Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
Authors: Hanglei Hu
Yingying Guo
Zhikang Chen
Sen Cui
Fei Wu
Kun Kuang
Min Zhang
Bo Jiang
Venue: ICML 2025 poster (2025)
Keywords: Neural Collapse
Personalized Learning
Large Language Model
Primary area: deep_learning->robustness

Abstract: Personalized learning, especially data-based methods, has garnered widespread attention in recent years, aiming to meet individual student needs. However, many works rely on the implicit assumption that benchmarks are high-quality and well-annotated, which limits their practical applicability.  In real-world scenarios, these benchmarks often exhibit long-tail distributions, significantly impacting model performance. To address this challenge, we propose a novel method called **N**eural-**C**ollapse-**A**dvanced personalized **L**earning (NCAL), designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure. NCAL introduces Text-modality Collapse (TC) regularization to optimize the distribution of text embeddings within the large language model (LLM) representation space. Notably, NCAL is model-agnostic, making it compatible with various architectures and approaches, thereby ensuring broad applicability. Extensive experiments demonstrate that NCAL effectively enhances existing works, achieving new state-of-the-art performance. Additionally, NCAL mitigates class imbalance, significantly improving the model’s generalization ability.

Advancing Personalized Learning with Neural Collapse for Long- Tail Challenge
Hanglei Hu
Yingying Guo
Zhikang Chen
Sen Cui ? Fei Wu
Kun Kuang
Min Zhang
Bo Jiang'
Abstract
Anderson et al., 1995; Raj & Renumol, 2019; Pukkhem & Vatanawood, 201 1) rely on predefined rules and expert knowledge to guide personalization, while data-based meth- ods (Ayeni et al., 2024; Tapalova & Zhiyenbayeva, 2022; Conati et al., 2021; Wang et al., 2024b; Han et al., 2024) leverage large-scale data and advanced machine learning techniques to dynamically adapt and optimize the learning experience. Due their flexibility and efficiency in meet- ing individual needs, data-driven methods have gradually become the mainstream approach. Therefore, in this paper, we focus on data-based personalized learning.
Personalized learning, especially data-based meth- ods, has garnered widespread attention in recent years, aiming to meet individual student needs. However, many works rely on the implicit as- sumption that benchmarks are high-quality and well-annotated, which limits their practical ap- plicability. In real-world scenarios, these bench- marks often exhibit long-tail distributions, sig- nificantly impacting model performance. To ad- dress this challenge, we propose novel method called Neural-Collapse- Collapse- Advanced personalized Learning (NCAL), designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure. NCAL introduces text- modality collapse (TC) regularization to optimize the distribution of text embeddings within the large language model (LLM) representation space. Notably, NCAL is model-agnostic, making it com- patible with various architectures and approaches, thereby ensuring broad applicability. Extensive experiments demonstrate that NCAL effectively enhances existing works, achieving new state-of- the-art performance. Additionally, NCAL mit- igates class imbalance, significantly improving the model's generalization ability. Code is avail- able at https: / httggggtthhhhct.g com/ mmt/uummmmmu///mmmmmm/ NCAL ICML2025. git.
Data- basad personalized learning is widely applied in impor- tant educational scenarios such as knowledge tracing (Ghosh et al., 2020; Sun et al., 2025), cognitive diagnosis (Wang et al., 2020; 2024b), computer adaptive testing (Zhuang et al., 2023; Mousavinasab et al., 2021) and on. In re- cent years, researchers have progressively advanced toward deeper exploration, not only leveraging more complex ar- chitectures to enhance the expressive power of models (Lu et al., 2021; Grawemeyer et al., 2016) but also focusing on constructing larger-scale and more diverse datasets (Choi et al., 2024; Sun et al., 2019; Huang et al., 2016) to improve the generalization ability of models across different contexts. For example, in cognitive diagnosis tasks, by thoroughly analyzing students' learning behaviors and cognitive charac- teristics, personalized diagnostic reports can be generated to help teachers monitor students' progress and identify learn- ing bottlenecks in real time, thus providing technical support for student-centered teaching models (Wang et al., 2020; Lu al., 2021). However, these methods often based implicit assumption that the data used in data-driven approaches is of high quality and annltanno..
1. Introduction
Personalized learning is an important downstream task in intelligent education, aiming to address the unique needs, preferences, and abilities of individual learners (Shemshack & Spector, 2020; Bernacki et al., 2021; Wang et al., 2024a). It primarily encompasses two paradigms: rule-based and data-based methods. Rule-based methods (Skinner, 1961;
In real-world scenarios, the collection of high-quality and standardized educational data is both challenging and im- practical due to concerns over protecting the personal pri- vacy of minors and the reliance on annotations from ed- ucational experts (Dominguez al., 2010; Enders al., 2008). As result, benchmarks for data-based personalized learning naturally exhibit imbalance (or long-tail) distribu- tions (Menon et al., 2020; Kanchon et al., 2024; Zhang et al., 2024). However, existing research has largely overlooked this problem. Experiments shown in Figure (a) and (d) demonstrate that the performance of the Qwen2.5 varies with changes in data balance. When the 0.25 value is
'Equal contribution Corresponding author leader 'East China Normal University "Tsinghua University 'Zhejiang Univer- sity. Correspondence to: Min Zhang <mzhang cs. wss.cmn..eu.cn... Bo Jiang <bjiang @ ccn..c....d............
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Advancing Personalized Learning with Neural Collapse for Long-Tail Tail Challenge
ACC: 71.71
ACC: 76.86
ACC: 61.14
ACC: 74.86
Macro-FI: F1: 71.51
Macro-F1, Fl: 76.72
Macro-Fl: F1: 60.59
Macro-Fl: 75.58
Imbalauce r 0
Imbalance r == 0.03
(a) Qwwnn.5-Instruct7B
(b) NCAI-QQee2.5-mmsmuuee
(c) Qwen2.5-Instruet778
(d) NCAL--Qwen..5-nsstueee
Figure 1: Neural collapse degree visualization in Qwen2.5 (Qwen et al., 2025) TMWPL. Arrows represent the directions of category centers, while points indicate sample features, with colors corresponding to categories. (a) and (b) show the final classification for the baseline and our NCAL under the T 0.25 setting. (c) and (d) illustrate the experimental results in the T = 0.03. NCAL clearly strengthens the ETF structure, leading to improve generalization performance.
higher, indicating weaker data imbalance, the model per- forms better. Conversely, as the r = 0.03 value decreases and data imbalance worsens, the model's performance dete- riorates, dropping from 71.71 (Figure (a)) to 61.14 (Fig- ure (c)). Additionally, the distribution of class centers transitions from being dispersed to becoming increasingly aggregated. This naturally raises a pivotal question:
LoRA fine-tuned models under long-tail data distributions. Through theories and experiments, we demonstrate that in- tegrating NC into personalized learning leads to significant improvements across multiple evaluation metrics. Our con- tributions are mainly summarized as follows:
To the best of our knowledge, we are the first paper to extend the neural collapse phenomenon to data-based personalized learning and to explore its underlying rep- resentational mechanism for generalization capability.
How can the data imbalance long-toil distributions in data-based personalized learning be studied and explored to enhance the robustness of intelligent education models?
We further investigate the impact of class imbalance on the performance of LLM--based methods in personal- ized learning, filling a critical gap in existing research. We propose the NCAL to improve the generalization of LoRA fine-tuned LLMs in glass-imbalanced settings.
The recently discovered neural collapse (NC) phenomenon has emerged as a groundbreaking insight into the optimal structure of visual representations in the field of computer vi- sion (Papyan et al., 2020; Li et al., 2023; Zhang et al., 2022). This phenomenon reveals that when a model achieves zero training error on a sufficiently large and balanced dataset, the last-layer features within the same class converge to their class center. Furthermore, these class centers, along with their corresponding classifier vectors, align with the vertices of simplex equiangular tight frame (ETF). A simplex ETF describes geometric structure where vectors maximize pairwise angles while maintaining equal norms. Inspired by this phenomenon, we aim to investigate the geometric structures of representations during the optimization in data- based personalized learning methods, seeking to enhance their theoretical foundation and practical performance.
Through theoretical analysis and extensive experi- ments, we validate the effectiveness of combining NCAL with existing LoRA fine-tuning methods for LLMs and demonstrate its significant improvement.
2. Related Work
Personalized learning. Personalized learning empha- sizes customizing educational experiences to address the unique needs, preferences, and abilities of individual learn- ers (Shemshack & Spector, 2020; Bernacki et al., 2021). Over the years, it has evolved into two primary paradigms: rule-based and data-based methods. Rule-based meth- ods (Skinner, 1961; Anderson et al., 1995; Raj & Renumol, 2019; Pukkhem & Vatanawood, 20 rely on predefined rules and expert knowledge to guide personalization. In con- trast, data-based methods (Ayeni et al., 2024; Graesser et al., 2004; Grawemeyer et al., 2016; Tapalova & Zhiyenbayeva, 2022; Conati al., 2021) harness insights from large-scale
In this paper, we propose novel method called Neural- Collapse-Advanced personalized Learning (NCAL), de- signed to learn features that conform to the equiangular tight frame (ETF) structure. Specifically, NCAL intro- duces text-modality collapse (TC) regularization to opti- mize the distribution of text embeddings within the large language model (LLM) representation space, thereby en- hancing the robustness and generalization ability of LLM
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Advancing Personalized Learning with Neural Collapse for Long-Tail Tail Challenge
Data Features
Model Architecture
LLM SFT
W hat should we do then? Could we perhaps formulate a hypothesis?
