sequence Benchmark, although there not much difference with other methods without IL, the overall average performance is still the best without IL, which also validates the effectiveness of our method in different frameworks.
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Exploiting Presentative Feature Distributions for Paraieter-EEficient Continual Learning of Large Language Models
SuperNI Benchmark F.Ray.
Long Sequence Benchmark
Methods
Avg.
APT
FWT1
BWTt
APT
F.Ral.
FWT1
BWT1
Replay
35.37
16.92
-1.35
-15.79
71,28
13.05
1.28
-12.18
53.33
SAPT-LORA
X
51.50
0.91
1.88
-0.57
82.02
1.50
1.86
1.25
66.76
TASL-LORA
x
53.01
-0.81
84.33
-0.98
68.67
SeqLoRA
6.43
33.39
-13.58
-3.94
9.72
78.61
0.81
-73.37
8.08
L2P
12.73
11.87
-19.14
-7.95
57.98
22.49
1.36
-16.63
35.36
ProgPrompt
3.34
35.57
-3.29
-33.18
7.98
71.55
-2.63
-66.71
5.66
LFPTS
34.37
15.80
-0.46
-14.47
67.01
13.89
2.48
-12.80
50.69
EPI
75.15
1.61
-0.77
-1.42
O-LoRA
25.89
26.37
-0.14
-24.59
69.24
7.00
-8.15
-4.05
47.57
SAPT-LORA
11.12
42.83
0.70
400.44
10.18
78.45
1.93
-73.22
10.65
TASL-LoRA
27.43
-1699
72.29
-2.04
49.86
Ours-Dot
49.25
2.89
-0.43
-2.56
70.50
9.61
1.86
-9.53
59.88
Ours-L2
51.88
0.48
0.05
-0.26
70.05
9.61
1.87
-9.53
60.97
Table 14: The test performance (%) on two continual learning benchmarks using the T5-Large model. indicates methods with IL, while / signifies methods without IL. All evaluation metries are reported after the training on the last task of the benchmarks. In methods without II., the best performances are highlighted in bold. The last column Avg. the average of the performance on the two benchmarks.
SuperNI Benchmark
Long Sequence Benchmark
T
Avg.t
AP1
F.Ral.
FWTT
BWT1
APt
F.Ral.
FWT1
BWT1
1.0
54.20
1.86
0.66
-1.26
79.77
4.08
-0.42
-3.84
66.99
0.6
53.99
1.30
-0.01
-0.76
81.67
2.36
-1.62
-2.21
67.83
0.2
54.75
0.58
0.43
-0.42
83.06
0.95
-1.74
-0.59
68.91
Table 15: The test performance (%) on two continual learning benchmarks with temperature T changes using the LLaMA-2- 7B model equipped 1.2 distance. All evaluation metrics are reported after the training on the last task of the benchmarks with Order The last column Avg. is the average of the performance on the two benchmarks.
SuperNI Benchmark F.Ral
Long Sequence Benchmark F.Ral
T
Avg.
AP1
FWTT
BWT1
APM
FWT1
BWT1
1.0
56.47
1.16
0.52
-0.36
82.51
2.52
-2.17
2.29
69.49
0.6
56.67
0.92
0.36
0.03
83.81
1.10
-2.08
-0.98
70.24
0.2
56.96
0.29
0.46
0.24
84.67
0.64
-1.74
-0.43
70.82
Table 16: The test performance (%) on continual learning benchmarks with temperature T changes using the LLaMA-2- 13B model equipped I.2 distance. All evaluation metrics reported after the training the last task of the benchmarks with Order 1. The last column Avg. is the average of the performance the two benchmarks.
C.4. Hyperparameter
In the dynamic selection module, Following previous studies (Zhao et al., 2024), we fix this coefficient to 1.0. We add additional experiments concerning the impact of the temperature coefficient in Table 15 and Table 16. In fact, the temperature coefficient and the K have similar usage, as they both adjust the scaling of weights. When the methodes 0, effectively corresponds to setting to 1. Therefore, is not necessary to simultancously adjust multiple parameters externally. We can only adjust one them. In addition, we provide experimental results for different LoRA ranks in Table 17.
the softmax function that includes inherent temperature hyperparameter T.
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Exploiting Presentative Feature Distributions for Parameter-Efficicn Continual Learning of Large Language Models
SuperNI Benchmark
Long Sequence Benchmark
d
Avg.
