Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
Authors: Weibin Liao
Tianlong Wang
Yinghao Zhu
Yasha Wang
Junyi Gao
Liantao Ma
Venue: NeurIPS 2025 poster ()
Keywords: Medical Lay Language Generation
Large Language Model
LoRA
PEFT
Primary area: deep_learning
TL;DR: We propose Magical, an asymmetric LoRA architecture to meet the requirement for semantic fidelity and diverse lay-style generation in medical lay language generation task.

Abstract: Medical Lay Language Generation (MLLG) plays a vital role in improving the accessibility of complex scientific content for broader audiences. Recent literature to MLLG commonly employ parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA) to fine-tuning large language models (LLMs) using paired expert-lay language datasets. However, LoRA struggles with the challenges posed by multi-source heterogeneous MLLG datasets. Specifically, through a series of exploratory experiments, we reveal that standard LoRA fail to meet the requirement for semantic fidelity and diverse lay-style generation in MLLG task. To address these limitations, we propose Magical, an asymmetric LoRA architecture tailored for MLLG under heterogeneous data scenarios. Magical employs a shared matrix A for abstractive summarization, along with multiple isolated matrices B for diverse lay-style generation. To preserve semantic fidelity during the lay language generation process, Magical introduces a Semantic Invariance Constraint to mitigate semantic subspace shifts on matrix A. Furthermore, to better adapt to diverse lay-style generation, Magical incorporates the Recommendation-guided Switch, an externally interface to prompt the LLM to switch between different matrices B. Experimental results on three real-world lay language generation datasets demonstrate that Magical consistently outperforms prompt-based methods, vanilla LoRA, and its recent variants, while also reducing trainable parameters by 31.66%. Our code is publicly available at https://github.com/tianlwang/Magical.git.

Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
Weibin Liao Tianlong Wang*i, Yinghao Zhu
Yasha Wang* , Junyi Gao S*- Liantao M
National Engineering Research Center For Software Engineering, Peking University
School of Computer Science, Peking University
School of Computing and Data Science, The University of Hong Kong
Centre for Medical Informatics, University of Edinburgh
HHealth Data Research UK
fliaoweibin, tianlong. wang) Sstu pku. edu. en, malt@pku. edu. cn
https://github. com/ comm/tianin Maaawaag/Magiiaal git
Abstract
Medical Lay Language Generation (MLLG) plays vital role in improving the accessibility of complex scientific content for broader audiences. Recent literature to MLLG commonly employ parameter-efffcieet fine-tuning methods such Low- Rank Adaptation (LoRA) to fine-tuning large language models (LLMs) using paired expert-lay language datasets. However, LoRA struggles with the challenges posed by multi-source heterogeneous MILLG datasets. Specifically, through a series of exploratory experiments, reveal that standard LoRA fail meet the requirement for semantic fidelity and diverse lay-style generation in MILG task. To address these limitations, we propose Magical, asymmetric LoRA architecture tailored for MILG under heterogeneous data scenarios. Magical employs a shared matrix A for abstractive summarization, along with multiple isolated matrices B for diverse lay-style generation. To preserve semantic fidelity during the lay language generation process, Magical introduces Semantic Invariance Constraint to mitigate semantic subspace shifts on matrix A. Furthermore, to better adapt to diverse lay-style generation, Magical incorporates the Recommendation www.rnddatinnniniieeeee Switch, an externally interface prompt the L.M to switch between different matrices B. Experimental results on three real-world lay language generation datasets demonstrate that Magical consistently outperforms prompt-based methods, vanilla LoRA, and its recent variants, while also reducing trainable parameters by 31.66%.
Introduction
Medical Lay Language Generation (MLLG) [1] shared task surrounds the abstractive summarization and lay-style generation of biomedical articles, aiming generate readable, accessible summaries for non-expert audiences
[2]. This task addresses a critical barrier: the highly specialized language used in clinical and biomedical literature often limits public understanding and reduces patient adherence to medical advice in clinical settings. By translating complex medical content into lay-friendly language, MLLG promotes equitable access to life-related knowledge [3-5], supporting the broader
Equal Contribution. Corresponding Authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

societal right to understand and engage with health information [6-8]. To achieve this goal, recent literature [9, 10, 2] has explored the use of parameter-effiiiet fine-tuning (PEFT) [11] methods such as LoRA [12] on large language models (LLMs)) [13-15] by leveraging paired expert-lay language data, to enable LLMS to equip domain-specifie knowledge and transformation patterns relevant to MLLG tasks. Given the scarcity of paired expert-lay language data, recent efforts [16, 17] have shifted toward utilizing multi-source MILG datasets to enrich training signals and promote generalization.
Key Insights of LoRA on MLLG. LoRA [12] modifies the original model by injecting a low- rank decomposition into the weight updates, expressed as y Woz + BAr, where Wo denotes the pre-trained weight matrix, and A and B are low-rank matrices capturing the adaptation. This formulation reveals a structural resemblance to aelo-encoder-baeee representation editing [18-21], where 2o represents the original representation, and BAr can be interpreted as an additive, low- dimensional edit in the representation space [22]. Applying LoRA to MILLG implicitly assumes that both abstractive summarization and lay-style generation can be modeled within low-rank subspace. However, this assumption raises two critical questions: Does the low-rank adaptation reliably preserve semantic fidelity? and Can they adapt robustly to diverse lay-style generation proposed by multi-source MLLG datasets? Given that semantic fidelity a potential optimization objective and heterogeneity is inherent characteristic of multi-source datasets, it remains uncertain whether LoRA's constraiad updates can adequately model both abstractive summarization and diverse lty-style generation. This motivates deeper investigation into the representational capacity of LoRA in scenarios requiring both semantic stability and diverse stylistic transformation.
In this study, we conduct series of exploratory experiments using LoRA to fine-tune L.MSs on multi-source MILG datasets. Our in-depth analysis of heterogeneity MIL.G datasets and LoRA's mechanics yields several insightful observations and leads to the formulation of key hypotheses. Firstly, despite serving the same MLLG tasks, datasets from different sources exhibit tremendous heterogeneity. This heterogeneity further contributes to LoRA's suboptimal performance. Results indicate that rather than utilizing multiple datasets to fine-tune a single LoRA, it is more effective to employ multiple smaller LoRAs, each fine-tuned exclusively on a specific dataset. This suggests that the detriments of data heterogeneity outweigh the potential benefits of increased training diversity. Furthermore, we investigate semantic fidelity. Results demonstrate that LoRA's low-rank projection causes detrimental semantic subspace shift, which challenges the optimization objective of semantic fidelity in MILG tasks. Based on these observations, contend that standard LoRA fail to meet the requirement for semantic fidelity and diverse lay-style generation in MILG task.
To address these challenges, we propose Magical (Medical Lay Language Generation via Semantic In- variance and Layperson-tailores Adaptation), an asymmetric LoRA architecture with a shared matrix A for abstractive summarization and multiple isolated matrices B for diverse lay-style generation. Specifically, for matrix A, Magical first employs
Layer Identificate fafiicaiioon to identify the Transformer layers within the LLM that are semantically relevant, then applies Semantic Contrastive Learning to encourage matrix A to project input representations into a semantic latent subspace, thereby ensuring semantic fidelity within the low-rank projection. For ma- trix B, Magical utilizes multiple isolated matrices B to project representations into diverse lay-style subspaces, enabling adaptation to different sources of MLLG datasets individually. Furthermore, in- spired by the divide-and-conguee principle [23], Magical incorporates the Recommendati iondidattinniidddeeeeeeee Switch, an external interface that prompts LLM to dynamically switch among different B matrices.
Our contributions are summarized as follows:
1. Insightly, we provide valuable insights into existing approaches that fine-tune LLMs using LoRA for the MLLG task. Based on these insights, we conducted a series of exploratory experiments and confirmed that LoRA falls short in achieving semantic fidelity and diverse lay-style generation. To address this challenge, we propose Magical, method that incorporates Semantic Invariance Constraint and Layperson- tailored Adaptation.
2. Technically, we design demantic-Relevee
Layer Identif edentificatin iictiion and employ Semantic Contrastive Learning on matrix A to enforee semantic fidelity during the low-rank pro- jection process. Furthermore, we introduce multiple isolated matrices B along with iercomenddtioon-uiidee Switch to enable diverse lay-style generation.
3. Experimentally, we conduct extensive experiments to validate the robustness of Magical in maintaining semantic fidelity and its effectiveness in supporting diverse lay-style generation.

---

992
Epert
Paocaaal DesySeoki
CORSUE
202
180
Coper hot
Expert Tex
Codrane
dulc
Godrane
dule
PMG Pecspodss
Cocrane
duit
Figure I: Distribution of Word Count, DCRS [1] and readability evaluation of DeepSeek- V3 on three heterogeneous MILG Datasets.
2 Preliminary and Motivation
2.1 Low-Rank Adaptation (LoRA)
LoRA [12] introduces parameter-efficient fine-tuning strategy that achieves performance comparable full fine-tuning across various benchmarks. Instead of updating the full set of pretrained model weights Wo. LoRA keeps these weights frozen and injects trainable low-rank decomposition matrices into each layer of the model. Specifically, for each layer, LoRA introduces two consecutive low-rank matrices A and B to model the residual weight updates, thereby enabling task-specific adaptation. The process can mathematically formulated as follows:
= y Ay Wor + BAr
(1)
where y E Rd denotes the output and E R represents the input. The matrices B Rdxr and E IR' r are low-rank projections, with r < min(d, k).
Heterogeneous MILG Datasets
In this empirical study, we conduct detailed investigation of three real-world publicly available datasets used in our work: Cochrane [24], el [25], and Plos cros_genetics [2]. Specifically, we analyze both the expert and lay texts in these datasets using three metrics: Word Count, Dale-Chall Readability Score (DCRS) [1] and readability evaluation of DeepSeck -3 (For details of these metrics, please refer Appendx. B). As shown in Fig. 1, we observe that for Cochrane and Plos_genntics, the word count decreases after lay transformations, whereas for eLife, the word count increases. terms of DCRS, only eLife exhibits improved readability after lay transformations, whereas the readability of Cochrane and Plos sesseeeeeessssee decreases following the same process. Furthermore, based on readability evaluation conducted using DeepSeek-V. V3 [26]. observe that although all three datasets show overall improvement readability after lay transformations, the magnitude of improvement varies datasets.
Our views the following: each MLLG dataset may be associated with an inaccessible inter-annotator agreement [9], resulting in distinct dataset characteristics, including simplification strategies, depth of simplification, domain-specific conventions, and stylistic preferences. In some scenarios, MLLG involves removing non-essential content while preserving the message (word count J)) In others, it requires supplementing the original text with additional background knowledge to explain domain-specifie technical terms (word count t). Based this, we derive first key observation:
Observation I: Different medical lay language generation datasets yield diverse lay-style generation proposed by heterogeneity, stemming from differences in inter-annolator agreement.
2.3 LoRA Meets Heterogeneous MLLG Datasets
Single-LoRA vs. Multi-LoRA. In this preliminary experiment, we primarily investigate whether LoRA can adapt to multi-source heterogeneous data and enable diverse lay-style generation. To achieve this goal, we focus on LoRA and conduct a series of experiments, as shown Table. 1, to gain deeper insight into its underlying mechanisms. We adopt two independent experimental setups. In the first setup, we use single LoRA with a rank of 24 to jointly fine-tune on three datasets: Cochrane [24], eLife [25], and Plos tlos_genettce [2]. In the second setup, three separate LoRAs,