I-H
Batch size
Let's hypothesize that Xiao Ming's age is X years.
Pretrained weights
is accoplable. Given that iiao Ming's age X, can you now deduce someone else's age?
I-Q
WER-
LLM NCAL
father's is age.
LoRA
Then, how old is the father?
I-Q
A=N(O,
Input
B=0
Widden layer
Response Resporse
huttation-qusttoaing Response- Simplistic Response
Teuh modalit Collagee
The architecture of the proposed NCAL. While traditional LLM fine-tuning methods (e.g., LoRA) fail to achieve ETF in their final text representations, our NCAL introduces the Text-modality Collapse (TC) loss to align text representations with a simple ETF structure, enabling robust classification performance on long-tail distributions.
Figure 2:
data, frequently employing advanced machine learning tech- niques, to dynamically adapt and optimize learning experi- ences. Rule-basse based methods often require significant invest- ments of time and resources, making data-based methods the dominant paradigm in personalized learning. In this paper, we focus primarily on data-based personalized learning.
fields, including imbalanced learning (Liu et al., 2023; Zhu et al., 2021; Tirer & Bruna, 2022; Kothapalli, 2022), con- trastive continual learning (Montmaur et al., 2024), trans- fer learning (Galanti et al., 2021), federated learning (Li et al., 2023; Huang et al., 2023) and so on. Although neural collapse has been validated in various domains, existing research has largely been limited to linear classifiers, In this paper, we are the first to apply neural collapse to address the long-tail -ail problem in data- bata-based personalized learning and improve the robustness of large language models during LoRA fine-tuning in personalized learning scenarios.
Data-based personalized learning. The ultimate goal of data-based personalized learning is to have learning sys- tem (Shemshack & Spector, 2020) that can self-adjust ac- cording to learners' characteristics and needs. Based on the rulc-based foundation, data-based personalized learning gradually progressing towards this goal. Recent studies have not only used diverse techniques enhance the rep- resentational and analytical capabilities of models (Zhou et al., 2020; 2016; Bulathwela et al., 2020; Graesser al., 2004), but also the assessments of students' ability and cognitive level are refined and automated (Askarbekuly &
3. Preliminary
In this section, we first give detailed description of LoRA- based fine fine-tuning for LLMs in Section 3.1, and then intro- duce the problem definition of neural collapse in Section 3.2.
Aničić, 2024; Cohn et al., 2024; Chang & Ginter, 2024), which construct students' portraits better. For example, in the personalized recommendation task, the browsing logs of different students are analyzed to understand learners' back- ground knowledge and cognitive state, and then recommend personalized content that is novel and in the nearest devel- opment zone (Bulathwela et al., 2020). However, these methods are often based on an implicit assumption that the data used in data-driven approaches of high quality and well-annotated. Therefore, in this paper, we propose a novel method to address the challenge in the real-worle setting.
3.1. Data-Based Personalized Learning
Data-based personalized learning emphasizes the process- ing and analysis of students' learning and behavioral data. Owing to LLMs' exceptional ability to handle complex lan- guage tasks, their application in data-based personalized learning has become a prominent trend (Liu al.; Shrid- har al., 2022; Cheng al., 2022; Zhang et al., 2023). Modern LLMS typically adopt the Transformer architecture, which processes sequential data through multiple layers of self-attention and feed-forward networks. The fundamental computation in each transformer layer can be expressed as:
Neural collapse. Neural collapse is a geometric phe- nomenon where the structured arrangement of features en- hances the separation between categories. This phenomenon was first discovered by Papyan et al. (Papyan et al., 2020) during training on balanced datasets, enabling classifiers to make more stable decisions as training progresses. In recent years, research neural collapse has expanded into various
lu FFN(LayyyNom((/h MultiHeadd(h--1)), (1)
where by represents the hidden state at layer 1, FFN means the ford-fo network, LayerNorm denotes the layer nor- malization and the multi-head attention mechanism (Multi-
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
Head) is computed through H parallel attention heads:
where M [m] :....... my] E ROXN, U E R satisfies rotation, N is the number of classes and U IN, In is the identity matrix, E RR x1 is an all-ones vector. Vec- tors in simplex ETF have an equal l2 norm and consistent pairwise angles, as captured in the following formula:
MultiHead( (X) Concat(head,,,.., he. (2)
head,- Attention XW,,, XXW)).
(3)
For text encoding, LLMs first transform input tokens into continuous representations through an embedding layer:
v.vv, = -
N
1 N-
vi E [I,
(7)
2 = =((x)= + P. 2 E R
(4)
where òz,j is the Kronecker delta function, which equals when i = j and 0 otherwise. This ensures the maximal equiangular separation is pairwise angle 1 N-11
where x' represents the input token sequence, E is the token embedding matrix, P is the positional encoding, I. is the sequence length, and D is the model dimension.
The neural collapse phenomenon (Papyan et al., 2020) is guaranteed by the following three interrelated characteris- ties: features collapse to the class prototypes (NC1), proto- types collapse to simplex ETF (NC2), and classifiers col- lapse to the same simplex ETF (NC3).
Consequently, increasing body of research in personal- ized learning is focusing on how to effectively fine-tune large language models to address the unique needs of indi- vidual learners, harnessing the models' robust representa- tional capabilities to enhance learning outcomes.
NC1: As training advances, the variation within each class in the final layer will collapse, ie., for all n, Σw + O. Diy = f. Σ (ux,i un) (un,i un)" where Un,i de- notes the features obtained from the i-th sample of class n, and u, is the class n's prototype.
Of all the fine-tuning methods, LoRA aims at fine-tuning LLMs on specific tasks, reducing the number of trainable parameters and minimizing performance loss (Hu et al., 2021). Inspired by (Aghajanyan et al., 2020), LoRA as- sumes that weight updates during adaptation also have a low "intrinsic rank", and constrains the update of the pre-trained weight matrix Wo E Rdd through a low-rank decomposi- tion AW- BA, where B E Rd and A T include trainable parameters. For these matrixes, d denotes the rank of LLMs, is the rank of lora module, and i is the output dimension of the original weight matrix.
NC2: All classes converge to the vertices of a sim- plex ETF with identical pairwise angles, i.e., ün (u, Ug) / |lu, ugll, as defined in Equation (7). ug Σ Ene Un,i denotes the global mean of the features.
NC3: The vectors in the final layer of the linear classifier matrix converge simplex ETF prototypes, i.e., u, m,/ |,,)l |l. where un is the classifier weights of class n.
To enhance parameter efficiency and reduce memory usage, LoRA modifies the forward pass as follows:
y Wox + AWr x = Wax @ BAr
(5)
4. Methodology
where x E R denotes the input, E Rd represents the out- put after a low-rank adjustment and the rank r < min(d, k) because the parameter efficiency of LoRA must be preserved during inference. a is a constant parameter for scaling. The matrix A is initialized as Gaussian distribution with mean of and the matrix B is initialized zero matrix.
In this section, we present our NCAL approach to address the long-tail problem in educational data. We first extend the concept of NC text-based LLMs in Section 4.1, in- troducing the TCD. In Section 4.2, describe our TC regularization, which integrates an NC-inspired loss with LoRA fine-tuning. This approach aims to optimize LLM representations towards a simplex ETF structure. Finally, in Section 4.3, we provide a theoretical analysis of our method.
3.2. Neural Collapse
Neural collapse refers phenomenon observed in the final stage of training when the model achieves zero training error and datasets are balanced. At this stage, the last layer of the feature and structure of the classifier together form a simplex equiangular tight frame (ETF), which is defined as:
4.1. Neural Collapse in Text-Based LLMs
While neural collapse (NC) has been extensively studied in computer vision and other domains, its application to pure text-based L.LMs remains relatively unexplored. We propose a theoretical framework to extend NC concepts to text-based L.Mss, focusing the structure of text represen- tations in the embedding space.
Definition (Simplex equiangular tight frame). A matrix consisting of N vectors m, E E" with v 2 N meaning the dimension of the feature space, may form simplex ETF if it satisfies the following formula:
Let h+(-) be the text encoder at layer l. We define the text- modality collapse degree (TCD) for text representations:
N N
1 U(I
M
N IN1K),
ATCD = Avgizs (ht( (()((t))))((()))) - Tw 1), (8)
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
where μ - 1 and l are text inputs, N is the num- ber of classes, and Tw is fixed-length constraint for text representations. The TCD measures the degree of separa- tion among text representations, reflecting their proximity to a simplex equiangular tight frame (ETF) structure. A lower ÅTCd indicates that text representations are closer to forming simplex ETE, which a desirable property for classification tasks. From Figure 2. we hypothesize that:
classes while simultaneously maintaining parameter effi- ciency through minimal augmentation of trainable weights.