APT
F.Ray
FWT1
BWT
AP1
F.Ral
FWT1
BWTT
2
54.03
1.23
-0.63
-0.85
79.84
4.46
-1.77
-4.01
66.94
4
54.20
1.86
0.66
-1.26
79.77
4.08
-0.42
-3.84
66.99
6
54.49
1.40
0.77
-1.06
79.75
4.36
-1.87
-4.00
67.12
Table 17: The test performance (%) on two continual learning benchmarks with LoRA rank d changes using the LLaMA-2- 7B model equipped L2 distance. All evaluation metrics are reported after the training on the last task of the benchmarks with Order The last column Avg. is the average of the performance on the two benchmarks.
Methods
Memory
Training Time
O-LoRA
2.097M (0.03%)
37:04
Ours
2.359M (0.03%)
10:47
SAPT
2.928M (0.04%)
13:52
Table 18: The report memory usage (for LLaMA-7B, rank=4) and training time (on the last task in Long Sequence benchmark) for the three methods.
C.5. Similarity Measurement
In the main text, we discussed two similarity measurement methods: Negative L2 euclidean distance and dot product similarity. We also considered the results of using cosine similarity for similarity measurement in Table 19 and Table 20. Actually, this also means that our proposed method does not rely on specific similarity measure, which makes our method more flexible with plug-in similarity measures.
C.6. Memory and time
In Table 18, we provide a comparison of memory and training time. While our method stores additional feature distributions compared with O-LoRA, these distributions are represented lightweight vectors. Even when stored across all layers, they introduce only 0.262M additional parameters. As shown in Figure 3, retaining feature distributions for only a subset of layers achieves strong CL. performance while further reducing memory overhead.
SAPT and our method incur only small increase in lightweight computations during training. However, the parameters added by SAPT require gradient updates and replay data, resulting in slower training speed for SAPT. In contrast, O- LoRA necessitates calculating the square difference between LoRA parameters during loss computation, which introduces substantial computational overhead and leads to the slowest training speed.
C.7. Additional dynamic selection experiments
We provide the average performance of our method under different Top-k strategies for dynamic selection in Figure In order provide details on other metrics, provide detailed experimental results in Table 11. As the Top-K value decreases, we observe noticeable improvements across various metries, indicating that method effectively identifies the most relevant LoRA blocks. It is important note that selecting the Top-1 does not imply that method relies only single LoRA block for prediction. This is because our method is implemented at each transformer layer, allowing for different selections across layers. Therefore, even when using the Top-1 selection, our method fully leverages the knowledge from different LoRA blocks.
C.8. Visualisation experiments
In Figure 1, we present visualizations of the LoRA block selection results, which are averaged across all layers. To offer a more detailed view, we further provide the results for each individual layer in Figure 5, Figure 6, Figure 7 and Figure 8. We can observe that the shallow layers, there are no particularly prominent weights, indicating that the presentative feature distributions in the earlier layers are relatively similar, and the model is likely capturing more superficial features. As the
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Exploiting Presentative Feature Distributions for Faracitte-Efficient Continual Learning of Large Language Models
SuperNI Benchmark F.Ral
Long Sequence Benchmark
Methods
Avg.1
APt
FWTt
BWTT
APT
F.Ray.
FWTT
BWT1
Ours- Durs-Cosine
54.29
0.98
0.34
-0.82
83.02
0.83
-1.78
-0.59
68.66
Ours-Dot 53.98
1.52
-0.06
-0.72
80.04
4.43
0.14
-4.16
67.01
Ours-I.2
55.82
0.22
0.27
-0.17
83.13
0.42
-0.32
-0.35
69.48
Table 19: The test performance (%) on two continual learning benchmarks (Top-k=1) using the LLaMA-2-7B model. All evaluation metries are reported after the training on the last task of the benchmarks with Order 1. The last column Avg. is the average of the performance on the two benchmarks.
SuperNI Benchmark
Long Sequence Benchmark
Methods
Avg.1
APT
F.Ral
FWTt
BWTT
APT
F.Ral
FWTT
BWTT
Ours-Cosine
56.65
0.94
0.44
-0.7
84.50
0.43
-1.71
-0.32
70.58
Ours-Dot 53.55
2.56
0.73
-1.56
82.72
2.47
-0.56
-2.14
69.14
Ours-L22
56.71
0.05
0.13
0.33
84.59
0.36
-0.39
-0.32
70.65
Table 20: The test performance (%) on two continual learning benchmarks (Top-k=1) using the LLaMA-2-13B model. All evaluation metries are reported after the training on the last task of the benchmarks with Order The last column Avg. the average of the performance on the benchmarks.
layers deepen, there is clear tendency in the weights, with the model capturing distinctive features based characteristics, whether they be in Q V. Although the trends of Q and V are the same, there are still differences between them. For example, Q seems to have larger weights compared to V, but both are able to identify the most relevant LoRA blocks. These visualization results further validate the effectiveness of our method.