---

Table 1: Performance of LoRA and mLoRA (multiple smaller LoRAs) on Cochrane, eLife and Plos _oeneties datasets. n. is the number of LoRAs, r denotes the rank of each LoRA. All evaluation metries are defined such that higher values indicate better performance (t).
Cochrane
eLife
Plos genetics R-2
Methods
rXn
#Params
R-1
R-2
R-L
BLEU
R-1
R-2
R-L
BLEU
R-1
R-L
BLEU
LoRA mLoRA
24x 8x3
36M
39.43
16.07
37.21
10.20
42.32
11.55
40.26
5.62
41.04
12.42
38.35
6.33
36M
40.01
16.33
37.71
10.37
46.69
13.48
44.57
7.36
44.92
13.62
41.42
8.80
LLaMA3.
LoRA mLoRA
24x1
62M
41.32
17.45
38.72
11.84
48.25
14.59
46.27
8.49
41.98
12.85
39.17
6.79
8x3
62M
40.19
16.56
37.53
12.06
49.40
14.88
47.29
8.53
47.55
16.34
43.98
20ven2.5-78-farttee
LoRA
24x 1
60M
41.32
17.45
38.72
11.84
48.25
14.59
46.27
8.49
41.98
12.85
19.17
6.79
47.38
7.93
42.95
each with a rank of 8, to fine-tune the LLM individually on each dataset. The total number of trainable parameters remains the same across both setups.
The experimental results show that, in the vast majority cases, deploying multiple smaller LoRAs yields better performance than using single larger one. We attribute this to the fact that while single LoRA can leverage a larger amount of training data, its ability to adapt is constrained by the heterogeneity of the MLLG data. As result, it struggles finish diverse lay-style generation. This analysis yields another critical observation:
Observation II: The interference caused by the heterogeneity of data when using fully shared LoRA parameters outweighs the potential benefits brought by the additional information gained from data augmentation.
Semantic shift LoRA. A potential optimization ob- jective in the MLLG task is ensuring semantic fidelity [2], as misinterpretation or distortion of medical information lead patients developing inaccurate health percep- tions or making inappropriate decisions [3, 6, 27]. We conduct in-depth analysis of semantic conveyance in low-rank projection of LoRA. Specifically, we randomly select a Multi-layer Perceptron (MLP) and project the representations of the original expert texts and the LoRA- finetuned lay texts onto the first two singular directions of semantically relevant subspace. We then plot their respec- tive Kernel Density Estimation (KDE) distributions [18], as shown in Fig. 2. It can be observed that the original expert texts and generated lay texts exhibit significant distributional divergence in the semantic subspace. This indicates that LoRA lacks a mechanism to preserve se- mantic fidelity in low-rank projection, leading to notable semantic shift during LoRA fine-tuning. The case study provided in Appendix. E. 10 also demonstrates the seman- tic degradation caused by the naive application LoRA. This motivates third key observation:
Expert Text
Text
10
-5
-15
-20
10
Figure 2: Projections of activations from expert text and lay text on the top-2 singular directions of the semantic sub- space, which form the and y-axes of the KDE plot.
Observation III: The naive application of LoRA in the MLLG task demonstrates insufficient control over semantic fidelity, resulting in harmful semantic shifts that degrade model performance.
Summary. Building on the above insightful observations, our goal is to more effectively leverage mixed heterogeneous data by adapting to diverse lay-style generation while preserving semantic fidelity throughout the lay transformations process. Inspired by works such as HydraLoRA [28], we propose Magical. Magical employs Semantic Invariance Constraint on to enforce semantic fidelity during the low-rank projection process (addressing Observation III), and intro-

---

Expert
Probing Semantic
Semantic-relevant
Arsatinmmaatoon
Agrat
Pre-trained
8
Byy
Loy
Weights
Heterogeneous
Transformer Loyer
Switcch
Interface
Key
Tronsformer Loyer
LN
Overy
Attention
Cross correcction Morri
Tip the long snoke
Semantic- Semontic-ireleevat
Coched bey dictionry
simplified Into soake.
(o) Heterogeneous Expert-Loy Dotasets
(c)
Layer identification
Loyperson-toliored Adoptation
Figure 3: Overview of the Magical. (a) Illustrates the target audience expert-lay language and the heterogeneity of multi-source datasets. (b) Depicts Magical employs probing techniques to identify semantic-relevam layers for subsequent Semantic Contrastive Learning. (c) Shows Magical applies Semantic Contrastive Learning matrix A to enforce semantic invari- ance, and utilizes an external Recommendation Agent to switch between different matrices for Layperson-taile tailored Adaptation.
duces Layperson-tailored Adaptation on B to enable diverse lay-style generation (addressing Observations and IT).
3 Methodology
3.1 Framework of Magical
We present Magical, novel framework inspired by HydraLoRA [28] that employs asymmetric structure for MLLG. The framework operates on a collection of datasets = (D1. D2,..., .2.....Dy where each D, corresponds to training data for specific lay-style and N is the number of datasets.
}.
Magical utilizes shared matrix A prxk for abstractive summarization and multiple isolated where each B; E Rdxr is dedicated to specific lay-style generation. The parameter represents the low-rank dimension, & the input dimension, and is the output dimension. The process can be mathematically formulated as follows:
matrices B {B1, ......, Bx).
N 0s
y = Wo
B,A
where a; is branch control variable that controls the degree of branch participation.
As illustrated in Fig. 3, Magical further employs Semantic Invariance Constraint matrix and wayyeeson-taillooee Adaption on matrix B to enable more robust medical lay language generation.
3.2 Semantic Invariance Constraint on A
Prior work [29] has shown that different layers LLMs capture distinct functional representations. Accordingly. Magical employs probing techniques [30- 32] to identify layers that are more semantically relevant and applies customized LoRA fine-tuning specifically to those layers. To achieve this, Magical utilizes expert-lay language pairs with aligned semantics and determines the semantic relevance of each layer based on its probing accuracy on a semantic classification task.
Semantic-Relevannn Layer Identification.
Specifically, the paired expert-lay language dataset = (( xe (2) ..... (I ( where Io and F, denote the expert language (original) and the lay language (simplified) respectively, and
5

---

N represents the number of samples in the dataset. Magical constructs semantic consisteney classification task by aggregating expert and lay language, and creates a probing dataset D A pair is considered a positive sample if = j. and a negative sample otherwise.
I...... ;.... Let x=' [xo = Magical defines the probe pı(x*) Sigmoid (00-11, x*), for each layer of the LLM to detect the semantic-relcvance of the activations, where 80-1 denotes the parameters of the first layers of the LLM.
Next, Magical randomly splits the dataset into a 4:1 ratio of training to testing sets and fits a binary linear classifier pl-) on the training set. The layers achieving the top-K validation accuracy are identified as semantic-relevant layers, and subsequent Semantic Contrastive Learning is applied specifically these layers.
Semantic Contrastive Learning. Based on the earlier Observation III, Magical encourages LoRA to project input representations through matrix A into semantic-relevaae low-rank subspace, in order to preserve semantic fidelity during the low-rank projection process. To achieve this, contrastive learning [33] is applied to representations in this space. Specifically, Magical seeks to establish clear boundary between samples with different semantics in the semantic space, and contrastive learning is a well-established approach for this purpose [18, 34]. For the expert-lay language dataset D= = ((xb)) (1) .265 ),."))... (x. x )) Magical treats { as the positive sample for xo and ()()) * as a negative sample. Contrastive learning aligns the representations by minimizing the distance between (1) and 444, while maximizing the distance between (+) and x (t) Magical denotes the set of positive samples for as x and the set of negative samples as then the training objective can be formally defined as:
ExEx+ exp(sim(e, /T)
Coontra (I,x* = -lg Σx'ex x exp(sim(r; )/T)
where sim(-) refers to cosine similarity between representations, and 7 is the temperature.
Regarding implementation details, since Magical receives only expert language input in the MLLG task, we construct a cached key dictionary to leverage lay language as contrastive targets. Specifically, during each training iteration, we use the matrix A retained from the previous iteration encode the lay language into representations, which are then stored as a key dictionary and cached for use in the current iteration's Semantic Contrastive Learning.
3.3 Layperson-tailored Adaptation on B
vs. Switch-Controlled. Magical explores two mechanisms for branch control variable a; in Eq. aggregate matrices B: Router- Controlled Selection and Selection. In this research, Magical defines them as follows:
Contrll-o Selection: Router-Controlled Selection defines a as a continuous probability distribution, leveraging a Soft Selection mechanism to aggregate the performance of multiple B matrices. Specifically, this can be formulated as Σ a; = 1. Typically, multiple matrices B are activated, and the LLM integrates their outputs handle complex tasks.
Switch-Controlled Selection: Switch-Cone Controlled Selection defines & as a one-hot vector, employ- ing Hard Selection mechanism to choose specific branch. Specifically, this entails that a; = 1 while a = for all + j. In this setting, the LLM typically does not autonomously identify the task. Only single matrix B is activated, which reduces interference from other B matrices.
Recommendation-guidedd Switch. Unlike existing mainstream approaches [28, 35, 36] that em- ploy Router- Controlled Selection to enable LoRA for multi-task learning, Magical adopts Switch- Controlled Selection for MLLG task to mitigate the interference caused by the heterogeneity of multi-source lay language dataset. This design is motivated by the observation that, typical multi- task learning frameworks, the differences between tasks are often easily distinguishable, allowing simple prefix prompts [37, 38) and the in-context learning ability [39] of L. to effectively combine multiple B matrices. However, in the heterogeneous data setting of the MLLG task, such simple