4.3. Theoretical Analysis
We conduct systematic gradient- analysis of backprop- agated loss signals elucidate the mechanistic influence of our proposed methodology. specifically examining its op- timization trajectory perturbations under imbalanced class distributions characteristic of real-world training scenarios.
(1) A higher degree of neural collapse (lower AtCd) leads to stronger generalizability in text classification tasks.
Gradient analysis: The gradient of the task-specific loss Lask with respect to text representations is:
(2) The presence of data imbalance during the training pro- cess adversely impacts the degree of the neural collapse phenomenon, thereby compromising the model's general- ization performance capabilities.
D (1.)
nkk
" Em (13)
(PL())))) 1)
These hypotheses can be formally expressed as:
This gradient consists of an "intra-class cohesion" term and "inter-class repulsion" term. Under dlas--imbllanced learning paradigms, gradient signals for underrepresented minority classes become quantitatively dominated by the repulsion term, thereby creating geometric configuration bi- ases that may result in suboptimal feature space organization for rare categories. The gradient of TC loss is:
T - ATCD ++ Acc 4
(9)
where the ratio t counts of the least classes
counts of the most closses serves as a quantita- tive metric characterizing the severity of class distribution imbalance within the dataset. Based on these theoretical insights, we design our method to optimize the LLM's s rep- resentations towards a simplex ETF structure, even under class imbalance conditions.
(by(t)) at. Ne(t,). (14)
Σ (( Tw p)) 0t, imiuigtt
The TC loss explicitly adjusts text representations to ensure maximal angular separation, mitigating the adverse effects of imbalanced gradient updates. Impact on class imbal- ance: The TC loss provides an additional regularization term that is independent of class frequencies. This helps to balance the gradient updates across all classes:
4.2. Text-modality Collapse Regularization
We introduce the text-modality collapse (TC) loss and inte- grate with LoRA fine-tuning. The TC loss is defined:
Lrc- = Σ
T(va # us) ((()))))) - Tw - -x)2, (10)
1SKGIE
(1) For minor classes, compensates for the dominance of the inter-class repulsion term in Lassk.
subject to:
[1i(ta)22 S VTw, Vi (1,...,
(11)
(2) For major classes, it ensures their representations do not overly dominate the embedding space.
where B is the batch size, yi is the class of the i-th sample, and I[y # Uj) is an indicator function.
By integrating the TC loss into the LoRA fine-tuning pro- cess, we encourage the model to learn a more equitable representation space. This theoretical analysis supports our method's 's effectiveness in addressing the long-tail distribu- tion problem in educational data.
For implementation, we utilize LoRA to efficiently adapt the LLM weights Wo using trainable sank-decomposition matrices B E d and A E Rr The final weight matrix Wi is computed as the sum of the initial weights Wo and the product of these low-rank matrices BA.
5. Experiments
The combined loss function integrates the TC loss with the standard task-specific loss:
In this section, we present a comprehensive evaluation of NCAL through extensive experiments on large language models using LoRA (Hu et al., 2021) parameter-efficienn fine-tuning. Our investigation aims address the follow- ing research questions: Q1: How does NCAL perform compared to current state-of-the-art methods in addressing long-tail data distributions (see Section 5.2)? Q2: How do different data distribution ratios affect the ETF structure and TC regularization in models? What is the relationship be- tween data imbalance and the effectiveness of our proposed
L = Lrusk + XLre,
(12)
where À controls the influence of the TC regularization. During training, the LLM's hidden representations are af- fected by the updated weights, which are learned by mini- mizing this combined loss. This allows the LoRA updates to strategically direct the LLM toward achieving an opti- mally equidistant feature space configuration, thereby en- hancing the discriminative power for underrepresented tail
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
(a) TIMSS Math Word Problem Labeling (TMWPL)
(b) Productive Math
Tutoring Dialogue (PMTD)
Recall
Initiation- Initiatio-QQuesioniin Questionisttt (1-Q)
The sum of the interior angles of obtuse triangle the same as the sum of the interior angles of acute triangle.
Could you calculate plus 3? Tell me your calculation process.
B.
Initiation- Mintation-Hitss/// (I-H)
Formulate
The question ...... We are given Tom's age, us well as the age difference between the futher and Tom, and between the father and the grandfather. Can we the age relationships ...... to determine the grandfathrrs age?
In integer division, w when two numbers are divided, the quotient is 31 and the remainder is 6.
What is the smallest divisor? What is the dividend in this case?
Identify
Response- Refuse to Response (R-RR)
If a month's 's income is represented by positive number and ...... negative number, then Wang Xiaogiang's 's income 2,800 can be recorded as #, and expenditure... ......
huve ideas.
Respone-Simplistic Response (R-SR)
Yes, or okay.
Represent
Response- Response (R-FR)
In the new rural reconstruction, village built a path meters long. The construction team repaired 35 meters a day. After days of repair, there were still meters left. 2000, 5, there still meters left.
It well-established fact that the interior angles of any triangle sum to 180 degrees.
Inferences
Feedback-Feedine Feediing back performance (F-F)
To make 9. 1875=100,000, there are numbers that can be filled in
You'l re right.
the O.
6. B.5 C,4
Beedback-Instructimg (F-I)
Analyze
In this problem, we know ...... Since the grandfather 30 years older than the father, the grandfather''s age would be 35 30, which equals 65
The school needs to buy uniforms for 25 football players. The unit prices uniforms are 78 #/set, 85 /sct and 104 /sct. How much is the minimum ......? What is the maximum .....??
years old.
Unrelatedness (U)
Implement
Hi. Could you see my shared sereen?
Figure 3: Examples of TMWPL dataset with seven classes and PMTD dataset with eight classes,
method (see Section 5.3)? Q3: How does the NCAL model perform under diverse settings (see Section 5.3)?
each cognitive category are provided in Appendix A.
PMTD. The PMTD dataset captures one-on-one instruc- tional interactions between teachers and students, with a particular focus on guided teaching strategies used to sup- port students in solving challenging math problems. Draw- ing on the IRF framework (Sinclair & Coulthard, 2013) and scaffolding theory (Van de Pol et al., 2010), we collaborated with experts from the Education University to design an analytical framework comprising three primary categories and eight subcategories: Teacher Initiation (I-Q, I-H), Stu- dent Response (R-RR, R-SR, R-FR), Teacher Feedback (F-1, F-F), and Unrelated Content (U). Two trained annotators conducted the labeling process, achieving inter-rater re- liability of 86% (Cohen's 's Kappa), with any disagreements resolved through discussion. Among all categories, R-FR (feodback-relatee student responses) is the most frequent, accounting for 1,014 samples, while R-RR (repetition re- sponses) the least frequent, with only 62 samples. The test set includes 40 samples for each category, and the remain- ing data is used for training. Full definitions and illustrative examples are provided in Appendix B.
5.1. Experimental Setup
Datasets, Experiments are demonstrated on two person alized learning datasets. The TMWPL dataset is designed to evaluate students' cognitive abilities, while the PMTD dataset focuses on classifying teacher-student dialogue be- haviors. Examples from each dataset are shown in Figure 3. As illustrated in Figure 4, both datasets exhibit characteris- tic long-tail distribution in sample counts across categories.
TMWPL. The TMWPL dataset is developed based on the TIMSS (Trends in International Mathematics and Science Study) assessment framework, aiming to comprehensively evaluate students' mathematical cognitive abilities. It cov- seven core cognitive dimensions defined in the TIMSS Mathematics Cognitive Domains: Recall, Formulate, Iden- tify, Represent, Implement, Inferences, and Analyze. The dataset consists of math word problems designed for stu- dents in grades 3 to 6, systematically annotated by profes- sional education experts. Each problem was independently labeled by three to five annotators, with the final label deter- mined by majority voting. Among the dimensions, Recall has the highest number of samples (5,045), while Analyze has the fewest (216). The test set includes 50 samples per cognitive dimension, with the remaining data used for train- ing. Detailed definitions and representative examples of
Baselines. our evaluation of various large language mod- els fine-tuned with LoRA to assess our approach, we have primarily selected three categories of models. This selection was guided by our dataset, which predominantly consists of Chinese content and focuses on the domain of mathemat- ics. The models chosen for our study include: (1) Chinese-
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
TMWPL Dataset
PMTD Dataset
Number of Samples
Number Samples
1000
4000
750
500
2000
250
Recall
Formulate
Identify
Represent
Implement
Inferences
Analyze
R-FR
1-Q
R-SR
F-1
I-H
R-RR
Classes
Classes
Figure 4: Sample distribution across categories in TMWPL and PMTD. clear long-tailed distribution observed in both.
focused instruction models: Qwen2.5 instruct (7B, 14B) and DeepSeek v2 chat. (2) English-focused instruction models: 1lama3.1 instruct and Vicunal.5 (7B, 13B). (3) Logic and math-oriented models: Qwen2.5-math and DeepSeek-mati
with intermediate steps at 0.03, 0.15, and 0.25. Figure lustrates that model accuracy improves with more balanced data distributions. However, decline at the 0.25 ratio due to insufficient data volume for effective fine-tuning. Our analysis reveals strong correla- tion between the ATCD values and classification accuracy across all model variants. Specifically, larger ATCD values consistently correspond to lower accuracy scores, indicating that the degree of deviation from the ideal ETF structure directly impacts model performance. This finding suggests that maintaining more uniform text representation struc- ture is crucial for achieving optimal classification results, particularly in long-tail scenarios.
observed performance
Implementation details. Our model training implements LoRA-based PEFT, using the LLaMA Factory (Zheng et al., 2024) automated training and inference pipeline as a foun- dation. All experiments were conducted on a system with eight NVIDIA A100 GPUs (80GB each).