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Exploiting Presentative Feature Distributions for Parameter- Efficient Continual Learning of Large Language Models
Layer::0
11:0
warts
Figure 5: The heatmap shows the selection of LoRAs for each layer of our method equipped with the L2 distance in the 15 tasks of the SuperNI benchmark. For all results, scaled the values by factor of 100 and highlighted the highest value in each row with yellow box.
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Exploiting Presentative Feature Distributions for Parameter- Efficient Continual Learning of Large Language Models
warts.
Figure 6: The heatmap shows the selection of LoRAs for each layer V of our method equipped with the L2 distance in the 15 tasks of the SuperNI benchmark. For all results, scaled the values by factor of 100 and highlighted the highest value in each row with yellow box.
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Exploiting Presentative Feature Distributions for Parameter- Efficient Continual Learning of Large Language Models
Uyyer
Upper 30
Layer
Layer 11:0
15. Laye
1994
Figure 7: The heatmap shows the selection of LoRAs for each layer Q of our method equipped with the L2 distance in the 15 tasks of the Long Sequence Benchmark. For all results, scaled the values by factor of 100 and highlighted the highest value in each row with yellow box.
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Exploiting Presentative Feature Distributions for Parameter- Efficient Continual Learning of Large Language Models
Laper
1aye113:
Lagert3:
Figure 8: The heatmap shows the selection of LoRAs for each layer of our method equipped with the L2 distance in the 15 tasks of the Long Sequence Benchmark. For all results, scaled the values by factor of 100 and highlighted the highest value in each row with yellow box.
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Reviews:
[{"review_id": "ERDQ0gxBdk", "reviewer": "Reviewer_D8DQ", "summary": "This paper presents a method for continual learning (CL) in large language models (LLMs) that addresses information leakage (IL) while maintaining strong performance. The method leverages the feature representation capability of pre-trained LLMs to encode task-related information into presentative feature distributions, and dynamically selects relevant LoRA blocks based on the similarity between input instances and the presentative feature distributions of different tasks. Experiments demonstrate the effectiveness of the method across multiple benchmarks and model architectures.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you so much for your valuable comments! We provide point-by-point responses to address your concerns.\n\n**Q1: (Methods and References) Similar methods have already been proposed ...... e.g. Feature Adaptation (Iscen et al., 2020).**\n\nA1: Thank you for pointing out these relevant studies, and we will incorporate them into our related work. They indeed utilize feature representations for incremental learning. **However, our method is significantly different from theirs.**\n\nFirstly, we focus on LLMs and aim to make them to continually learn (fine-tune) in dynamic environments, such as distribution shifts or integrating new knowledge. In contrast, iCaRL and Feature Adaptation primarily address classification tasks, where the goal is to increase the number of classes that the model can recognize, which is fundamentally different from our goal.\n\nSecondly, iCaRL utilizes the distance between the output features and the centers of each class. In our selection module, we utilize distances between **pre-trained features** and task center from **each layer**. The sources of features and the targets of selection are different.\n\nAdditionally, Feature Adaptation preserves features extracted by different feature extractors and embeds them to the same space. In contrast, we directly utilize the feature from frozen pre-trained LLMs. Moreover, we do not claim to be the first to use feature distributions as a surrogate for training data. Our goal is to employ these distributions to avoid **information leakage**.\n\n**Q2: (Experiments) The effectiveness of different modules have not be sufficiently demonstrated by e.g. ablation studies.**\n\nA2: Our method first leverages pre-trained LLMs to encode tasks into presentative feature distributions, and then calculate the similarity between instances and the presentative feature distributions to dynamically select proper PEFT blocks. In other words, presentative feature distribution and dynamic selection are connected modules of our method that must work in sequence. Removing any module from our method would make it meaningless. \n\n**Q3: (Weaknesses and Questions) The authors mentioned that IL ......  and how CL can be applied to these scenarios?**\n\nA3: In most cases, model weights (instead of training data) are commonly shared (e.g., open-source ecosystem DeepSeek, Qwen and Mistral). In such situations, CL through data replay is not feasible. Enterprises and research institutions often fine-tune models for specialized tasks with training data that contains proprietary information, such as medical assistants and fraud detection systems. This data often includes user interactions, which cannot be shared due to privacy regulations. This presents challenges for CL, such as adapting models to evolving regulations or updating fraud detection systems to counter new threats, especially when these models must integrate prior knowledge. In these scenarios, effective CL without IL is crucial.