---

Table 2: Performance comparison of Prompt, various LoRA variants, and Magical across three backbone LLMs on three MLLG datasets. All evaluation metrics are defined such that higher values indicate better performance (t). The best results are highlighted bold, while the second-best results are underlined. The Impro (%) indicates the relative improvement of Magical over the second-best performance.
Methods
#Params
Cochrane R-2
eLife
genetics R-I.
R-1
R-L.
BLEU
R-1
R-2
R-I.
BLEU
R-1
R-2
BI LLU
LLoMA3.2-3B-Insttie
Prompt
N/A
39.81
11.37
36.51
5.28
37.82
8.22
35.47
3.08
37.88
7.78
35.08
4.13
LoRA [12]
36M
40.01
16.33
37.71
10.37
46.69
13.48
44.57
7.36
44.92
13.62
41.42
8.80
LLLoRA [42]
36M
40.85
16.38
38.44
10.23
45.41
13.02
43.30
7.09
43.75
13.91
40.54
7.79
DoRA [43]
40.03
37.81
10.26
12.01
40.84
6.16
41.17
6.44
PISSA [44]
36M
39.75
16.14
37.38
10.52
42.89
11.99
40.85
6.26
43,51
13.91
40.45
7.79
Magical
24M
45.33
19.39
42.36
16.66
49.16
14.68
46,91
8.30
47,50
15.47
44.03
10.24
Impro (%)
33.33
10.97
18.38
58.37
5.29
8.90
5.25
12.77
16.36
1.LMM33.
Prompt
NIA
41.67
12.50
38.60
6,23
35.82
9.44
33,36
1.85
39.13
8.67
36.16
4,66
LoRA [12]
62M
40.19
16.56
37.53
12.06
49.40
14.88
47.29
8.53
47.55
16.34
43.98
11.54
LsLoRA [42]
62M
43.30
18.12
40.62
12.89
49.33
15.01
47.25
8.71
42.19
12.86
39.35 36
6.87
DoRA [43]
64M
43.24
18.28
40.60
13.50
48.47
14.67
46.41
8.44
42.48
13.06
39.59
7.17
PISSA [44)
42.95
17.80
40.23
13.89
14.66
46.74
8.40
39.64
37.26
5.62
Magical
42M
45.71
19.52
42.79
16.68
50.44
15.49
48.02
8.67
48.77
16.64
45.06
11.86
Impro (%)
32.23
5.57
6.78
5.34
20.09
2.11
3.20
1.54
-0.46
2.57
1.84
2.46
2.77
Qwen2..5 5-BB Instruct
Prompt
44.53
15.57
09
8.85
32.29
1.80
12.50
41.14
7.73
LoRA [12]
60M
44.23
19.37
41.60
14.50
47.38
13.90
45,42
7.93
46.41
16.29
42.95
9.94
oo..AA [42]
60M
45.22
19.48
42.46
15.06
48.70
14.54
46.48
7.76
46.26
15.60
42.87
9.90
DoRA [43]
61M
45.65
19.64
42.87
15.45
47.38
13.90
45.24
7.70
46.64
15.97
43.24
10.12
PISSA [44)
60M
44.87
19.30
42.12
14.52
48.86
14.46
46.68
7.82
46.81
15.93
43.57
10.48
Magical
42M
47.42
20.81
44.38
17.89
50.50
15.28
48.16
8.66
48.54
16.39
44.79
11.42
Impro (%)
30.00
3.88
5.96
3.52
15.79
3.36
5.09
3.17
9.21
3.70
0.61
2,80
8.97
prompts become ineffective. This is because overly simplistic instructions fail to comprehensively articulate the target lay-style, and demonstrations [40] may significantly increase sequence length, thereby raising the risk of fost-in-the-midde [41]. In subsequent experiments, we validated the dilemma faced by Router-Controlled Selection in the MLLG task and demonstrated the rationality of Contch-Cnntoolled Selection.
Due to the limitations of LLMs in autonomously selecting appropriate lay-style, Magical adopts a sowidd-and-ooqquee principle [23] by employing an external Recommendation Agent to handle lay-style selection. It further introduces an open Interfuce to bridge the Switch mechanism with the Recommendatton Agent, enabling dynamic switching to the most suitable matrix B for the target lay-style generation. The core insight of Magical lies in avoiding the excessive accumulation of redundant optimization objectives within a single low-rank subspace, which would otherwise compromise the quality of lay language generation.
4 Experiment
4.1 Experimental Setups
Network Architecture, Datasets and Metrics. Our experiments were based on various backbone LLMs, including LLaMA3 series [45] of various sizes (LLaMA3.2-3B-Inst) Instruct) and 5et.n2..--B--tttuuee [46]. To evaluate the effectiivness of Magical, we conducted ex- periments on three publicly available real-world MLLG datasets, including Cochrane [24], eLife [25], and Plos Pliisllooeeeeeee genetics [2]. We followed the official data splits construct the training and test sets. Detailed statistics of these datasets are provided in Appendix. A. Following the evaluation protocol of existing literature [9], we adopted ROUGE-1 / (R-1), ROUGE-2 (R-2), ROUGE-L (R-L), and BLEU as evaluation metrics for the MI.G task, with detailed descriptions available in Appendix. B.
and LLaMA3. I-BB-
Baselines. We adopt the following state-of-the-art approaches compared baselines.
7

---

Table 3: Ablation Studies of Magical using 1---MA33.--B-I-tteeee backbone LLM on three MLLG datasets. SRLI: Semantic-Relene Relevant Layer Identification. SCL; Semantic Contrastive Learning. All evaluation metries are defined such that higher values indicate better performance (t).
Cochrane
eLife
Plos geneticss R-2
Methods
R-1
R-2
R-L
BLEU
R-1
R-2
R-L
BLEU
R-1
R-L
BLEU
LLaMA3. In--nsst
Magical
45.71
19.52
42.79
16.68
50.44
15.49
48.02
8.67
48.77
16.64
45.06
11.86
w/o SRLI
41.41
17.22
38.65
12.20
49.83
15.04
47.60
8.59
47.97
16.33
44.31
11.57
w/o SCL
45.09
19.31
42.26
14.79
49.67
14.96
47.41
8.30
48.35
16.35
44.74
11.62
Single B
41.32
17:45
38.72
11.84
48.25
14.59
46.27
8.49
41.98
12.85
39.17
6.79
Switch /Router
41.77
16.93
39.34
11.21
47.76
14.08
45.64
7.86
41.01
12.39
38.27
6.19
Prompt leverages the in-context learning [39] ability to achieve MILG. Specifically, use a well-crafted prompt to describe the target lay-style with demonstrations. (See Appendix. D for detailed prompts).
Prompt:
Various LoRA Variants: We adopt LoRA [12] (multi-LoRA version) and several recently proposed
LoRA variants as our baseline models, including HydraLoRA [28], rsloRA [42], DoRA [43], and PiSSA [44]. It is worth noting that HydraLoRA [28] serves as an inspiration for Magical, and is therefore treated as variant of Magical in our ablation studies.
Implement Details. We used the PyTorch library implement all the algorithms based the open- source HuggingFace transformers [47]. The experiments were conducted on 8 NVIDIA-H20--6GBB GPUs. For each experimental setup, we trained all LLMs for epoch with DeepSpeed ZeRO Offload [48]. We utilized the Adam W optimizer and a cosine learning rate scheduler, with warm-up Adaptation, adopt manually specified matrix B, corresponding to Recommendation Agent with 100% recommendation accuracy.
ratio set 0.1. For the Recommendation Agent in Layperson-tail
4.2 Experimental Results
Comparison with Recent Literature. As shown Table. 2, we compare Magical with Prompt method, vanilla LoRA, and various LoRA variants across three MILG tasks. To comprehensively evaluate the effectiveness of Magical, we employ backbone LLMs with diverse architectures and scales. The experimental results demonstrate that Magical achieves the best performance in most cases, ranking second only to rsLoRA on the BLEU for the eLife dataset. Moreover, compared to the second-best performance, Magical yields an average relative improvement of 4.80% in R-1, 6.89%1 in R-2, 4.51 6 in R-L and 15.99%f I in BLEU across all settings, confirming its strong performance. More importantly, while achieving performance improvements, Magical also introduces average 31.66%. trainable parameters. Notably, the Prompt method consistently performs the worst in the majority of scenarios, especially on the BLEU metric. This supports our earlier claim that simple prompts are insufficient to effectively capture the target lay-style, making it challenging to generate language tailored for laypersons.
We verified the effectiveness of each module by removing some modules from Magical and evaluated the modified models using the ILAMA33. 1.1.A...1888. across three MILG datasets. The experimental results were presented in Table. 3. The experimental results provide deeper insights into the design of Magical. We first analyze the modules added to matrix A. When the Semantic-Relevant Layer Identificate cation (SRLI) component is re- moved -.cd Semantic Invariance Constraint are imposed across all Transformer layers of the LLM there is a significant performance drop (e.g., an average 1.90% J. R-1). This suggests that not all Transformer layers are responsible for semantic activation, and indiseriminately apply- ing constraints may harm other functionalities. Similarly, removing the Semantic Contrastive Learning (SCL) module also leads to a noticeable performance degradation (e.g., an average 0.83% BLEU). This indicates that without the optimization of latent objective promoting semantic invari- ance, the low-rank projection of LoRA may introduce semantic shifts, thereby undermining model performance.
Ablation Studies.
backbone L.M
8