5.2. Overall Performance
In Table I, we report the overall performance our NCAL and baselines on two long-tail datasets. According to Ta- ble 1, we have the following findings: (1) Our NC algorithm demonstrates consistently superior performance across Chi- nese and English large language models, including those specifically enhanced for reasoning tasks. (2) Our 7B NCAL model achieves remarkable results by outperforming models ranging from 7B to 14B, including both dense and Mixture- of-Expsrts (MoE) architectures. This notable achievement across all evaluated benchmarks demonstrates that our ap- proach can deliver state-of-the-an performance while main- taining computational efficiency through smaller param- eter. (3) We also observed an interesting phenomenon: reasoning- speocili-se models excel in mathematical prob- lem classification tasks, whereas instruction-tune models outperform dialogue classification scenarios. This finding underscores the importance of task-specific model optimiza- tion for achieving optimal performance.
Base or instruct-tuned LLMs. We further investigated the performance differences between the base model and the instruction-tuned model. As shown in Table 2, the instruction-tun models consistently outperformed the base model on both test datasets, achieving higher accuracy. Moreover, even after applying LoRA fine-tuning to our dataset, the base model still exhibited pronounced ten- dency to generate uncontrolled and meaningless outputs. Therefore, we ultimately adopted an evaluation strategy that selects only the top tokens. This experiment further vali- dates motivation: optimizing models long-tail data may introduce negative feedback, highlighting the impor- tance of properly handling long-tail samples during training.
Configuration of prompts. the context of instruction- tuned models, the design of prompts significantly impacts their zero-shot capabilities, particularly distinguishing between directly generating categories and generating cate- gories after applying Chain of Thought (COT) reasoning. To address this issue, we investigated the influence of including prompt that specifies classification tasks on model per- formance. Specifically, the optimal model input combined task descriptions with dataset features, while the scenario without prompts only included the dataset features (detailed prompt configurations can be found in Appendix C). Our results in Tabel 2 indicated that, in the scenario where only
5.3. Ablation Study
To validate the design of NCAL, we conducted ablation study focusing data ratio configurations, the use of Base or Instruct-tuned LLMs, and the configuration of prompts.
Data ratio analysis. We examined data ratios ranging from 0.03 (minimum to maximum class quantity ratio) to 0.25,

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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
Table I: Experiments on the two long-tail datasets, TMWPL and PMTD. The best results are highlighted in bold.
TMWPL.
PMTD
Model
Type
Parameters
Acc
Macro-F1
Acc
Macro-F1
Qwen2.5- (Qwen al., 202.5)
Chinese-focused
7B
61.14
60.59
65.94
61.46
Qwen2.5-Instruct (Qwen al., 2025)
Chinese-focused
14B
65.14
65.02
66.56
61.91
DeepSeck-V2-Lit---00 V2-Lte--2 (Liu et al., 2024)
Chinese-focused
16B
65.14
66.17
61.56
53.78
NCAL-Qwen2.5-Instree
Chinese-focused
7B
74.86
75,58
70.31
67.43
Vicunal.5 (Chiang et al., 2023)
English-focused
7B
63.14
62.61
61.88
53.55
Llama3. I-Instruct (Dubcy al., 2024)
English-focuset
8B
61.43
61.73
65.62
60.41
Vicuna (Chiang et al., 2023)
English-focused
13B
67.43
68.26
62.50
56.31
NCAL-Llama3.1
English-focee focused
8B
69.43
69.14
67.50
64.92
Qwen2.5 @wen2.5-Matth Math--Insththee (Qwen et al., 2025)
Logic and math-oriented
67.71
68.77
59.69
51.14
Deepseek-Math- (Liu et al., 2024)
Logic and math-oriented
7B
67.71
68.20
59.38
51.37
SCCL-Qwen2.5-- Math- Instrunstttut
Logic and math oriented
math-oriented
7B
74.86
75.51
63.12
58.32
1.17
76.36
1.4
1.34
75.00
1.4
1.34
72
1.3
76.86
1.0
1.2
1.0
1.00
0.8
66
72.00
1.0
0.94
0.9
63.14
61:43
0.41
60
0.83
0.6-
66.88
0.51
Owen
Owen-Math
Liama
Ours
Owon
Owen
Curs
Owen
Ours
Models
Models
Models
[A) TCD and Ace Variations under
0.03
(b) A and
Ane Ace Variations under 0.15
(CD and Acc Varintions under 0.25
Figure 5: Comparison of ATCD with model performance on the TMWPL dataset, where ATCD is calculated based on Lrc. indicates the degree of data imbalance, with r 0.25 for less imbalance and 0.03 for high imbalance. Our findings show that, overall, as data balance improves, classification performance is significantly enhanced.
Table 2: Ablation study of prompt in NCAL and LLMs.
6. Conclusion
Model
TMWPL-Acc
PMTD-Ace
In this paper, to the best of our knowledge, we are the first paper to study the long-tail problem for data-based person- alized learning. To address the real-word challenge, we propose neural-collapse-adaane
Qwen2.5-7B (wen2.557B-IInstrrce Instruct
74.86
70.31
w/o instruction sft
74.00 (0.86 L)
70.00 (0.31 !)
w/o prompt
75.00 (0.14
67.19 (3.12
personalized Learn- ing (NCAL). NCAL optimizes feature representations to conform to the simplex ETF structure and introduces Text- modality Collapse (TC) regularization enhance the distri- bution of text embeddings within the LLM representation space. Through comprehensive experiments on two bench- mark datasets (TMWPL and PMTD), we demonstrate three key findings: (1) NCAL exhibits consistent superior perfor- mance across various I.IMs,, including both Chinese and English models, validating its versatility; (2) Our 7B pa- rameter NCAL achieves state-of-the-ar performance while maintaining computational efficiency, outperforming larger models up to 14B parameters; (3) The effectiveness of NCAL remains stable across different prompt configura- tions and model architectures. Additionally, our ablation study confirms that NCAL effectively mitigates class imbal- ance and enhances model generalization ability, making promising solution for practical personalized learning.
w/o prompt NCAL
73.71 (1.15
62.50 (7.81
Llama3. Instruct
69.43
67.50
w/o instruction sft
68.29 (1,14 L)
66.87 (0.63
w/o prompt
69.71 (0.28 1)
62.19 (5.31 I)
w/o prompt NCAL
67.14 (2.29
56.25 (11.25
Qwen2.5 1-0.5-5--h----- 7 Instructt
74.86
63.12
w/o instruction sft
72.00 L)
62.50 (0.62
w/o prompt
71.43 (3.34
60.31 (2.81 L)
w/o prompt NCAL
71.43 (3.34
58.44 (4.68
the dataset feature inputs without any extra prompts un- derwent LoRA fine-tuning, all models exhibited a notable decline in performance. However, after applying our pro- posed NCAL method, instruction-related models showed slight improvement in the TMWPL dataset. Despite a minor decline in performance on the PMTD dataset, our NCAL method generally demonstrated relative stability under the influence of varying prompts.
8

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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
Acknowledgements
Cheng, S., Liu, Q., Chen, E., Zhang, K., Huang, Z., Yin, Y., Huang, X., and Su, Y. Adaptkt: A domain adaptable method for knowledge tracing. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining WSDM, pp. 123-131, 2022.
This work was supported in part by the National Natural Sci- ence Foundation of China (No. 62477012) and the Natural Science Foundation of Shanghai (No. 232R 1418500). The authors would like to express sincere gratitude these fund- ing agencies for their support. The opinions and conclusions presented in this paper are solely those of the authors and do not necessarily reflect the views of the funding organizations. Sen Cui would like to acknowledge the financial support received from the Shuimu Tsinghua scholar program.
Chiang, W.-L., Li, Z., Lin, Z.,, Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023. URL https: lwsys.orr//laii.....o... org/blog/ 2023- 03-300----00-00 wwwa/.
Impact Statement
Choi, S.-K., Huang, J.-X., and Kwon, O.-W Semi- automatic construction of bidirectional dialogue dataset for dialogue- based reading comprehension tutoring sys- tem using generative ai. In International Conference on Intelligent Tutoring Systems, pp. 305-313. Springer, 2024.
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.
Cohn, C., Hutchins, N., Le, T., and Biswas, G. A chain- of-thought prompting approach with I1ms for evaluating students' formative assessment responses in science. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 38, pp. 23182-231999 2024.