\n\n**Q4: (Questions) Does your method support sparse activation like MoE ...... (since all LoRAs contribute to the final output).**\n\nA4: Of course, as our method is based on parameter isolation techniques, it inherently supports selective activation similar to MoE. In fact, we have already implemented this method. As demonstrated in Figure 4, we present the performance variation curve using Top-$k$ activation, which only activates the $k$ closest LoRA modules. We recommend selecting a smaller $k$ to achieve more stable results.\n\n**Q5: (Questions) What’s the difference between cosine similarity ...... you propose in Section 3.3?**\n\nA5: Cosine similarity $\\text{Cos}(A, B) = \\frac{A \\cdot B}{||A|| ||B||}$ and dot product similarity $\\text{Dot}(A, B) = \\frac{A \\cdot B}{\\sqrt{d}}$ are both mathematical methods to measure the similarity degree between two vectors. Their primary difference lies in the normalization scale ($||A|| ||B||$ for cosine similarity and $\\sqrt{d}$ for dot product similarity). \nWe also conducted experiments using cosine similarity. Since dot product similarity is commonly used in the attention calculation, we reported its results in our manuscript. You can find the experiments of cosine similarity through the anonymous link https://anonymous.4open.science/r/ICML-2025-Rebuttal-10369/ICML_2025_Rebuttal.pdf (Table 15 and 16).\n\n**Q6: (Questions) In the zero-shot ...... why would SAPT-LoRA without IL perform better than the one with IL?**\n\nA6: We have indeed observed this phenomenon. It might be due to data replay causing the model to overfit, which results in reduced generalization capabilities. Since the replay data in SAPT is synthetic, it primarily emphasizes the prompt and structure of the problem. As a result, in a zero-shot setting, it is likely to overlook the knowledge required to select the LoRA blocks.", "claims_and_evidence": "The claims are clear and supported by convincing evidence.", "methods_and_evaluation_criteria": "The paper proposes a CL method for LLMs that addresses the IL issue by avoiding introducing new trainable parameters during the selection process. Although the method does prevent catastrophic forgetting and IL, it lacks novelty. Similar methods have already been proposed by earlier works. For example, iCaRL (Rebuffi et al., 2017) uses a nearest-mean-of-examplars classifier, which is highly similar with the selection module in this paper. The idea of storing feature distributions instead of training data has also appeared in previous works, e.g. Feature Adaptation (Iscen et al., 2020).", "theoretical_claims": "This paper presents no proofs or theoretical claims.", "experimental_designs_or_analyses": "The experimental settings are reasonable. The choice of benchmarks, baseline methods and evaluation metrics are appropriate and comprehensive. However, the effectiveness of different modules (i.e. presentative feature distribution and dynamic LoRA selection) have not be sufficiently demonstrated by e.g. ablation studies. In addition, in Table 3, SAPT-LoRA without IL performs even better than the one with IL, which is contrary to the previous results. It would be better if the authors attempt to explain this phenomenon in the paper.", "relation_to_broader_scientific_literature": "This paper advances the field by addressing a specific limitation (information leakage) in CL for LLMs. It contributes a solution to a practical problem – how to apply CL methods of LLMs to data-sensitive scenarios. It also presents a conceptual framework for how pre-trained models can be adapted in ways that respect data privacy constraints.", "essential_references_not_discussed": "CL methods with similar ideas to this paper but not discussed include iCaRL (Rebuffi et al., 2017), Feature Adaptation (Iscen et al., 2020), etc. There are also parameter isolation-based LLM methods that haven’t been discussed in this paper, e.g. MoLoRA (Zadouri et al., 2023) and LoRAMoE (Dou et al., 2023).", "other_strengths_and_weaknesses": "The authors mentioned that IL hinders the application of CL in scenarios involving data-sensitive or specialized tasks. It would be better if they could provide some examples – what kind of scenarios in practice would requires high data sensitivity or privacy, why such scenarios require CL, and how CL can be applied to these scenarios?", "other_comments_or_suggestions": "There are some typos in the paper:\nTable 1 – “L5” should be “T5”\nTable 4 – “Singe” should be “Single”\nTable 4 – “proformance” should be “performance”", "questions_for_authors": "1. Application in practice: what kind of scenarios in practice would IL matter, why such scenarios require CL, and how CL can be applied to these scenarios? \n2. Does your method support sparse activation like MoE (i.e. only activate some of the LoRA modules in a layer, instead of all at once)? The formula in Section 3.4 suggests that it’s not supported (since all LoRAs contribute to the final output).\n3. What’s the difference between cosine similarity and the dot product similarity you propose in Section 3.3?