---

10
Dpen
Copn
(a) Kernel Density Estimation
(b) Cross-correlation Matrix
Figure 4: (a) shows Kemel Density Estimation based on the projections of activations obtained with without the application of Semantic Invariance Constraint on A. (b) presents Cross- correlation Matrix for the representations of expert text and lay text under matrix A with / without the use of Semantic Invariance Constraint on A.
We further investigate the design choices regarding matrix B in Magical. Replacing multiple B matrices with a single matrix results in a clear performance drop (c.g., an average 4.46% & R-1). This is because single matrix / struggles project expert texts into diverse lay-style texts effectively. Moreover, when the one, the performance becomes comparable to using only single B matrix. This suggests that the routing mechanism does not effectively guide the LLM in selecting an appropriate B, thus failing to exploit the advantages brought by multiple B for layperson-taie -aiilore adaptation.
Selection mechanism is replaced with a Router-Controlle
Verification of Semantic Invariance. As shown in Fig. 4a, we randomly selected ML.P layer with / without the Semantic Invariance Constraint applied on matrix A, and projected the representations of expert texts and generated lay texts onto the first two singular directions of the semantic-relevant subspace. We then plotted their respective kernel density estimation distributions. The experimental results demonstrate that applying the Semantic Invariance Constraint on A effectively suppresses semantic shifts during the low-rank projection process. Furthermore, randomly selected a matrix A and visualized the cross-correlation matrix between the representations of expert texts and their corresponding lay texts after transformation by A as shown in Fig. 4b. The results indicate that this matrix A is capable of mapping expert texts and lay texts into a shared representation space, thereby preserving semantic fidelity in the generated lay text. The case study presented Appendix. E.10 provides a more detailed illustration of the semantic degradation introduced by LoRA and the semantic fidelity preserved by Magical.
Extended Investigations and Key Insights. We conducted a more detailed analysis of eyperson-tailoree Adaptation, including an investigation into the Dilemma of Router- Controlled Selection, and demonstrated that Router-Controoled Selection completely fails in scenar- ios where the LLM autonomously selects matrices B. Furthermore, analyzed the Sensitivity of Recommendation Performance and observed that, although the performance of Magical degrades with declining recommendation system quality, it still consistently outperforms standard baseline models. We also examined the Sensitivity of Rank, and showed that Magical continues to surpass baseline algorithms even when different ranks used. Detailed experimental results can be found in Appendices. E. E.2, and E.3. In addition, we provide Case Study in Appendix. E. E.10 to further analyze the superiority of Magical.
5 Limitation, Future Works and Conclusion
Despite the promising results obtained in our work. it is important to acknowledge the limitations. Considering that Magical employs wwrideandd-conques strategy, which relies on an external Rec- ommendation Agent to select suitable layperson styles. Although effective and empirically shown to outperform baselines even with suboptimal recommender the Recommendation Agent itself was not implemented in this work (see Implement Details 4.1) due to the lack of corresponding user data. Recent advances [49, 50] recommendation systems suggest that building such agent requires modeling the target users based on their individual profiles and behavioral histories. However, the current MILLG dataset does not provide these dyypersonsppecinn information. We also notice that this same linguistic complexity also poses challenges for interpreting the outputs of electronic health

---

record (EHR) predictive modeling [51-53]. In future work, adapting Magical to translate these technical outputs understandable narratives could significantly improve patient engagement and shared decision-making We will also focus on the development of lay-style recommendation agents under the cold-start setting [54]. Existing relevant strategies include meta-learning [55, 56], pre- trained models [57, 58]. and reinforcement learning [59, 60), among others. Integrating these recent advancements into our lay-style recommendation agents could more effectively support Magical in achieving lay-style recommendation and lay-style generation in real-world scenarios.
In this work, we conducted in-depth investigation into the limitations of using LoRA to fine-tune LLMs for the MLLG task, particularly its difficulty in preserving semantic fidelity and adapting to diverse lty-style generation patterss introduced by heterogeneous data. To address these chal- lenges, we proposed Magical, an asymmetric LoRA- A-baed framework that incorporates Semantic Invariance Constraint and Layperson-tailonn alignment and stylistic flexibility. While Magical does not yet integrate a dedicated Recommendation Agent for lay-style selection in this work, we advocate novel perspective: decoupling lay-style recommendation from lay-style generation. We envision multi-agent system where separate, spe- cialized modules handle generation and recommendation respectively, enabling mutual reinforcement between the two components. This collaborative design offers promising direction for advancing research in the MLLG domain.
Adaptation to enhance both semantic
Acknowledgement
This work supported by National Natural Science Foundation of China (62402017), Beijing Tra- ditional Chinese Medicine Science and Technology Development Fund (BIZYZZD-2025-33 Peking University Clinical Medicine Plus X (Young Scholars Project-the Fundamental Research Funds for the Central Universities PKU202SKKULCX0024: Pilot Program- Key Technologies Project 2024YXXL- HGG007), and Peking University "Teng Yun" Clinical Research Program (TY2025015), Liantao Ma was supported by Beijing Natural Science Foundation (L244063, L244025), Beijing Municipal Health Commission Research Ward Excellence Clinical Research Program (BRWEP2024W0321500)) and Xuzhou Scientific Technological Projects (KC23143). Junyi Gao acknow the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science (grant 218529/2/19/2) and Baidu Scholarship.
10

---

References
[1] Tomas Goldsack, Zheheng Luo, Qianqian Xie, Carolina Scarton, Matthew Shardlow, Sophia Ananiadou, and Chenghua Lin. Biolaysumm 2023 shared task: Lay summarisation of biomedical research articles. In Association for Computational Linguistics (ACL), 2023.
[2] Yue Guo, Wei Qiu, Gondy Leroy, Sheng Wang, and Trevor Cohen. Retrieval augmentation of large language models for lay language generation. Journal of Biomedical Informatics, 149:104580, 2024.
[3] Chandrayee Basu, Rosni Vasu, Michihiro Yasunaga, and Qian Yang. Med-easi: Finely annotated dataset and models for controllable simplification of medical texts. In Conference Artificial Intelligence (AAAI), volume 37, pages 14101-1410,, 2023.
[4] Xuanyu Shi, Daoxin Yin, Yongmei Bai, Wenjing Zhao, Xin Guo, Huage Sun, Dongliang Cui, and Jian Du A bibliographic dataset of health artificial intelligence research. Health Data Science, 4:0125, 2024.
[5] Jincai Huang, Yongjun Xu, Qi Wang, Qi Cheems Wang. Xingxing Liang, Fei Wang, Zhao Zhang, Wei Wei, Boxuan Zhang, Libo Huang, et al. Foundation models and intelligent decision-making Progress, challenges, and perspectives. The Innovation, 2025.
[6] Tomas Goldsack, Carolina Scarton, Matthew Shardlow, and Chenghua Lin. Overview of the biolaysumm 2024 shared task on the lay summarization of biomedical research articles. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 122-131, 2024.
[7] Xinyu Ma, Yasha Wang, Xu Chu, Liantao Ma, Wen Tang, Junfeng Zhao, Ye Yuan, and Guoren Wang. Patient health representation learning via correlational sparse prior of medical features. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(11):11769-1177
2022.
[8] Hua Ma, Xiaoru Yuan, Xu Sun, Glyn Lawson, and Qingfeng Wang. Seeing your stories: visualization for narrative medicine. Health Data Science, 4:0103, 2024.
[9] Kush Attal, Brian Ondov, and Dina Demner-Fushman. A dataset for plain language adaptation of biomedical abstracts. Scientific Data, 10(1):8, 2023.
[10] Zihao Li, Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Matthew Shardlow, and Goran Ne- nadic. Investigating large language models and control mechanisms to improve text readability of biomedical abstracts. International Conference on Healthcare Informatics (ICHI), pages 265-274. IEEE, 2024.
[11] Xinyu Ma, Xu Chu, Zhibang Yang, Yang Lin, Xin Gao, and Junfeng Zhao. Parameter efficient quasi-orthogonal fine-tuning via givens rotation. International Conference on Machine Learning (ICML), pages 33686-33729, 2024.
[12] Edward Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR), 2022.
[13] Weibin Liao, Xu Chu, and Yasha Wang. Tpo: Aligning large language models with multi-branch & multi-step preference trees. In International Conference on Learning Representations (ICLR),
2025.
[14] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song. Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.0332 2024.
[15] Weibin Liao, Xin Gao, Tianyu Jia, Rihong Qiu, Yifan Zhu, Yang Lin, Xu Chu, Junfeng Zhao, and Yasha Wang. Learnat: Learning nl2sql with ast-gu task decomposition for large language models. arXiv preprint arXiv:2504.23227 2025.

---

[16] Chenxi Whitehouse, Fantine Huot, Jasmijn Bastings, Mostafa Dehghani, Chu- Chu-Cheng Lin, and Mirella Lapata. Low-rank adaptation for multilingual summarization: An empirical study. In Findings of North American Chapter of the Association for Computational Linguistics (NAACL), pages 1202-1228, 2024.
[17] Hwanmun Kim, Kamal Raj Kanakarajan, and Malaikannan Sankarasubbu. Saama technologies at biolaysumm: Abstract based fine-tuned models with lora. In Proceedings of the 23rdd Workshop on Biomedical Natural Language Processing, pages 786-792, 2024.
[18] Shaolei Zhang, Tian Yu, and Yang Feng. Truthx: Alleviating hallucinations by editing large language models in truthful space. In Association for Computational Linguistics (ACL), pages 8908-8949, 2024.
[19] Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In International Conference Learning Representations (ICLR), 2023.
[20] Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan Vazquez, Ulisse Mini, and Monte MacDiarmid. Activation addition: Steering language models without optimization. arXiv e-prints, pages arXiv-230s 2023.
[21] Tianlong Wang, Xianfeng Jiao, Yinghao Zhu, Zhongzhi Chen, Yifan He, Xu Chu, Junyi Gao, Yasha Wang, and Liantao Ma. Adaptive activation steering: tuning-free Ilm truthfulness improvement method for diverse hallucinations categories. In International World Wide Web Conference (WWW), pages 2562-2578, 2025.
[22] Xinyu Ma, Yifeng Xu, Yang Lin, Tianlong Wang. Xu Chu, Xin Gao, Junfeng Zhao, and Yasha Wang. Dressing up Ilm: Efficient stylized question-answering via style subspace editing. In International Conference on Learning Representations (ICLR), 2025.
[23] Mohammadreza Pourreza, Hailong Li, Ruoxi Sun, Yeounoh Chung, Shayan Talaei, Gau- rav Tarlok Kakkar, Yu Gan, Amin Saberi, Fatma Ozean, and Sercan O Arik. Chase-sql: Multi-path reasoning and preference optimized candidate selection in text-to-sql. arXiv preprint arXiv:2410.01945 2024.
[24] Ashwin Devaraj. Byron C Wallace, Iain Marshall, and Junyi Jessy Li. Paragraph-level simplification of medical texts. In North American Chapter of the Association for Computational Linguistics (NAACL), volume 2021, page 4972, 2021.
[25] Tomas Goldsack, Zhihao Zhang, Chenghua Lin, and Carolina Scarton. Making science simple: Corpora for the lay summarisation of scientific literature. In Empirical Methods in Natural Language Processing (EMNLP), pages 10589-10604, 2022.
[26] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412. 19437, 2024.
[27] Xinyu Ma, Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao, Liantao Ma, and Wenwu Zhu. Fused soomov-wasserstrr graph mixup for graph-level classifications. Advances in Neural Information Processing Systems (NeurIPS),
-55267627776666 2023.
[28] Chunlin Tian, Zhan Shi, Zhijiang Guo, Li Li, and Cheng-Zhong Xu. Hydralora: An asymmetric lora architecture for efficient meninnning. Advances in Neural Information Processing Systems (NeurIPS), 37:9565 77774455-98844.1011 2024.
[29] Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao. Model tells you what to discard: Adaptive kv cache compression for 1lms. In International Conference on Learning Representations (ICLR). 2024.
[30] Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arX1v.1610.01644, 2016.
12