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
Appendix
We provide supplementary details to enhance the understanding our research. Specifically, focuses on dataset class descriptions and experimental prompt design, offering concise insights into the methodological nuances and experimental setup that support our main paper's findings.
A. The TMWPL Dataset label description
Table 3: Mathematics Cognitive Domains
Level
Label Type
Label Description
Recall
Recall definitions, terminology, number properties, units of measurement, geometric properties, and notation (e.g., a x b ab, a a 3a). Determine efficient/appropriaae operations, strategies, and tools for solv- ing problems.
Formulate
Identify
Identify numbers, expressions, quantities, and shapes Recognize when entities are mathematically equivalent Read information from graphs, tables, texts, or other sources.
D
Represent
Represent data in tables or graphs; create equations, inequalities, geo- metric figures, or diagrams that model problem situations; and generate equivalent representations for a given mathematical entity or relation- ship.
Implement
Implement suitable strategies and operations to produce solutions to problems.
Inferences
Make valid inferences based on information and evidence.
Analyze
Analyze, describe, or use relationships among numbers, expressions, quantities, and shapes.
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
B. The PMTD Dataset label description
Table 4: Dialogue Actions, Definitions, and Examples
Dialogue Level
Dialogue Action Questioning (I-Q)
Definition
Example
It involves asking students questions that require active linguistic and cognitive answer.
Could you calculate 5 plus 3? Tell me your calculation process.
Initiation (I)
Hints (I-H)
It entails the provision of clues or sugges- tions by the teacher help the student go forward. The teacher deliberately does not supply the entire solution detailed in- structions under such circumstances.
The question asks us to calculate the grandfather's age. We are given Tom's age, as well as the age differ- ence between the father and Tom, and between the father grandfather. Can we use the age lationships between the father Tom, and the father and the grandfa- ther. to determine the grandfather's age?
the
Refuse to Response (R-RR)
Refuse answer questions remain silent without providing a response.
have ideas choose to remain silent. Mm, yes, okay.
Response (R)
Simplistic Response (R-SR)
The responses simple the point of lacking depth thoroughness.
Factual Response (R-
The answers provided are characterized by their factuality, reliance memory, and explanatory nature.
It a well-establissed fact that the interior angles of any triangle to 180 degrees, You'le 're're right / You smart.
FR)
Feeding back on per- formance (F-F)
It involves the provision of information garding the student's performance to the student him/herself.
Feedback (F)
Instructing (F-I)
It involves the teacher telling students what to do or explanation of how something must be done and why.
this problem. we know the age difference between the father and Tom 30 years. By taking Xiao Tom's age, which years old, and adding 30 years, we find that the fa- ther currently 35 years old. Since the grandfather is 30 years older than father, the grandfather's age would be 35 + 30, which equals 65 years old. Hi.
Unrelatedness (U)
Unrelatedness (U)
It does not directly relate to teaching and learning OR the topic under study.
Could you see my shared screen?
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Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
C. Prompt settings
Prompts of TMWPL Tuning.
Now there problem with the content question please determine which category this problem belongs from following categories:
Recall: Recall definitions, terminology, number properties, units of measurement, geometric properties, and notation (e.g., x b ab, + a
Formulate: Determine efficient/appropriate operations, strategies, and tools for solving problems.
Identify: Identify numbers, expressions, quantities, and shapes Recognize when entities are mathematically
equivalent Read information from graphs, tables, texts, other sources.
Represent: Represent data tables or graphs; create equations, inequalities, geometric figures, diagrams that model problem situations; and generate equivalent representations
given mathematical entity or relationship.
Implement: Implement suitable strategics and operations to produce solutions to problems.
Inferences: Make valid inferences based information and evidence.
Analyze: Analyze, describe,
relationships among numbers, expressions, quantities, and shapes.
Note Please directly output the category the target problem, with no additional output.
Figure 6: Example Prompts of Model Tuning in TMWPL Dataset.
Prompts of PMTD Tuning.
There is dialogue between teacher and student (a small part extracted from it). Please classify the target dialogue according the classification requirements.
Partial Dialogue Context
Student: XXXX.
Tutoring Teacher: XXXX? Student: XXX!
Dialogue to be Classified Tutoring Teacher: XXX?
Classification Requirements
I-Q: involves asking students questions that require an active linguistic cognitive answer.
I-H: It entails the provision of clues or suggestions by the teacher to help the student go forward. The teacher
deliherately does not supply the entire solution or detailed instructions under such circumstances. F-F: It involves the provision of information regarding the student's performance the student him/herself.
F-1: involves teacher telling the students what do explanation of how something must done
why.
R-RR: Refuse to answer questions or remain silent without providing a response.
R-SR: The responses simple to the point of lacking depth or thoroughness.
R-FR: The answers provided characterized their factuality, reliance on memory, and explanatory nature.
does not directly relate teaching and learning OR topic under study.
Note
Please directly output the type the target dialogue, with no additional output.
Figure 7: Example Prompts Model Tuning in PMTD Dataset.
14


Reviews:
[{"review_id": "y6SOENpWYh", "reviewer": "Reviewer_FPDE", "summary": "The paper proposes a new method called Neural-Collapse-Advanced Personalized Learning (NCAL) to address the limitations of data-based personalized learning approaches that assume well-annotated benchmarks. In reality, these benchmarks often have long-tail distributions that affect model accuracy. NCAL introduces a regularization technique, Textmodality Collapse (TC), to optimize text embedding distributions in LLMs. The method is model-agnostic, allowing for its integration with various architectures. The authors demonstrate that NCAL improves performance across tasks and mitigates class imbalance, achieving new state-of-the-art results in the field.", "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": "> **Q1: However, the assertion that NCAL is model-agnostic would benefit from further clarification.**\n\n**A1:** NCAL is designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure by introducing TC regularization to optimize text distribution. As a result, NCAL serves as a plug-and-play framework. To further demonstrate its versatility, we present experimental results in Table 2, showcasing NCAL’s performance when integrated with different LLM architectures, such as Qwen2.5 and Llama3.1.\n\n> **Q2: More detailed examples of its impact on real-world class imbalance problems would provide further credibility to the claims.**\n\n**A2:** Thank you for your valuable suggestion. We visualized the class imbalance statistics of the personalized learning data collected from real-world scenarios used in this paper (see Q5). We also conducted experiments on other open-source datasets to evaluate NCAL’s performance under different class imbalance datasets. These results further demonstrate its effectiveness in practical applications (see Q3).\n\n> **Q3: However, incorporating additional personalized learning baselines or datasets.**\n\n**A3:** As suggested by the reviewer, we add a comprehensive study comparing four LLM methods and three open-source datasets, including: [IITJEE NEET AIIMS Students Questions Data](https://www.kaggle.com/datasets/mrutyunjaybiswal/iitjee-neet-aims-students-questions-data); [MathDial](https://github.com/eth-nlped/mathdial/tree/main); [DialogID](https://github.com/ai4ed/DialogID/tree/main). These results demonstrate the effectiveness of NCAL in comparison with more baselines under various long-tail personalized learning datasets. These results are shown below:\n\n| Model                       | Parameters | Dataset   | Acc       |\n| --------------------------- | ---------- | --------- | --------- |\n| DeepSeek-R1-Distill-Qwen-7B | 7B         | TMWPL     | 60.86     |\n| phi-4                       | 14B        | TMWPL     | 72.00     |\n| DeepSeek-V2 | 16B | TMWPL | 65.24 |\n| Qwen2.5-Instruct | 7B  | TMWPL | 61.14 |\n| **NCAL-Qwen2.5-Instruct**   | 7B    | TMWPL | **74.86** |\n\n| Model                 | Parameters | INASQD-Acc  | MathDial-Acc   | DialogID-Acc |\n| --------------------- | ---------- | -------- | ----- | --------------------- |\n| Qwen2.5-Instruct      | 7B         | 94.50 | 51.50        | 85.15 |\n| **NCAL-Qwen2.5-Instruct** | 7B       | **96.50** | **57.00** | **87.46** |\n| Llama3.1-Instruct | 8B | 95.50      | 51.00 | 85.75 |\n| **NCAL-Llama3.1-Instruct** | 8B       | **98.00** | **53.50** | **87.34** |\n| Qwen2.5-Math-Instruct | 7B        | 95.00 | 52.00 | 84.53 |\n| **NCAL-Qwen2.5-Math-Instruct** | 7B | **96.50** | **57.50** | **86.03** |\n\n> **Q4: Would it be possible to include additional visualizations?**\n\n**A4:** In Figure 1, we present the visualization to demonstrate the effectiveness of our method in improving the class feature space. The arrows indicate the direction of class centers, while the points represent sample features, with colors corresponding to the classes. In Figures 1 (a) and (c), it is visually evident that the baseline suffers from severe class representation imbalance. In (b) and (d), our method more uniformly learns the information for each class.\n\n> **Q5: However, more information should be provided, such as the number of samples in each category, to give a more comprehensive view of the data distribution.**\n\n**A5:** We thank the reviewer for pointing out this issue. We will include the statistical data in the final version. The data distribution is as follows, and it is clear that the number of samples in different categories exhibits a distinct long-tail distribution.