\n4. In the zero-shot experiment (Table 3), why would SAPT-LoRA without IL perform better than the one with IL?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8XOtDA7LFr", "reviewer": "Reviewer_MixK", "summary": "This paper presents a novel continual learning method for LLMs that avoids information leakage by employing presentative feature distributions. The proposed method characterizes parameter-efficient fine-tuning blocks using feature distributions and dynamically selects suitable blocks based on similarity metrics. The proposed methods perform well in continual learning benchmarks without accessing previous data. Extensive experiments on SuperNI and Long Sequence benchmarks validate its effectiveness.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for the time and effort you have dedicated to reviewing our work. We provide point-by-point responses to address your concerns.\n\n**Q1: (Weaknesses) The empirical evidence is evident but ...... like cosine similarity.**\n\nA1: Due to word limit and the repeated mention of this weakness, we kindly ask you to refer to the responses for Q3 of Reviewer D8DQ and Q3 of Reviewer 54sH.\n\n**Q2: (Weaknesses) The hyperparameter sensitivity is not discussed ...... of this coefficient in the experiments.**\n\nA2: The temperature coefficient $T$ is an inherent hyperparameter of the softmax function. Following previous studies (SAPT, Zhao et al.), we fix this coefficient to 1.0. We add additional experiments concerning the impact of the temperature coefficient in the anonymous link https://anonymous.4open.science/r/ICML-2025-Rebuttal-10369/ICML_2025_Rebuttal.pdf (Table 17 and 18).\n\nIn fact, the temperature coefficient and the $K$ have similar usage, as they both adjust the scaling of weights. When the $T$ methodes 0, it effectively corresponds to setting $K$ to 1. Therefore, it is not necessary to simultaneously adjust multiple parameters externally. We can only adjust one of them.\n\n**Q3: (Weaknesses and Questions) In the ...... compare the actual memory usage or training time with O-LoRA?**\n\nA3: We explicitly discussed the memory usage of our method in Lines 380-384 of the manuscript. To further clarify, we provide a detailed comparison across different methods in the anonymous link https://anonymous.4open.science/r/ICML-2025-Rebuttal-10369/ICML_2025_Rebuttal.pdf (Table 19).\n\n(Memory) While our method stores additional feature distributions compared with O-LoRA, these distributions are represented as lightweight vectors. Even when stored across all layers, they introduce only 0.262M additional parameters. As shown in Figure 3, retaining feature distributions for only a subset of layers achieves strong CL performance while further reducing memory overhead.\n\n(Training Time) SAPT and our method incur only a small increase in lightweight computations during training. However, the parameters added by SAPT require gradient updates and replay data, resulting in slower training speed for SAPT. In contrast, O-LoRA necessitates calculating the square difference between LoRA parameters during loss computation, which introduces substantial computational overhead and leads to the slowest training speed.\n\n\nThere is no rank reduction during the combination process (illustrated in Figure 2). The feature distributions $D^{l} _{k} = \\mathbb{E} _{p(x^{k}, y^{k})} [\\mathbf{W}^{l}h^{l}(x^{k})]$ are derived from pre-trained model features and are independent of the LoRA blocks. This allows our method to seamlessly combine LoRA blocks of different ranks into a unified model.\n\n**Q4: (Questions) The authors define “information leakage” ...... Could authors clarify the technique novelty compared to O-LoRA?**\n\nA4: O-LoRA avoids catastrophic forgetting by ensuring that the parameters of new tasks are orthogonal to those of existing tasks. However, when new tasks overlap or conflict with learned tasks, this constraint can limit the learning performance. As a result, O-LoRA cannot perform as well as state-of-the-art continual learning methods, although it prevents IL as well as our method.\n\nOur method treats each LoRA block as a repository of knowledge, dynamically selecting them as needed. Although our method preserves the presentative feature distributions, these distributions are high-dimensional and beyond human comprehension, similar to the LoRA parameters. Moreover, presentative feature distributions represent averaged information across populations and cannot reconstruct individual information. Therefore, our approach achieves superior continual learning performance while effectively avoiding information leakage.\n\n**Q5: (Questions) In Table 2, the AP result ...... Could the authors clarify the reason for this discrepancy?