---

[31] Alexis Conneau, German Kruszewski, Guillaume Lample, Loic Barrault, and Marco Baroni. What you can cram into single vector: Probing sentence embeddings for linguistic properties. In Association for Computational Linguistics (ACI.), pages 2126-2136, 2018
[32] Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, 1(1, 2016.
[33] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning (ICML), pages 1597-1607. PmLR, 2020.
[34] Kihyuk Sohn. Improved deep metric learning with multi-class n-pair loss objective. Advances in Neural Information Processing Systems (NeurIPS), 29, 2016.
[35] Yuchen Li, Hao Zhang, Yonggi Zhang, Hengyi Cai, Mingxin Cai, Shuaiqiang Wang, Haoyi Xiong, Linghe Kong, Dawei Yin, and Lei Chen. Rankexpert: A mixture of textual-and- behavioral experts for multi-objective deaning-to-rante in web search. In Conference on Knowl- edge Discovery and Data Mining (KDD), pages 4578-4589, 2025.
[36] Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Yu Han, and Hao Wang. Mixture-of-loras: An efficient multitask tuning method for large language models. In International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), pages 11371-11380, 2024.
[37] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), pages 4582-4597, 2021.
[38] Weibin Liao, Yinghao Zhu, Zhongji Zhang, Yuhang Wang, Zixiang Wang, Xu Chu, Yasha Wang, and Liantao Ma. Learnable prompt as pseudo-imputation: Rethinking the necessity of traditional ehr data imputation in downstream clinical prediction. In Conference on Knowledge Discovery and Data Mining (KDD), pages 765-776, 2025.
[39] Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, al. A survey in-context learning. In Empirical Methods in Natural Language Processing (EMNLP), pages 1107-1128, 2024.
[40] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems (NeurIPS), 33:1877- 1901, 2020.
[41] Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. Transactions of the Association of Computational Linguistics (TACL), 12, 2024.
[42] Damjan Kalajdzievski. A rank stabilization scaling factor for fine-tuning with lora. arXiv preprint 232.02231 2023.
[43] Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Swang-Ting Cheng, and Min-Hung Chen. Dora: Weight-decomposed low-rank adaptation. In International Conference on Machine Learning (ICML), 2024.
[44] Fanxu Meng, Zhaohui Wang, and Muhan Zhang. Pissa: Principal singular values and singular vectors adaptation of large language models. Advances Neural Information Processing Systems (NeurIPS), 37:121038-121072 2024.
[45] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.1397. 2023.
[46] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint 16X6992309.16609 2023.
13

---

[47] Shashank Mohan Jain. Hugging face. In Introduction to transformers for NLP: With the hugging face library and models to solve problems, pages 51-67. Springer, 2022.
[48] Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He. {Zero-offload]: Democratizing (billion-scale) model training. In USENIX Annual Technical Conference ( (USENIX ATC), pages 551-564, 2021.
[49] Yuchen Li, Haoyi Xiong, Linghe Kong, Rui Zhang, Fanqin Xu, Guihai Chen, and Minglu Li. Mhrr: Mooes recommender service with meta hierarchical reinforced ranking. IEEE Transactions on Services Computing (TSC), 16(6):4467-4480) 2023.
[50] Weibin Liao, Yifan Zhu, Yanyan Li, Qi Zhang, Zhonghong Ou, and Xuesong Li. Revgnn: Negative sampling enhanced contrastive graph learning for academic reviewer recommendation. ACM Transactions on Information Systems (TOIS), 43(1):1-26, 2024.
[51] Junyi Gao, Yinghao Zhu, Wenqing Wang. Zixiang Wang. Guiying Dong, Wen Tang, Hao Wang. Yasha Wang, Ewen M Harrison, and Liantao Ma. A comprehensive benchmark for covid-19 predictive modeling using electronic health records in intensive care. Patterns, 5(4), 2024.
[52] Yueying Wu. Junyi Gao, Wen Tang, Chunyan Su, Yinghao Zhu, Tianlong Wang, Ling Wang, Weibin Liao, Xu Chu, Yasha Wang. et al. Exploring the relationship between dietary intake and clinical outcomes in peritoneal dialysis patients stratified by serum albumin levels: A 12-year follow-up using fine-grained electronic medical records data. Health Data Science, 5:0280, 2025.
[53] Liantao Ma, Chaohe Zhang, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Yinghao Zhu, Tianlong Wang, Xinyu Ma, Yasha Wang, Wen Tang, et al. Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. Patterns, 4(12), 2023.
[54) Deepak Kumar Panda and Sanjog Ray. Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review. Journal of Intelligent Information Systems, 59(2):341-366 2022.
[55] Homanga Bharadhwaj. Meta- letanlea for user cold-start recommendation. In International Joint Conference Neural Networks (LJCNN), pages 1-8. IEEE, 2019.
[56] Xidong Feng, Chen Chen, Dong Li. Mengchen Zhao, Jianye Hao, and Jun Wang. Cmml: Con- textual modulation meta learning for cold-start recommendation. In International Conference on Information and Knowledge Management (CIKM), pages 484-493, 2021.
[57] Yuchen Li, Haoyi Xiong, Linghe Kong, Qingzhong Wang. Shuaiqiang Wang, Guihai Chen, and Dawei Yin. S2phere: Semi-super pre-training for web search over heterogeneous learning to rank data. In Conference on Knowledge Discovery and Data Mining (KDD), pages 4437-4448, 2023.
[58] Yuchen Li, Haoyi Xiong, Yongqi Zhang. Jiang Bian, Tianhao Peng, Xuhong Li, Shuaiqiang Wang, Linghe Kong, and Dawei Yin. Rankelectra: Semi-supervised pre-training of tearning-to- rank electra for web-scale search. In Conference on Knowledge Discovery and Data Mining (KDD), pages 2415 2425, 2025.
[59] Luo Ji, Qi Qin, Bingqing Han, and Hongxia Yang. Reinforcement learning to optimize lifetime value in cold-start recommendation. In International Conference on Information and Knowledge Management (CIKM), pages 782-791, 2021.
[60] Yanan Wang, Yong Ge, Zhepeng Li, Li Li, and Rui Chen. M3rec: A context-aware offline meta-level model- reinforcement learning approach for cold-start recommendation. ACM Transactions on Information Systems (TOIS), 42(6):1 27, 2024.
[61] Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, and Saptarshi Ghosh. Legal case document summarization: Extractive and abstractive methods and their evaluation. In Proceedings of the 2nd Conference of the Asia- Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference Natural Language Processing, pages (048-1064, 2022.
14

---

A Statistic of Datasets
We present the detailed size and composition three MLLG datasets in Table. 4.
Table 4: Statistical information of three MLLG datasets.
Datasets
#Train
#Test
Cochrane [2]
3,979
480
el.ife [25]
4,587
241
Plos_genetics [2]
4,000
300
B Details of Metrics
B.1 Heterogeneity Assessment Metries
Word Count Word Count quantitative analysis of sentence length, operationalized as the average number of words per sentence. This metric serves important stylistic feature in textual analysis.
The average Word Count was calculated using the following formula:
Total number of words
Word Count
(4)
Total number of sentences
Document Complexity and Readability Score (DCRS) The Document Complexity and Readabil- ity Score (DCRS) represents significant advancement in the field of text readability assessment, offering a multidimensional approach to quantifying textual complexity. Unlike traditional readability formulas such as flesch-Kincaid SMOG that primarily rely on surface-level features, DCRS integrates lexical, syntactic, semantic, and discourse-level features to provide more comprehensive evaluation of document readability.
DCRS is defined as a weighted composite metric:
DCRS = aL BS +C
where L represents lexical complexity, S denotes syntactic complexity, C' indicates conceptual density, and D measures discourse coherence. The coefficients a, 3, 7, and å are empirically derived weights that reflect the relative contribution of each component overall readability, with a a 3
1.
Readability Evaluation of DeepSeek-V3 We use DeepSeek- V3 to evaluate the readability of the text, employing the following prompt:
Prompt used for generating readability evaluation by DeepSeek- V3
TEMPLETE r"" [Task]
Evaluate the readability of two medical texts (1-10 scale) using these criteria:
1. Conciseness (simple vocabulary/short sentences)
2. Structural clarity (logical flow/hierarchical explanations)
3. Terminology handling (necessary jargon with explanations)
4. Comprehension ease (quick understanding for non-experts)
[The Start of Medical Text 1] imedical.text_11 [The End Medical Text 1]
15

---

[The Start of Medical Text 2] [medical_text_21 [The End of Medical Text 2]
[Scoring Guide]
10:
Fully understandable without medical background
7-9:
Minimal jargon explained through context
4-6:
Requires basic medical knowledge
1-3:
Excessive unexplained technical terms
[Output Instructions]
Output ONLY two numerical scores (e.g., "7 9") on the first line NOT include any explanations, analysis, or additional "
B.2 Content Preservation and Quality Metrics
ROUGE Metrics The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) suite provides metrics for assessing the quality of generated summaries by measuring overlap with reference texts:
ROUGE-1 (R-1) calculates the overlap of unigrams (individual words) between the generated text reference text, reflecting content coverage the word level.
ROUGE-2 (R-2) measures the overlap of bigrams (word pairs) between the generated and reference texts, capturing local word order and phrasal information.
ROUGE-L (R-L) computes the longest common subsequence between the generated and reference texts, evaluating fluency and word order without requiring consecutive matches.
These ROUGE variants collectively provide insights into how well the simplified text preserves essential content from the expert source while using accessible language. Higher ROUGE scores indicate better content preservation.
BLEU Score The Bilingual Evaluation Understudy (BLEU) metric, initially developed for machine translation evaluation, has been adapted for text simplification tasks. BLEU calculates the precision of n-gram matches between the generated text and reference text, with a penalty for brevity. The score ranges from 0 to 1, with higher values indicating greater similarity reference texts.
BLEU is particularly valuable for assessing the linguistic quality and fluency of generated lay texts, complementing the recall-oriented nature of ROUGE metrics. The formula incorporates various n-gram precisions:
N Wn log Pr.
BLEU BP exp
Σ
where BP the brevity penalty, w, are weights for different n-gram precisions, and Pn. represents the precision for n-grams of length
C Hyperparameter Settings
We present the detailed hyperparameter settings in Table. 5.
16