\n\n| Classes  | Recall | Formulate | Identify | Represent | Implement | Inferences | Analyze |\n| ------- | ------ | --------- | -------- | --------- | --------- | ---------- | ------- |\n| TMWPL samples | 5045   | 1283      | 717      | 711       | 405       | 235        | 216     |\n\n| Classes  | R-FR | I-Q  | R-SR | F-I  | F-F  | U    | I-H  | R-RR |\n| ------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| PMTD samples | 1014 | 880  | 737  | 518  | 510  | 389  | 104  | 62   |\n\n> **Q6: It is recommended to include experiments comparing these methods with the approach proposed in this paper.**\n\n**A6:** We agree with the reviewer that data augmentation is indeed an effective approach for addressing long-tail distributions. However, data augmentation requires strict control over the augmentation strategy, as well as the quality and diversity of the generated samples. In contrast, NCAL has broader applicability. It is important to note that we sincerely cannot provide a baseline comparison with the paper ``Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation,” as the augmentation method used in that work is based on ID-type data (e.g., 0, 1, 2), which cannot be learned using LLM encoding.", "claims_and_evidence": "The claims regarding NCAL’s enhancement of model performance and its potential to address class imbalance are well-supported by experiments. However, the assertion that NCAL is model-agnostic would benefit from further clarification, particularly in terms of its practical application to various architectures. Additionally, more detailed examples of its impact on real-world class imbalance problems would provide further credibility to the claims.", "methods_and_evaluation_criteria": "The proposed method, NCAL, is well-aligned with the problem of data-driven personalized learning, particularly in addressing the long-tail distribution challenge in real-world benchmarks. The evaluation is conducted on two benchmark datasets. Extensive experiments demonstrate its effectiveness in improving model performance and mitigating class imbalance, making the evaluation criteria appropriate for the problem at hand.", "theoretical_claims": "I have verified the theoretical claims in the paper, and the proofs are correct and well-founded.", "experimental_designs_or_analyses": "The experimental design of this paper aligns with the standards of data-driven personalized learning in terms of data selection, baseline setup, and implementation details. However, incorporating additional personalized learning baselines or datasets would further enhance the completeness and comprehensiveness of experiments.", "relation_to_broader_scientific_literature": "This paper builds upon prior research in neural collapse and representation learning by introducing NCAL, which optimizes feature representations through the simplex ETF structure and enhances text embedding distribution with TC regularization. However, the authors should improve the writing to enhance the clarity of the paper.", "essential_references_not_discussed": "As far as I understand, the key references have already been covered.", "other_strengths_and_weaknesses": "## Strengths\n- The introduction of Textmodality Collapse (TC) regularization is a unique contribution that optimizes the distribution of text embeddings within the LLM representation space, significantly enhancing performance.\n\n- NCAL is model-agnostic, meaning it can be integrated with various architectures and methods, making it highly adaptable for different use cases and ensuring wide practical applicability.\n\n\n## Weaknesses\n- Personalized learning is a crucial research topic in intelligent education. However, due to various constraints in real-world environments, the collected data often exhibit severe long-tail distributions. The authors propose the NCAL method, which leverages the concept of neural collapse and introduces TC regularization to constrain the model, mitigating the impact of long-tail distributions. Would it be possible to include additional visualizations, rather than relying solely on numerical metrics in Tables, to more intuitively demonstrate the effectiveness of the proposed method in enhancing category representation space?\n\n- This paper uses the TIMSS and PMTD datasets to evaluate the effectiveness of this method. However, more information should be provided, such as the number of samples in each category, to give a more comprehensive view of the data distribution.\n\n- Recently, some works [1] have used data augmentation to mitigate the impact of data-related issues. It is recommended to include experiments comparing these methods with the approach proposed in this paper.\n\n[1] Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation", "other_comments_or_suggestions": "Please see other reviews", "questions_for_authors": "I believe the authors have addressed an important and interesting problem. I am particularly concerned about whether the proposed method can be better visualized to clearly demonstrate how it addresses the long-tail problem in the representations. If this can be done, I would be willing to increase my score.", "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": "cNOwHd9wMi", "reviewer": "Reviewer_kS9o", "summary": "Personalized learning has gained significant attention due to its ability to address individual student needs. Still, many methods rely on the assumption of high-quality benchmarks, which are often unrealistic. To overcome this, the authors proposed NCAL, which utilizes a TC regularization to adjust the distribution of text embeddings within LLMs. NCAL is designed to be compatible with multiple models and shows impressive results in improving generalization and overcoming class imbalance. Experiments confirmed its ability to achieve SOTA performance.", "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": "> **Q1: While the paper provides evidence for the performance improvements and generalization ability of NCAL, the claim of it being “model-agnostic” remains somewhat unclear.**\n\n**A1:** NCAL enhances feature learning by aligning with the simplex equiangular tight frame (ETF) structure through Text-modality Collapse (TC) regularization, which optimizes text distribution. This design ensures NCAL's compatibility with various models, making it a versatile plug-and-play framework. As evidenced in Table 2, we demonstrate its effectiveness across multiple LLM architectures, including Qwen2.5 and Llama3.1, further supporting its model-agnostic property.\n\n> **Q2: More specific details on its application to different models and a deeper explanation of how it addresses class imbalance will help to reinforce these claims.**\n\n**A2:** We have further clarified the learning mechanism of NCAL. Specifically, NCAL aims to learn features that conform to the Equiangular Tight Frame (ETF) structure by introducing Text-Modality Collapse (TC) regularization. This optimization improves the distribution of text embeddings within the representation space of large language models (LLMs), thereby enhancing the robustness and generalization ability of LoRA fine-tuned LLMs under long-tail data distributions.\n\n> **Q3: The authors should add ablation experiments to further discuss the impact of different imbalance ratios.**\n\n**A3:** As suggested by the reviewer, we conducted a comprehensive comparative study on different imbalance ratios. Specifically, we evaluated the model under imbalance ratios of 0.03, 0.05, 0.10, 0.20, and 0.25. The experimental results are shown below, indicate that as the imbalance ratio decreases from 0.03 to 0.25, the model's performance improves accordingly, increasing from 74.86 to 76.86.\n\n| Data Redio (NCAL-Qwen2.5-Math-Instruct) | TMWPL-Acc |\n| - | - |\n| 0.03 | 74.86 |\n| 0.05 | 75.29 |\n| 0.10 | 77.14 |\n| 0.15 | 78.00 |\n| 0.20 | 78.29 |\n| 0.25 | 76.86 |\n\n> **Q4: Different forms of prompts can significantly impact the performance of LLM models. In Section 5.3, the authors examined the effect of using or not using prompts on the experimental results but did not further analyze the performance differences across different prompt formats.**\n\n**A4:** Following the reviewer's suggestion and related work, we validate our method with more prompt forms, including base prompt (see Table 5 in our manuscript), w/o prompt, few-shot (introducing a few reference samples in the prompt), role play (personalized scene prompts), and only class name（see Table 4）. The experimental results are shown below:\n\n| Model  | base prompt | w/o prompt | few-shot | role play | only class name |\n| ------- | ----------- | ---------- | -------- | ---------------- | -------------------- |\n| NCAL-Qwen2.5-Instruct      | 74.86       | 75.00      | 74.86    | 74.86 | 74.00  |\n| NCAL-Llama3.1-Instruct     | 69.43       | 69.71      | 68.29    | 68.57  | 69.43  |\n| NCAL-Qwen2.5-Math-Instruct | 74.86       | 71.43      | 72.57    | 74.86            | 72.00                |\n\nThe experimental results show that different prompt forms have little impact on the model. The possible reason is that our method, using the TC regularization approach, can learn a good category feature space that is not overly influenced by the prompt during training and inference. This experiment further supports our motivation.\n\n> **Q5: I recommend including more baseline methods, for data-driven personalized learning to further strengthen the comprehensiveness of the study.**\n\n**A5:** We thank the reviewer for pointing out this issue. We conducted extensive experiments based on the papers recommended by the reviewer. Unfortunately, we found that the codes for the methods in [1] and [3] have not been released, making it difficult for us to reproduce these methods within the short time frame for the rebuttal. Additionally, [2] is a survey paper, from which we selected the most recent representative state-of-the-art (SOTA) works for performance comparison. The experimental results are as follows, showing that our method still achieves SOTA performance compared to other SOTA baselines under the more challenging TMWPL dataset.\n\n| Model | Llama3.1-Instruct|gemma-2-9b-it | internlm3-8b-instruct | **NCAL-Qwen2.5-Instruct**|\n| ------- | ---------- | --------- | --------- | --------|\n| Parameters | 8B | 9B | 8B | 7B |\n| Dataset | TMWPL | TMWPL | TMWPL| TMWPL|\n| Acc |61.43 |72.29|72.86| **74.86**|\n\n\n> **Q6: The concept of equidistant text representation (ETR) regularization needs further explanation. Does it share the same meaning as Textmodality Collapse (TC)?