**\n\nA5: To ensure a fair comparison, we implemented previous CL methods using the open-source framework of SAPT. These adjustments led to improved performance for most methods compared with their original reports. Our experiments are based on the SAPT framework, which explains the lack of significant differences in SAPT results as reported. It's important to note that SAPT did not provide detailed instructions for reproducing O-LoRA. Our settings may differ due to two key hyperparameters in O-LoRA: we consistently set $\\lambda_{1}$ to 0.5 and $\\lambda_{2}$ to 0, retaining all LoRA layers without merging them—factors that significantly impact results. Additionally, discussions on the O-LoRA GitHub indicate that the number of GPUs used can influence results, with an error margin exceeding 8\\%. Our experiments utilized 8 A100 GPUs, while SAPT used 4 A800 GPUs. We provide the code to reproduce the O-LoRA results in the anonymous link https://anonymous.4open.science/r/ICML-2025-Rebuttal-10369/ICML_2025_Rebuttal.pdf.", "claims_and_evidence": "The claims made in the submission are supported by empirical evidence and supportive experiments.", "methods_and_evaluation_criteria": "The proposed method avoids information leakage in continual learning and the benchmarks are appropriate for evaluation in continual learning.", "theoretical_claims": "This paper does not have any proof for theoretical claims, and the justification for using presentative feature distributions is mainly based on empirical experiments without solid theory formalization.", "experimental_designs_or_analyses": "The experimental designs are well-structured, following prior works in continual learning. However, the hyperparameter sensitivity is not discussed well in this paper, such as the temperature coefficient.", "relation_to_broader_scientific_literature": "No specific.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths:\n1. The proposed method avoids information leakage in continual learning, which is practical in real-world deployment.\n2. The experiments are comprehensive to show the effectiveness of the proposed method, comparing against strong baselines, O-LoRA and SAPT.\n\nWeaknesses: \n1. The computational cost and memory overhead are not discussed. While the method avoids information leakage, it is unclear the cost for additional computation in similarity-based selection.\n2. The empirical evidence is evident but this paper lacks rigorous theoretical analysis for the similarity metric selection, for example, the reason for choosing $L_2$ Euclidean distance and Dot Product Similarity rather than alternatives like cosine similarity.\n3. The comparison discussion on previous works primarily focuses on SAPT and mentions the difference in information leakage. However, O-LoRA itself does not information leakage based on the definition of this paper since the key difference from O-LoRA is not clear and not discussed well.\n4. The hyperparameter sensitivity is not discussed well in this paper. In the equation in line 217 on page 4, there is the temperature coefficient used to control normalization, but there is no analysis of the impact of this coefficient in the experiments.", "other_comments_or_suggestions": "There are several typos in the paper, for example, in lines 063-064, “L5-Large model” should be “T5-Large model”, and in line 192, “Equ 3” should be “Eq. 3” or “Eq(3)”.", "questions_for_authors": "1. In the overall architecture of the proposed method, each task’s LoRA parameters and feature distributions are stored. Did authors compare the memory usage with O-LoRA? Since O-LoRA only needs to store the previous task LoRAs. Is there any rank reduction during the combination process in the right section in Figure 2? Could authors please compare the actual memory usage or training time with O-LoRA?\n2. The authors in this paper define “information leakage”, which refers to the accessing or reusing of task-related information (e.g. training data and task identifiers) from previously learned tasks again. But in my opinion, the presentative feature distribution can still be considered indirect information, which is related to actual parameters. While in O-LoRA, it only uses the previous LoRA parameters. Could authors clarify the technique novelty compared to O-LoRA?\n3. In Table 2, the AP result for O-LoRA on SuperNI benchmark is 37.17, but in SAPT paper, the AP result for O-LoRA on SuperNI benchmark is 24.26 (on page 20), which is a substantial difference. I checked the appendix of this paper and found that both studies use 50 training epochs and a learning rate of 5e-5. However, despite these similarities, the AP result in SAPT-LoRA with information leakage on SuperNI benchmark consistent similarities, whereas O-LoRA shows a significant difference in this paper. Could the authors clarify the reason for this discrepancy?\n4. Since there is a temperature coefficient $T$ used to control normalization in the equation, could authors please provide the analysis of how different values of this coefficient impact the performance of the proposed method?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kQW7gleasD", "reviewer": "Reviewer_54sH", "summary": "This paper proposes a novel parameter-efficient continual learning (CL) framework for large language models (LLMs) that leverages pre-trained model representations to dynamically select task-specific LoRA blocks via presentative feature distributions. The method addresses the critical challenge of information leakage (IL) inherent in prior CL approaches (e.g., data replay or parameter isolation with shared modules) by eliminating dependency on historical task data or identifiers during inference. Experimental validation on the SuperNI and Long Sequence benchmarks demonstrates state-of-the-art performance among IL-avoidant methods while maintaining competitive results compared to methods with IL risks.