---

Table Hyperparameter settings for fine-tuning the LLM with Magical. Contrastive Loss Weight denotes the weight of the contrastive learning loss (Eq. 3) in the composite loss. refers to the
temperature coefficient used in the contrastive loss (Eq. 3). K indicates the number of top- K
Transformer layers selected in the Semantic- Semantic-Relevant Layer Identification module.
Hyperparameter
LLaMA3.2 2-38- Insttrcct
LLaMA3. 1-8--Inrruct
Qwen2.5-775 Inssrrcctt
Batch Size
64
64
128
Learning Rate
le-4
3e-4
3e-4
Contrastive Loss Weight
0.5
0.3
0.5
0.5
0.5
0.5
16
D Prompt Method
Prompt used for baseline model to finish MLLG
Simplify the following medical text for general public understanding using these criteria:
1. Conciseness (simple vocabulary/shortt sentences)
2. Structural clarity (logical flow/hierarchical explanations)
3. Terminology handling (necessary jargon with explanations)
4. Comprehension ease (quick understanding for non-experts)
Below an example:
Medical Text: (medical_text) Simplified Text: (simplified_text)
Now please perform the simplification:
Medical Text: (medical.text.1)
E Additional Experiments
E.1
Dilemma of Router-Controlled Selection
We further conduct an in-depth analysis of the Router-Controlled Selection. Specifically, we randomly select router and visualize the selection results for the target lay-style. The corresponding confusion matrix is presented in Fig. 5. Experimental results indicate that the router fails to correctly identify the target lay-style, evidenced by the confusion matrix showing near-random selection behavior. This is primarily due to the insufficiency of expert text and limited prompts in accurately representing the target lay-style. In partieular, for the MILLG task, lay-styles exhibit substantial differences in aspects such as depth of simplification and simplification strategies, making it difficult for the L.MM perform correct routing based on such vague input. These findings highlight the effectiveness of Recommendation -giieed Switch proposed by Magical.
Selecttooddellcciionn..
E.2 Sensitivity of Recommendation Performance
In this work, we employ manually selected approach to simulate the behavior of a Recommendation Agent that has not yet been fully implemented in this work. This implies that the Recommendation Agent operates with an assumed 100% accuracy, which is rarely achievable in wallworld scenarios. Therefore, it is important to investigate the sensitivity of Magical to the accuracy of recommendations. Specifically, we manually introduce incorrect recommendations into randomly selected cases to simulate a more realistic setting with imperfect Recommendation Agents. As shown in Fig. 6, we systematically reduce the recommendation accuracy to 95%, 90%, 80%, 70%, and 60%, and evaluate the corresponding performance of Magical. Experimental results show that the performance of Magical degrades the recommendation accuracy decreases. However, it is noteworthy that even
17

---

Cochrane
36, 180
35, 687
653
Ground Truth
0.6
eLife
17,882
16, 779
19,
Plos geneticc
-02
22,
21,267
23, 809
0.0
Cochrane
eLife
Plos geo__gen
Predict Result
Figure 5: Confusion matrix of predictions made by Router selection matrix for target lty-style.
with only 60% recommendation accuracy, Magical still consistently outperforms naive LoRA-based methods. This demonstrates that the superiority Magical is not solely due to external manual selection of target styles, but also from its other advanced components, such as the Semantic Invariance Constraint.
48
18
Magical on Cochrane
nOUGE-1
ROUGE-2
Magical on Plas, geneteess
LoRA Plos_genetics
15
Magical Cochrane
42
Magical Plos genaticcs
Ploss_oooeeeee peneticnssss
100%
95%
70%
60%
100%
70%
60%
Recommendation Accuracy
Recommendation Accuracy
ROUGE-3
Magical on Cochrane
BLEU
Magical on Plas genetice
LoRA Plas_ Rlos_genetics
10
Magical Cochrane
Magical on Pos. peoeticss
Lof pee_eesstics.
100%
95%
83%
70%
60%
100%
95%
70%
60%
Recommendation Accuracy
Recommendation Accuracy
Figure 6: Sensitivity analysis of accuracy of Recommendattion Agent in Magical.
18

---

Cochrane
eufe
Plos, genetics
Lingic-LoRA (7-
50
Multi-LoRA
45
Magical
ROUGE-1
Singlo-LoRA (r-/X 31
DOUGE-1
ROUGE-1
Single-LoRA (r-rx3) MoRB-LoRA
LoRA
Magical
48
Magical
42
5
6 Renk
Rank
Rank
Cochrane
elife
Plas, genetics
Single LoRA
Multi-LoRA
19
Magical
16
ROUGE-2
Single (r-/X 31
ROUGE-2
ROUGE-2
Single LoRA px
1.8
Mult-LoRA
Multi-LoRA
Magical
Magical
14
17
Rank
eLife
genetics
SingleLLRA (r mrxx 3)
Multi LoRA
42
Magical
ROUGE-L
Single-LoRA (r= 31
ROUGE-
ROUGE-L
Lingle-LoRA (r Multi-LoRA
Multi-ooA
42
Magical
46
Magical
39
39
Rank
Cochrane
eLife
Plas genetics
12
8.4
15
Single-LoRA ro 31
Single-LoRA (rmrr r 3)
BLEU
Murt--LOR
BURU
Multi-LoRA
Magical
Magical
8.0
12
Single-LoRA (r=
Muli-LoRA
Magical
Rank
Rank
Rank
Figure 7: Sensitivity analysis of Rank in Single-LoRA, Multi-LoRA and Magical. Please note that the rank used in Multi-LoRA is consistent with that of Magical. In order to ensure parameter equivalence during training. Single-LoRA adopts higher rank than Multi-LoRA sppcciicalll.. its rank is 3 that of both Multi-LoRA and Magical.
E.3 Sensitivity of Rank
We conduct sensitivity analysis on the rank of Magical and compare it with Multi-LoRA of the same rank and Single-LoRA with rank 3x larger. The experimental results are shown in Fig. 7. As illustrated, Magical consistently outperforms both Single-LoRA and MoRA in the vast majority of cases.
19

---

E.4 Evaluation of the Actual Quality of Generated Lay Texts
In this experiment, we introduce two complementary evaluation strategies reflect the actual perceived quality of generated lay texts.
Large Language Models Evaluators (Full-scale Evaluation). We used o3-mini and DeepSeek- RI to rate outputs from Magical and all baseline systems on a 1-10 scale. These models acted as automatic evaluators simulating expert judgment across the full test sets. In this experiment, selected 1-8-MM33--B-IIttuueee as backbone LLM. As shown in Table. 6, Magical consistently outperformed all baselines across all datasets, indicating its robust ability to generate high-quality lay texts.
Table 6: Performance comparison of various LoRA variants and Magical using LLMs as evaluator. All evaluation metries are defined such that higher values indicate better performance (t). The best results are highlighted in bold.
Cochrane o3-mini
eLife
Plos genetics
Methods
DeepSeek-RI
03-mini
DeepSeek-RI
03-mini
DeepSeek-RI
LLMMA3. I-8-rrrttc
LoRA
6.57
6.44
6.94
7.02
7.35
7.43
LoLoR
6.75
6.43
7.28
7.58
7.07
6.99
DoRA
6.77
6.56
7.19
7.66
7.11
7.28
PiSSA
6.54
6.33
7.30
7.36
6.83
6.87
Magical
7.53
7.66
7.66
7.92
8.02
8.01
Human Expert Evaluation (Focused-scale Evaluation). To further validate Magical's quality in real-world setting, we randomly sampled 20 examples from each MLLG dataset (60 samples total), and invited two medical data analysis experts finish the human evaluation. Each expert was shown the expert-level source text, and two lay versions generated by LoRA and Magical- with the generation sources anonymized to ensure fairness. The results, presented in Table.7, show that both experts consistently rated Magical higher than LoRA, validating that Magical substantially improves the clarity and quality of generated lay texts.
Table Performance comparison of LoRA and Magical using Expert as evaluator. All evaluation metrics are defined such that higher values indicate better performance (t). The better results are highlighted in bold.
Cochrane (#20)
eLife (420)
Plos Plos_genetics (#20) expert-1
Methods
expert-1
expert-2
expert-1
expert-2
expert-2
LLaMA3. Ins-trrruct
LoRA Magical
6.90
7.10
6.95
6.90
7.05
7.05
8.55
8.35
7.85
7.40
8.60
8.50
E.5 Semantic Invariance Evaluation via Quantitative Metric
In this experiment, we have incorporated BERTScore as a complementary evaluation metric. We select LLaMA3.J-BB-IIsttuu as the backbone LLM and conduct our experiments accordingly. The new results are presented in Table. 8, and they align well with the trends we observed using KDE, thereby further confirming the semantic fidelity of Magical.
E.6 Compared to Full-Parameter Fine-Tuning (FFT)
We added Full-Parameter Fine-Tunin Tine-Tuning (FFT) as a new baseline in our experiments. shown in Table. 9, Magical consistently outperforms the FFT across all datasets and three different backbone
20