**\n\n**A6:** Thank you for the reviewer's comment. In our manuscript, the meaning of ETR is the same as TC, and we will correct this error in the final version.", "claims_and_evidence": "While the paper provides evidence for the performance improvements and generalization ability of NCAL, the claim of it being “model-agnostic” remains somewhat unclear. More specific details on its application to different models and a deeper explanation of how it addresses class imbalance will help to reinforce these claims.", "methods_and_evaluation_criteria": "yes", "theoretical_claims": "yes, no issues found", "experimental_designs_or_analyses": "yes", "relation_to_broader_scientific_literature": "The method contributes to the broader scientific literature by addressing the long-tail problem in data-based personalized learning.", "essential_references_not_discussed": "no", "other_strengths_and_weaknesses": "## Strengths\n- The proposed method can effectively tackle the problem of long-tail distributions in real-world data, which often affects the performance of models, making the method highly relevant for practical applications.\n\n- NCAL shows strong results in improving existing models, achieving state-of-the-art performance while also enhancing the model’s ability to deal with class imbalance.\n\n\n## Weaknesses\n- The authors should add ablation experiments to further discuss the impact of different imbalance ratios.\n\n- Different forms of prompts can significantly impact the performance of LLM models. In Section 5.3, the authors examined the effect of using or not using prompts on the experimental results but did not further analyze the performance differences across different prompt formats.\n\n- I believe the authors are tackling an important and practical issue in the field of personalized learning. I recommend including more baseline methods，e.g., [1-3]， for data-driven personalized learning to further strengthen the comprehensiveness of the study.\n\n[1] Askarbekuly, N. and Aniciˇ c, N. Llm examiner: automating assessment in informal self-directed e-learning using chatgpt. Knowledge and Information Systems, pp. 1–18,2024.\n\n[2] Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru,B., and Adewusi, O. E. Ai in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2):261–271, 2024.\n\n[3] Chang, L.-H. and Ginter, F. Automatic short answer grading for finnish with chatgpt. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 23173–23181, 2024.", "other_comments_or_suggestions": "Please refer to Other Strengths And Weaknesses", "questions_for_authors": "In Section 6, the concept of equidistant text representation (ETR) regularization needs further explanation. Does it share the same meaning as Textmodality Collapse (TC)?", "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": "AKn5OtznGl", "reviewer": "Reviewer_jYxM", "summary": "Personalized learning, particularly data-driven approaches, faces challenges due to long-tail distributions in real-world benchmarks, which affect model performance. To address this, the authors propose NCAL (Neural-Collapse-Advanced Personalized Learning), which leverages Textmodality Collapse (TC) regularization to optimize text embeddings in large language models (LLMs). NCAL is model-agnostic, compatible with various architectures, and improves generalization while mitigating class imbalance. Extensive experiments show that NCAL achieves state-of-the-art performance and enhances existing methods.", "questions": "", "limitations": "", "rating": 3, "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": "> **Q1: However, the claim about mitigating class imbalance would benefit from further discussion or visualizations to highlight the improvement in class distribution. More visual evidence could strengthen the claim of enhanced category representation.**\n\n**A1:** We appreciate the reviewer’s comments. Below, we present the statistical distribution of the data used in our paper. Please note that these two datasets were collected from real-world scenarios, with PMTD containing eight categories and TMWPL comprising seven categories.\n\n| Classes number  | Class 1 | Class 2  | Class 3 | Class 4 | Class 5  | Class 6 | Class 7  | Class 8 |\n| ------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| Classes  | R-FR | I-Q  | R-SR | F-I  | F-F  | U    | I-H  | R-RR |\n| PMTD samples | 1014 | 880  | 737  | 518  | 510  | 389  | 104  | 62   |\n| Classes  | Recall | Formulate | Identify | Represent | Implement | Inferences | Analyze |\n| TMWPL samples | 5045   | 1283    | 717   | 711  | 405   | 235  | 216 |\n\n> **Q2: However, the mathematical formulation and the justification behind the introduction of TC regularization could benefit from further explanation, and how it mitigates the long-tail distribution issue.**\n\n**A2:** The goal of NCAL is to learn a simple ETF through the method of neural collapse, to construct a uniformly partitioned category feature space. To achieve this, we introduce TC regularization, which aims to constrain the feature distribution of category partitioning learned from text data across different categories, effectively addressing the long-tail distribution problem.\n\n> **Q3: However, incorporating additional experiments to evaluate the method’s performance under imbalance rates and experimental details.**\n\n**A3:** On the TMWPL dataset, we conducted experiments based on the Qwen2.5-Math-Instruct architecture, evaluating the model's performance under different levels of long-tail distribution, ranging from low to high imbalance ratios (0.03, 0.08, 0.13, 0.18, 0.25). The corresponding performance results were 74.86, 75.01, 76.45, 78.06, and 76.86. The experimental results indicate that as the degree of long-tail distribution weakens, the model's performance improves.\n\n> **Q4: Could you further explain Equation (10)? Are i and j texts from the same category or different categories?**\n\n**A4:** Yes, I will provide a more detailed explanation for Equation (10). The texts $i$ and $j$ come from different categories, and the goal is to constrain the feature representations of samples from different categories to approach a uniform distribution, i.e., $1/(N-1)$, where $N$ represents the number of categories.\n\n> **Q5: Although the authors have validated the effectiveness of their method on two representative data-driven personalized learning datasets, it is recommended to conduct experiments on additional open-source datasets to more comprehensively assess the method’s generalization ability and robustness.**\n\n**A5:** As recommended by the reviewer, we have included a comprehensive study comparing three open-source datasets: [IITJEE NEET AIIMS Students Questions Data](https://www.kaggle.com/datasets/mrutyunjaybiswal/iitjee-neet-aims-students-questions-data), [MathDial](https://github.com/eth-nlped/mathdial/tree/main), and [DialogID](https://github.com/ai4ed/DialogID/tree/main). The results, presented below, highlight the effectiveness of NCAL compared to additional baselines. The experimental results indicate that our method consistently outperforms others across all datasets.\n\n| Model | Qwen2.5-Instruct | NCAL-Qwen2.5-Instruct | Llama3.1-Instruct | NCAL-Llama3.1-Instruct | Qwen2.5-Math-Instruct | NCAL-Qwen2.5-Math-Instruct |\n|-------|-------|-------|-------|-------|-------|-------|\n| Parameters | 7B | 7B | 8B | 8B | 7B | 7B |\n| INASQD-Acc | 94.50 | **96.50** | 95.50 | **98.00** | 95.00 | **96.50** |\n| MathDial-Acc | 51.50 | **57.00** | 51.00 | **53.50** | 52.00 | **57.50** |\n| DialogID-Acc | 85.15 | **87.46** | 85.75 | **87.34** | 84.53 | **86.03** |\n\n> **Q6:** The impact of different types of prompts on performance?\n\n**A6:** Thank you for the reviewer’s suggestion. Based on the prompts considered in our manuscript, we have referred to related work and explored additional prompt forms, including base prompt (see Table 5 in our manuscript), w/o prompt, few-shot (introducing a few reference samples in the prompt), role play (personalized scene prompts), and only class name. The experimental results are shown below. The results demonstrate that our method maintains strong robustness and generalization across different prompt settings.\n\n| Model | base prompt | w/o prompt | few-shot | role play | only classname |\n| -- | -- | -- | -- | -- | -- |\n| NCAL-Qwen2.5-Instruct | 74.86 | 75.00 | 74.86 | 74.86 | 74.00 |\n| NCAL-Llama3.1-Instruct     | 69.43 | 69.71 | 68.29 | 68.57 | 69.43 |\n| NCAL-Qwen2.5-Math-Instruct | 74.86 | 71.43 | 72.57 | 74.86 | 72.00 |", "claims_and_evidence": "The claims in the submission are generally well-supported by clear evidence, with experiments demonstrating NCAL’s effectiveness in addressing long-tail distributions and class imbalance. However, the claim about mitigating class imbalance would benefit from further discussion or visualizations to highlight the improvement in class distribution. More visual evidence could strengthen the claim of enhanced category representation.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria are appropriate for the problem. The use of NCAL to address the long-tail distribution problem in personalized learning is a novel approach. The experiments on benchmark datasets (TIMSS and PMTD) are suitable for evaluating the effectiveness of the proposed method in real-world scenarios.", "theoretical_claims": "Yes, I have reviewed the theoretical claims in the paper. The proofs for the claims related to the effectiveness of the NCAL method appear to be logically sound and well-supported by the experiments. However, the mathematical formulation and the justification behind the introduction of Textmodality Collapse (TC) regularization could benefit from further explanation, particularly regarding its impact on the feature representations and how it mitigates the long-tail distribution issue. More clarification in this regard would strengthen the theoretical foundation of the approach.", "experimental_designs_or_analyses": "The experimental design in this paper is generally sound, with a clear setup and methodology. However, incorporating additional experiments to evaluate the method’s performance under varying imbalance rates and providing more details on the experimental setup, such as dataset characteristics and imbalance ratios, would offer deeper insights into the model’s robustness and generalizability.", "relation_to_broader_scientific_literature": "This paper builds upon prior research in personalized learning and long-tail distribution challenges, addressing limitations in existing benchmarks that assume high-quality, well-annotated data. By leveraging the simplex ETF structure and introducing Textmodality Collapse (TC) regularization, NCAL refines text embeddings within LLMs, aligning with recent advancements in feature representation learning.", "essential_references_not_discussed": "The paper provides a solid discussion of related works.", "other_strengths_and_weaknesses": "## Strengths\n- The paper introduces NCAL, which leverages the simplex ETF structure and TC regularization to mitigate class imbalance, improving model generalization in personalized learning.