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for recognizing our work and taking the time to review our manuscript. Below is our response to address your concerns.\n\n**Q1: (Questions) How does the method disambiguate ...... layer-wise selection inherently mitigate this?**\n\nA1: Our method can effectively identify tasks with overlapping feature distributions. In cases where we have two identical tasks, the dynamic selection process will allocate selections evenly between them, similar to how each task would be trained independently. Furthermore, the layer-wise selection process offers additional mitigation against overlapping feature distributions. As evidenced by our visualizations in Figures 5–8, the model learns progressively from shallow to deep layers, and our method can capture and distinguish the nuances of feature overlaps.\n\n**Q2: (Questions) What is the maximum K tested? How ...... computation impact real-time inference?**\n\nA2: The parameter $K$ used for testing was consistent with that used during training. Specifically, in Tables 2, 10, and 14, we set $K$ to 1, while for the remainder of the experiments, $K$ was set to the total number of tasks. It is important to note that the values of $K$ during training and testing do not have to be identical. For simplicity, we chose to keep them the same.\n\nDuring real-time inference, our method calculates the similarity between instances and the stored feature distributions and uses this similarity to select PEFT blocks. This process only adds a step for similarity computation compared to the original inference. Since quadratic similarity, dot-product similarity, and cosine similarity are easily implemented with matrix parallelization, the real-time inference time does not significantly change with different $K$.\n\nAs reflected in Figure 4, we present the results of quadratic similarity under different $K$. It can be observed that quadratic similarity maintains stable performance across various $K$ values. This stability arises because quadratic similarity is unbounded before normalization, leading to more extreme weights after normalization. Different values of $K$ prioritize the exclusion of irrelevant LoRA blocks, and these blocks inherently have low weights when using quadratic similarity.\n\n**Q3: (Questions) While similarity metrics are explained ...... enhance the quality of the paper.**\n\nA3: We agree that a stronger theoretical basis would enhance the quality of this paper. However, theoretical frameworks related to large models, particularly in high-dimensional spaces such as hidden layers and feature spaces, are still under development. **All existing research lacks theoretical assurance.** Fortunately, we can provide some theoretical support for our method.\n\nInspired by the studies on model merging[1], changes in model parameters are considered as directions that contain knowledge of specific tasks, referred to as task vectors. Since LoRA blocks are ultimately added to the original model parameters in our method, each  LoRA blocks $A_k$ and $B_k$ can also be seen as task vectors, offering rationale for our dynamic selection.\n\nFor any given instance feature $\\mathbf{W}^{l}h^{l}(x)$, it has undergone the same pre-trained parameters $\\mathbf{W}^{l}$, thereby being a linear transformation applied uniformly across all instances. Given that related tasks require related knowledge, the final output space should be similar (e.g., vocabulary or patterns). Therefore, we establish a feature distribution $ D^{l} _{k} = \\mathbb{E} _{p(x^{k}, y^{k})} [\\mathbf{W}^{l}h^{l}(x^{k})] $ for the transformed features, and select blocks based on the distance to these distributions. \n\n[1] Gabriel Ilharco et al. Editing Models with Task Arithmetic. In NeurIPS, 2023.\n\n**Q4: (Questions) The fixed or limited tuning of hyperparameters ...... need further exploration.**\n\nA4: The temperature coefficient is an inherent hyperparameter of the softmax function, and the LoRA rank is inherent to LoRA fine-tuning. These two hyperparameters are not unique to our method. In Figure 4, we have discussed the specific hyperparameter $K$ associated with our method. To further validate the adaptability of our method across different hyperparameter settings, we have conducted additional experiments in the anonymous link https://anonymous.4open.science/r/ICML-2025-Rebuttal-10369/ICML_2025_Rebuttal.pdf (Table 20, 21 and 22).\n\nIn fact, the temperature coefficient and the parameter $K$ serve similar purposes, as they both adjust the scaling of weights. When the temperature $T$ approaches 0, it effectively corresponds to setting K to 1. Therefore, it is not necessary to simultaneously adjust multiple parameters externally, just choose one to adjust.\n\nWe agree that exploring adaptive hyperparameter tuning and systematic search is a promising area of research. This issue remains challenging, especially in large models, which have numerous hyperparameters that can be adjusted. We will make this challenging problem our future work.", "claims_and_evidence": "Yes, the claims are well-supported by empirical evidence.