---

Table 8: Semantic invariance evaluation of Prompt. various LoRA variants, and Magical using BertScore on three MLLG datasets. BertScore is defined such that higher values indicate better performance (t). The best results are highlighted bold.
Cochrane
eLife
Plos gensties
LLaMA3. 1-8B-Instruct
Prompt
85.81
85,25
86.73
LoRA
88.22 28.22
84.50
89.61
rsLoRA
87.73
84.71
88.71
DoRA
88.77
84.71
89.06
PiSSA
89.03
84.83
90.01
Magical
90.79
86.80
91.94
Table 9: Performance comparison FFT and Magical across three backbone LLMs on three MLLG datasets. All evaluation metrics are defined such that higher values indicate better performance (t). The better results are highlighted in bold.
Methods
#Params
Cochrune
eLifc
Plos gos_eges R-2
R-1
R-2
R-L
BLEU R-1
R-2
R-L
BLEU
R-1
R-L
BLEU
LLaMA3.2-3B I-B-trrutctt
FFT Magical
3,606M
44.19
18.28
41.37
13.84
47.01
12.72
45.01
7,20
43.50
14.12
40.25
7.79
45.33
42.36
16.66
49.16
15.47
44.03
10.24
LLaMA3. J-8B Instruct
FFT
8.030M
43.2
18.34
40.27
15.90
48.76
13.30
46,92
44.81 81
13.99
41.61
7.99
Magical
42M
45.71
19.52
42.79
16.68
50.44
15.49
48.02
8.67
48.77
16.64
45.06
11.86
(wwen2.5-7B. Instract
HFT
7,615M
45.99
19.62
42.58
15.81
47.73
14.49
45.36
7.20
45.49
15.64
43.20
9.97
Magical
42M
47.42
20.81
44.38
17.89
50.50
15.28
48.16
8.66
16.39
44.79
11.42
LLMs. Moreover, FFT introduces over 150x more trainable parameters compared to Magical. These results highlight Magical's dual advantages in performance and parameter efficiency.
E.7 Compared to Large-Scale LLMs
We added o3- 1--mmii as new baseline and applied the prompt-basee based method described in Section. 4.1. As shown in Table. 10, the performance of o3-mini with prompting was suboptimal. We argue that simple prompt strategies are insufficient to comprehensively capture the target lay-style, making non-learning- based approaches poorly adaptable to the target dataset.
Table 10: Performance comparison o3- o3-mini and Magical on three MLLG datasets. All evaluation metrics are defined such that higher values indicate better performance (t). The better results highlighted in bold.
Cochrane R-L
cLife
Plos genetics R-2
Methods
R-1
BLEU
R-2
R-L
BLEU
R-L
o3-mini
46.50
14.30
42.82
9.00
38.15
9.48
34.75
2.46
42.59
10.42
38.76
5.65
Magical
47.42
20.81
44.38
17.89
50.50
15.28
48.16
8.66
48.54
16.39
44.79
11.42
E.8 Sensitivity Analysis of K
we have added a sensitivity analysis of the top- K selection based on LLaMA3.1 1-8B-Instruct. The experimental results, shown in Table. 11, reveal that both overly small values of K (e.g., K 8) and overly large values (e.g., K 24 or 32) result degraded performance. This because selecting too many layers introduces semantic constraints to layers that not semantically relevant, leading
21

---

to interference with the LLM's functional integrity. Conversely, too few layers may fail to impose constraints on all semantically relevant layers, resulting in semantic shift.
Table 11: Sensitivity Analysis of K Magical on three MILG datasets. All evaluation metrics are defined such that higher values indicate better performance (1). The best results are highlighted in bold.
Cochrane
eLife
Plos genetics R-2
Methods
R-1
R-2
R-L
BLEU
R-1
R-2
R-L
BLEU
R-1
R-L
BLEU
LLaMA33
1-8B-Instruc
K =
44.74
19.18
41.91
15.5
49.28
15.02
47.02
8.56
48.76
16.79
45.06
12,09
K =
16
45.71
19.52
42.79
16.68
50.44
15.49
48.02
8.67
48.77
16.64
45.06
11.86
K 24
45.11
19.26
42.22
15.96
49.77
15.08
47.54
8.51
48.25
16.64
45.03
11.99
K 32
44.02
18.78
41.32
14.28
49.37
14.94
47.13
8.27
48.49
16.55
44.67
11.78
E.9 Discussion on Generalization of Magical
We investigate the generalizability of Magical in the following two important scenarios:
Generalization unseen datasets within the MILG task domain To explore this critical issue, we introduce a new MLLG dataset named EASI [3]. We selected LLaMA3.1 10MMA.11-BB-... 1-8-Inrrectt LLM, the experimental settings and result are presented Table. 12.
Instruct as backbone
Table 12:
Generalization performance of Magical unseen datasets within the MLLG task domain. All evaluation metrics are defined such that higher values indicate better performance (t). The best results are highlighted in bold.
Methods
Tuned
#Params
Train
Test
ROUGE-1
ROUGE-2
ROUGE-L
BLEU
IL -1B--Issrucc
Prompt LoRA
ESAI
37.85
21.67
36.43
15.04
A&B
20M
Cochrane
ESAI
41.25
25.21
37.73
9.42
LoRA
A&B
20M
cLife
ESAI
38.09
23.54
19.88
6.38
LoRA
A&B
20M
Plos_ coss_geeetccssss
ESAI
36.62
25.30
21.85
7.05
LoRA
A&B
20M
ESAI
ESAI
51.74
33.01
46.82
28.09
Magical
11M
ESAI
ESAI
53,82
36.55
49.87
30.57
Experimental results show that both prompt-based and LoRA-based methods exhibit poor generaliza- tion, performing significantly worse than end-to end-to-end fine-tuning with a newly initialized LoRA on the target dataset. The prompt-based approach suffers from limited performance due its inability to comprehensively capture the target lay-style. LoRA, on the other hand, is optimized toward specific lay-style on the training set, and thus demonstrates marked performance drop when there is a distribution shift between the training and test sets.
We evaluate Magical by reusing matrix A trained Cochrane, eLife, and PLOS Genetics, and introducing newly initialized matrix B, which is fine-tuned on EASI while keeping matrix A frozen. The results show that Magical outperforms even the fully fine-tuned LoRA EASI across all evaluation metrics.
Our key insight is follows: each heterogeneous MLLG dataset corresponds to unique lay-style. prompt-bane methods, LoRA, and Magical-arc not yet capable of robust generalization under truly unseen lay-style conditions.
Current
Nevertheless, we argue that Magical's matrix A constitutes a highly generalizable component. This is because Magical explicitly decouples abstractive summarization from lay-style generation. Matrix A, which responsible for summarization, independent of the lay-style and thus enjoys broad generalizability. Table. 12 is supported by the above empirical results, where Magical achieves superior performance and lower parameter overhead compared to LoRA under equivalent fine-tuning settings.
22

---

Table 13: Generalization performance Magical to law domain. All evaluation metries are defined such that higher values indicate better performance (1). The best results are highlighted in bold.
IN-Abs
IN-Ext
UK-Abs
Methods
@Params
R-1
R-2
R-L
BLEU
R-1
R-2
R-L
BLBU
R-1
R-2
R-L
BLBU
L. 1.MAA.-AA0 Instrcct
LoRA
62M
53.71
35.96
52.33
16.03
56.29
40.37
55.43
19.61
34.07
17.89
33.03
5.95
LsLoRA
62M
57.23
38.47
55.73
19.17
56.46
41.24
55.36
21.78
38.84
21.34
37.7
7.25
DeRA
64M
53.34
36.25
52.03
16.03
56.05
39.46
55.04
18.73
35.83
19.48
34.78
6.45
PISSA
62M
56.14
37.9
54.61
17.65
58.65
42.16
57.65
23.37
36.99
20.58
35.93
6.95
Magical
42M
59.23
39.79
57.45
20.35
60.07
44.81
59.1
26.73
38.72
22.28
37.70
8.36
Expert Text
Six studies (n = 478) of variable quality were included. A composite outcome of Infant Pain Scale (NIPS), Neonatal Facial Action Coding System (NFCS) and/or Premature Infant Pain Profile (PIPP) score reported in 288 infants, who not receive sweet tasting solution. Meta analysis showed significant reduction the venepuncture versus the heel lance group (SMD -0.76, 95% -1-1.00 to -0.52; 12 = 0%). When a sweet tasting solution was provided the SMD remained significant favouring the venepuncture group (SMD 0.38, 95% -0.69 to -0.07). The typical for requiring more than one skin puncture venepuncture versus heel lance (reported in 4 studies; 254) was -0.34 (95% CI -0.43 to 0.25;; 12 97%). The NNT to avoid one repeat skin puncture 4). Cry characteristics favoured the venepuncture group the differences by the provision of sweet tasting solutions prior to either procedure. Venepuncture, when performed by a skilled phlebotomist, appears to be the method of choice for blood sampling in term neonates. The use sweet tasting solution further reduces pain. Further designed randomised controlled trials should conducted settings where several individuals perform the procedures."
was (95%
reduced
Ground Truth of Lay Text (Human)
This review of trials found evidence that venepuncture, when done by a trained practitioner, caused less pain than heel lance. The use of sweet tasting solution given the baby prior to the event reduced pain further. The evidence included outcome measures using pain scales, how long baby cried and how mother rated their baby's pain.
LoRA
This review of six studies (478 babies) found that venepuncture is better than heel lance for blood sampling in term babies. use of sweet tasting solution reduces the pain associated with venepuncture. The use a sweet tasting solution does not reduce the pain associated with heel lance.
Magical
This review of six studies (involving 478 newborn babies) found that venepuncture (taking blood from vein) is less painful than heel lance (pricking the baby's heel) for babies who did not have a sweet tasting solution. When solution was given the babies before either procedure, the pain was reduced venepuncture less painful than heel lance. The reviow also found that venepuncture by skilled person is less painful than heol lance.
still
Table 14; Case study semantic fidelity of LoRA and Magical in MLLG task.
Generalization to Law Domain we further introduce legal summarization task using widely adopted dataset [61] in the community (IN-Abs, IN-Ext, and UK-Abs). We employ LLaMA3.1 1-88B- Instruct the backbone LLM and follow exactly the same experimental settings used for the MLLG task in the work. The experimental results are presented in Table. 13.
The results show that Magical outperforms all baseline methods across most metrics, with the only exception being slightly lower R-1 score than rsLoRA on the UK-Abs dataset. This demonstrates that Magical is also effective in the legal summarization domain.
23

---

E.10
Case Study
Case in MLLG Tasks We randomly select an expert text from the test set to showcase the lay language generation results of LoRA and Magical. Notably, LoRA generates the statement: "The use of sweet tasting solution does not reduce the pain associated with heel lance," which exhibits notable semantic shift from the original content. In contrast, Magical achieves effective lay adaptation while preserving semantic fidelity.
Moreover, for certain domain-specific terms such as "venepuncture" and "heel lance", Magical provides further explanatory information that absent not only LoRA but even in the ground truth lay text (Human). Such user-friendly explanations significantly enhance the comprehensibility of medical content for end users and facilitate more effective information communication.
Case in Legal Summarization Tasks We further share a similar observation we made in the legal summarization task. In this case, the assertion that "article 134 B does not apply" is the argument of Mr. Tatachari, rather than the conclusion of the High Court. In fact, the High Court concluded that "all the ingredients prescribed by the first column of article 134 B are satisfied." LoRA misinterpreted this distinction, which resulted in semantic inconsistency. Magical, in contrast, successfully accomplished the crucial task of preserving semantic fidelity in MLLG.
Original Legal Case Text
Appeal No. 801 1963.
September 2, 1959 of the MadAppeal by special leave from the judgment and decree dated ras High Court in Second Appeal No. 774 1957.
findings recorded by the High Court, it that the properties belonged temple; that they have been transferred by persons who must be deemed to be the previous managers the temple: and that they have been transferred for valuable consideration. The present suit has been brought against respondents who are appointed trustees of the temple by respondent No. 4; and so, the ingredients prescribed by first column of article 134 B are satisfied. That is why we must reject the ingenious argument urged before us by Mr. Tatachari that article B does not apply the present case.
Summary Generated Text by LoRA
The appellants filed a suit under section 87 of the Madras Hindu Religious and Charitable Endowments Act, 1951
The High Court held that article 134 B does not apply to the present case.
Summary Generated Text by Magical
The appellants filed suit against the respondents for declaration their title to certain properties
The High Court held that all the ingredients prescribed by the first column of article 134 B were satisfied
Table 15: Case study on semantic fidelity of LoRA and Magical in legal summarization tasks.
24