\n\n- The proposed method is model-agnostic and demonstrates superior performance across multiple architectures and benchmark datasets, achieving state-of-the-art results.\n \n\n## Weaknesses\n- Could you further explain Equation (10)? Are $i$ and $j$ texts from the same category or different categories?\n\n-  Although the authors have validated the effectiveness of their method on two representative data-driven personalized learning datasets, it is recommended to conduct experiments on additional open-source datasets to more comprehensively assess the method’s generalization ability and robustness.\n\n- Table 3 shows the impact of the presence or absence of prompts on performance. Have the authors considered the specific impact of different types of prompts on performance?", "other_comments_or_suggestions": "Please refer to other reviews.", "questions_for_authors": "Please refer to the weaknesses.", "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": "OPWDmofOLs", "reviewer": "Reviewer_zV7k", "summary": "This paper addresses a major challenge in personalized learning, where many existing methods assume high-quality, well-annotated benchmarks. In real-world settings, such benchmarks often exhibit long-tail distributions that negatively affect model performance. The authors propose a novel approach called Neural-Collapse-Advanced Personalized Learning (NCAL), which introduces a Textmodality Collapse (TC) regularization to optimize the distribution of text embeddings in large language models (LLMs). NCAL is model-agnostic, ensuring it can work with various architectures, and it is shown to improve performance significantly, surpassing previous state-of-the-art methods while mitigating class imbalance.", "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": "> **Q1: However, the text lacks detailed information on the underlying mathematical principles of the Textmodality Collapse regularization, and a deeper explanation would be beneficial.**\n\n**A1:** Thank you for the reviewer's insightful comment. In our paper, the goal of TC regularization is to promote a more balanced and structured text feature distribution, thereby addressing the long-tail distribution problem by leveraging the constraints from the information of different categories. In Section 4.3, we explain the mathematical principles behind TC regularization from the perspective of gradient optimization and its role in constraining the text feature space.\n\n> **Q2: However, the TC loss in Equation 14 and the loss in Equation 13 should be further explained and clarified.**\n\n**A2:** We further clarify the distinction between the TC loss in Equation 14 and the loss in 13. The gradient in 13 consists of an \"intra-class cohesion\" term and an \"inter-class repulsion\" term. In class-imbalanced learning paradigms, the gradient signals for underrepresented minority classes become dominated by the repulsion term, which creates geometric configuration biases that can result in suboptimal feature space organization for rare categories. Equation (14) explicitly shows that the TC loss adjusts text representations to ensure maximal angular separation, mitigating the adverse effects of imbalanced gradient updates.\n\n> **Q3: However, the authors should visualize the statistical analysis of these two real-world datasets.**\n\n**A3:** In the TMWPL dataset, there are seven classes: Recall, Formulate, Identify, Represent, Implement, Inferences, and Analyze, with sample sizes of 5045, 1283, 717, 711, 405, 235, and 216, respectively. On the other hand, the PMTD dataset consists of eight classes: R-FR, I-Q, R-SR, F-I, F-F, U, I-H, and R-RR, with sample sizes of 1014, 880, 737, 518, 510, 389, 104, and 62, respectively.\n\n> **Q4: The authors should explain why these four specific values of $\\tau$ were chosen for the experiments.**\n\n**A4:** Firstly, $ \\tau $ represents the imbalance ratio of the data, defined as the ratio of the number of samples in the least frequent category to the number of samples in the most frequent category. In our paper, 0.03 corresponds to the $ \\tau $ of our original data, while 0.25 is the maximum $ \\tau $ that our data can support for training without compromising the model's performance. Beyond this threshold, the data volume is insufficient for effective training.\n\n> **Q5:** The authors should explain the possible reasons behind this unexpected result.\n\n**A5:** The reviewer raised an interesting question. In the ablation experiments, the performance improvement of the \"w/o prompt\" condition compared to the baseline method may stem from the model's ability to more effectively construct the text feature space in the absence of a prompt. This suggests that removing the prompt could allow the model to be more flexible in feature extraction, thereby enhancing performance. Although this phenomenon is intriguing, we believe further experiments are needed to validate its correctness, and we plan to explore this as part of future work.\n\n> **Q6: More experimental designs for our method.**\n\n**A6:** We followed the reviewer's suggestion and considered additional baselines to compare the effectiveness of our method. The experimental results are shown below. Under the comparison with the same model parameters and even larger model parameters, our method still demonstrates robust performance on long-tail distribution datasets. This further confirms that the introduction of neural collapse can constrain the learning of the model's class space, thus avoiding biases caused by data with a large number of samples.\n\n| Model | Parameters | Dataset | Acc |\n| --- | --- | --- | --- |\n| glm-4-9b-chat  | 9B | TMWPL | 67.71 |\n| Ministral-8B-Instruct | 8B | TMWPL | 66.86 |\n| Yi-1.5-9B-Chat | 9B | TMWPL | 63.14 |\n| Baichuan2-7B-Chat | 7B | TMWPL | 61.14 |\n| **NCAL-Qwen2.5-Instruct**   | 7B  | TMWPL | **74.86** |\n\n> **Q7: In Equation (5), the symbol $\\alpha$ is not explained.**\n\n**A7:** $\\alpha$ is a constant parameter for scaling in our paper.", "claims_and_evidence": "The paper demonstrates convincing evidence that NCAL effectively addresses the long-tail distribution issue and enhances performance. The claim about improving generalization ability is well-supported by experiments. However, the text lacks detailed information on the underlying mathematical principles of the Textmodality Collapse regularization, and a deeper explanation would be beneficial.", "methods_and_evaluation_criteria": "Yes, the proposed method and evaluation criteria make sense for the problem. Personalized learning is a significant area of interest, and addressing the limitations of the benchmark, especially their long-tail distributions. It is an important step in improving model performance. The introduction of NCAL, with its Textmodality Collapse regularization, is a reasonable approach to optimize text embeddings and improve generalization ability.", "theoretical_claims": "I have specifically checked the correctness of the proofs for the theoretical claims in this paper. However, the TC loss in Equation 14 and the loss in Equation 13 should be further explained and clarified.", "experimental_designs_or_analyses": "The authors conducted experiments on two real-world personalized learning datasets and compared various types of large language models as baselines. The overall experimental design is reasonable and sufficiently demonstrates the effectiveness of the proposed method. However, the authors should visualize the statistical analysis of these two real-world datasets.", "relation_to_broader_scientific_literature": "This paper presents NCAL, a model-agnostic method that addresses long-tail distributions in personalized learning by optimizing text embeddings with Textmodality Collapse (TC) regularization. NCAL improves model generalization, mitigates class imbalance, and achieves state-of-the-art performance, advancing data-driven methods in real-world scenarios.", "essential_references_not_discussed": "In my understanding, the important references have already been discussed.", "other_strengths_and_weaknesses": "## Strengths\n\n- NCAL enhances the generalization ability of models by addressing the challenge of class imbalance, improving performance on diverse datasets.\n- By being model-agnostic, NCAL can be applied across different architectures, increasing its versatility and potential for wide adoption in various domains.\n\n## Weaknesses\n\n- The authors should provide a statistical analysis of the datasets used in the experiments and visually present the long-tail distribution of sample sizes in each category for a clearer understanding.\n\n- In Figure 4, the authors show the performance differences between various methods based on different values of $\\tau$. The rationale for selecting $\\tau = 0.25/0.03/0.15/0.25$ is unclear. The authors should explain why these four specific values of $\\tau$ were chosen for the experiments.\n\n- In Table 3, two performance spikes are observed for the “w/o prompt” condition in green. The authors should provide an explanation for the possible reasons behind this unexpected result.", "other_comments_or_suggestions": "In Equation 5, the symbol $\\alpha$ is not explained.", "questions_for_authors": "See other comments.", "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 explores the use of neural collapse regularization on text embeddings during the fine-tuning of large language models (LLMs) for personalized learning. Experimental results demonstrate that the proposed approach improves performance across tasks and helps mitigate class imbalance.

The technical contribution of this paper seems limited, as the idea of promoting neural collapse to address long-tailed and imbalanced scenarios has been explored before. But this paper presents a strong application of the neural collapse principle to LLMs, particularly in data-driven personalized learning. In the revision, the authors are advised to incorporate the discussions with reviewers (such as the additional experiments) and add more discussions about other approaches in promoting neural collapse for imbalanced learning.
Decision: Accept (poster)