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "I did not find significant formal proofs to check for correctness; it is primarily an algorithmic and empirical contribution.", "experimental_designs_or_analyses": "The experiments are well-structured: they compare multiple baselines under the same conditions (model, tasks, orders), and they measure standard metrics for CL.", "relation_to_broader_scientific_literature": "This paper builds on parameter-efficient fine-tuning (PEFT) techniques like LoRA, and addresses the continual learning problem which is well-studied in classical settings. They also link to recent LLM-based CL methods such as SAPT, O-LoRA, and prompt-based methods.", "essential_references_not_discussed": "I am not aware of a crucial missing reference that is essential for contextualizing the approach.", "other_strengths_and_weaknesses": "Strengths:\n1. The proposed framework contains presentative feature distribution and dynamic similarity-based selection which avoids common issues such as additional forgetting from new parameters or information leakage from data replay.\n2. The method addresses critical real-world needs—such as high training costs for LLMs, model scalability, and privacy constraints that prevent historical data reuse—by enabling data-free (or replay-free) continual learning with robust performance.\n3. Comprehensive experimental results on two representative benchmarks (SuperNI and Long Sequence) demonstrate the effectiveness of the proposed method.\nWeakness:\n1.While similarity metrics (e.g., L2 distance, dot product) are explained, the theoretical rationale for using high-dimensional feature distributions to stably represent task-specific domains remains underdeveloped. Further analysis of theoretical robustness and consistency in complex scenarios can enhance the quality of the paper.\n2.The fixed or limited tuning of hyperparameters (e.g., temperature coefficient , LoRA rank) raises questions about their adaptability to varying data scales or task distributions. Systematic hyperparameter search or adaptive strategies need further exploration.", "other_comments_or_suggestions": "A more detailed analysis of inference speed and memory usage would help clarify real deployment feasibility.", "questions_for_authors": "1.How does the method disambiguate tasks with overlapping feature distributions (e.g., sentiment analysis across domains)? Does layer-wise selection (Figures 5–8) inherently mitigate this?\n2.What is the maximum K tested? How does the quadratic similarity computation (for K tasks) impact real-time inference?\n3.While similarity metrics (e.g., L2 distance, dot product) are explained, the theoretical rationale for using high-dimensional feature distributions to stably represent task-specific domains remains underdeveloped. Further analysis of theoretical robustness and consistency in complex scenarios can enhance the quality of the paper.\n4.The fixed or limited tuning of hyperparameters (e.g., temperature coefficient , LoRA rank) raises questions about their adaptability to varying data scales or task distributions. Systematic hyperparameter search or adaptive strategies need further exploration.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper solves an important problem in LLM continual learning, called information leakage, where task-related information of learned tasks is accessed or reused again. This problem could impose potential risks on data privacy, which might hinder the real-world deployment of LLMs. To address this problem, this paper proposes to exploit presentative feature distributions by pre-trained LLMs and use them to dynamically select relevant knowledge to prevent forgetting. This method is simple and intuitive, but it is effective and has multiple advantages, for example, avoiding information leakage, no trainable parameters, and flexible expansion. Experimental results can demonstrate the effectiveness of the proposed method.

During the review process, this paper received mixed reviews, containing 1 accept and 2 weak reject, from 3 reviewers. All the reviewers confirmed the proposed method can avoid information leakage in LLM continual learning, and experimental results can valid the effectiveness of the method. However, Reviewer D8DQ has major concerns in the novelty and the application of the method. Reviewer MixK also raised major concerns in the similarity and training time. Reviewer 54sH gave positive rating and asked some minor questions.

During the rebuttal process, the authors provided a rebuttal, and all the reviewers acknowledged it. Reviewer 54sH said no further questions and decided to keep acceptance. Reviewer MixK raised one follow-up question about whether cosine similarity could also be considered an alternative for the proposed method. The authors replied to this question, while Reviewer MixK didn’t reply whether his follow-up question is addressed or not. Reviewer D8DQ also didn’t say whether his concerns are addressed or not.

From my viewpoint, this paper does not reply on certain evaluation metrics, so cosine similarity can be also used, and this is an advantage instead of disadvantage. I feel although the method is simple, the authors’ rebuttal explained the differences between this work and previous work. Since this work can avoid information leakage, the application would be more promising than similar methods. So I think the authors’ rebuttal addressed the concerns of Reviewer MixK and Reviewer D8DQ. So I recommend acceptance.
Decision: Accept (poster)