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claims in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
The limitations of the work discussed in detail in section 5.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
25

---

The paper provides the full set of assumptions and complete proofs for all theoretical results.
Justification:
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: The anonymized code repository link provided the abstract, and full experimental details are included in subsection 4.1 and Appendix C to ensure reproducibility.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that appropriate to the research performed.
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
26

---

The authors should provide scripts to reproduce all experimental results for the new reproducible, they Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification: Open access to the code is provided via the anonymized repository link included in the abstract.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.. cc/
for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https: / httppsscccppbpiccccceeceeecc ceroolccggiideee Codeeubbm ssi aasionPolicy) for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
proposed method and baselines. only subset of experiments should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Full experimental details included in subsection 4.1 and Appendix C to ensure reproducibility.
Guidelines:
The answer NA means that the paper does include experiments.
The experimental setting should be presented in the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: Experimental results tested multiple times ensure stability and reliability.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
27

---

The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma enror bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are range (e.g. negative enror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: Detailed specifications of the computational resources required for all experi- ments (including GPU types, memory usage) are reported in subsection 4.1.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics https: :/tpp::////uure htwp/nnuripp..ee cc cttppuullc/thicccctueeelllllllll
Answer: [Yes]
Justification: The research conducted in this paper conforms all aspects of the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
The potential societal impacts of the work are discuss in section and section
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
28

---

Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), faireess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The paper does not involve the release of any new datasets models.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g.. code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All third-party assets used in the paper (including code, data, and models) properly credited, and their licenses and terms of use are explicitly mentioned and respected.
Guidelines:
The answer NA means that the paper does use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
29

---

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. cow datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. If this information is available online, the authors encouraged to reach out the asset's s creators.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: All assets introduced in the paper well documented, and the documen- tation is provided alongside the assets in the anonymized repository link in abstract.
Guidelines:
The answer NA means that the paper does not release assets.
Researchers should communicate the details of the dataset/code/mooel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
30

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development in this research does involve LLMs as any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy fttpp::/neerips... for what should or should not be described.
31


Reviews:
[{"review_id": "WPB1JtHG99", "reviewer": "Reviewer_TVd2", "summary": "The authors aim to address the limitations of style transfer in LoRA models, particularly in the medical domain for expert to lay styles. The goal of th paper is to specifically address heterogenous datasets where the content can be quite different. The proposed method, \"Magical\", implements LoRA with a single A matrix and multiple B matrices for each different style or dataset.\n\nThe authors evaluate this method on 3 biomedical corpora across three backbone LLMs and shows performance improvements on top of vanilla LoRA.", "questions": "Are there external validations for the automated metrics? I understand the need for deterministic ways to evaluate quality, but how do we know the proposed methods do not just overfit to the measured quantity vs. actual perceived quality? For example, a small panel of physicians and patients evaluating quality.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "This is a useful and interesting paper that other researchers will appreciate reading, and represents a positive step in an area with limited attention, namely, tackling heterogeneous biomedical datasets. The method itself can be implemented with relatively few steps, and the experiments are thorough.\n\nThere are some limitations with how realistic the evaluations are, given the static nature and limited scope of the datasets, but these are general problems with medical LLM evaluation.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your acknowledgment on the usefulness of our method, the importance of the task, the thoroughness of our experiments, and the potential to engage a wider audience. We are particularly grateful for your clarification that the limitations of evaluation due to static and narrow datasets are a well-known and general challenge in the field of medical LLMs. Your perspective is highly encouraging and has motivated us to further advance the task of MLLG.\n\n> **Response to Evaluation Realism Concerns (Weaknesses#1)**\n\nWe appreciate your insightful observation. We **also acknowledge** the inherent limitations posed by the static nature and limited scope of the datasets, which affect the realism and generalizability of LLM evaluation. As you noted, this remains a **general problem** across the medical NLP community. \nDespite these challenges, we adhered to **standard academic protocols** by employing benchmark datasets [1,2,3] and evaluation metrics [4] that are **widely used in prior research**. In the manuscript, we aims to **present our observations and insights** to inspire further discussion within the community. We would like to **emphasize** that, within such a constrained setting, **Magical makes a modest yet meaningful step forward**.\n\n> **Response to External Validations for Automated Metrics (Question#1)**\n\nWe appreciate your raising the critical question: *“How do we know the proposed methods do not just overfit to the measured quantity vs. actual perceived quality?”*—a fundamental concern in evaluating MLLG systems. To address this, \nWe conducted a **human evaluation** study and invited two experts specializing in medical data analysis. We randomly selected 20 samples from each MLLG dataset (totaling 60 samples) and asked the experts to rate the quality of generated lay summaries on a scale from 1 to 10. Each expert was presented with the original expert-level text and two lay versions generated by LoRA and Magical, respectively, in a blind setting (i.e., the sources of the lay summaries were not disclosed).\n\nWe select `LLaMA3.1-8B-Instruct` as backbone LLM and the evaluation results are summarized in the table below. **Both experts consistently rated Magical significantly higher, confirming its superiority in producing high-quality, understandable lay summaries**.\n\n| Methods | Cochrane (#20) |  | eLife (#20) |  | Plos_genetics (#20) |  |\n|---|---|---|---|---|---|---|\n|  | expert-1 | expert-2 | expert-1 | expert-2 | expert-1 | expert-2 |\n| LoRA | 6.90 | 7.10 | 6.95 | 6.90 | 7.05 | 7.05 |\n| Magical | **8.55** | **8.35** | **7.85** | **7.40** | **8.60** | **8.50** |\n\nWe **acknowledge** that the current evaluation—based on only 60 samples—has significant **limitations**. We sincerely hope for your understanding that **conducting a comprehensive human evaluation is inherently a long-term effort**. This challenge is further compounded by the length of the data samples used in our study (as shown in Figure 1.a of the original manuscript, each instance averages approximately 300 words), which substantially increases the burden on human experts. Given the constraints of the rebuttal period (one week), it is challenging for us to complete a thorough human evaluation within such a limited timeframe.\n\nTo complement this, we additionally employed `o3-mini` and `DeepSeek-R1` as **LLM-based evaluators** to simulate large-scale expert-like judgments. These results, presented in the table below, consistently show that **Magical outperforms all baselines across all datasets**, further supporting that its improvements are not merely due to overfitting to specific metrics, but reflect genuine quality gains.\n\n| Methods | Cochrane |  | eLife |  | Plos_genetics |  |\n|---|---|---|---|---|---|---|\n|  | `o3-mini` | `DeepSeek-R1` | `o3-mini` | `DeepSeek-R1` | `o3-mini` | `DeepSeek-R1` |\n| LoRA | 6.57 | 6.44 | 6.94 | 7.02 | 7.35 | 7.43 |\n| rsLoRA | 6.75 | 6.43 | 7.28 | 7.58 | 7.07 | 6.99 |\n| DoRA | 6.77 | 6.56 | 7.19 | 7.66 | 7.11 | 7.28 |\n| PiSSA | 6.54 | 6.33 | 7.30 | 7.36 | 6.83 | 6.87 |\n| Magical | **7.53** | **7.66** | **7.66** | **7.92** | **8.02** | **8.01** |\n\nWe have completed the revision of our manuscript and have incorporated the details of **the human evaluation guidelines**, **the prompts used for LLM-based assessment**, and the **full results** into the updated version.\n\n> **References**\n\n[1] Paragraph-level Simplification of Medical Texts, NAACL, 2021\n\n[2] Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature, EMNLP, 2022\n\n[3] Retrieval augmentation of large language models for lay language generation, Journal of Biomedical Informatics, 2023\n\n[4] A dataset for plain language adaptation of biomedical abstracts. Nature Scientific Data, 2023\n\n***\nOnce again, we deeply appreciate your constructive feedback. Your emphasis on perceived quality evaluation is crucial for MLLG and has inspired us to push further toward building methods with real-world impact and user value.\nIf you have any further questions or concerns, please feel free to contact us at any time. We are always available and look forward to further discussions with you.\n\nBest regards,\n\nAll Authors"}, {"review_id": "hKkgzoXNKw", "reviewer": "Reviewer_DTBh", "summary": "This paper introduces Magical, a novel approach to improve how medical texts are simplified for non-experts readers. The authors found that standard LoRA has two critical limitations when applied to medical lay language generation: semantic drift, where LoRA fails to preserve the semantic fidelity during simplification, and style heterogeneity, where different medical datasets require different simplification styles, but standard LoRA struggles to adapt to this diversity. The authors propose use to an asymmetric LoRA architecture to address these issues, using a shared matrix A to handle abstractive summarization while maintaining semantic accuracy, and multiple matrices B to enable adaptation to diverse simplification needs. The authors report Magical outperformed standard LoRA and its variants across three medical datasets (Cochrane, eLife, Plos_genetics).", "questions": "The LLMs evaluated in the paper are relatively small (under 8B). Have you benchmarked the performance of stronger models, such as o3-mini or Claude?", "limitations": "yes", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths:\n* The paper is well-motivated and addressed an important problem - converting complex medical texts into language that non-experts can understand. This is important for improving public health literacy and patient adherence to medical advice.\n* The proposed asymmetric LoRA architecture is used in a novel setting.  \n* The paper is well-written, well-structured, and easy to follow.\n\n\n\n### Weakness\n\n* The authors use BLEU and ROUGE as the main metrics to evaluate the quality of medical lay language generation. However, since these metrics rely on surface-form n-gram overlap, they may not capture the true quality of the generated medical lay language. Human evaluation may be needed, and using a strong language model as a judge—such as o3—could also be a reasonable proxy.\n\n* The authors focus on comparing against standard LoRA as the baseline, but a comparison to full-parameter fine-tuning should have been included as well.\n\n* Following my previous two points, I’m not fully convinced by the reported effectiveness of the proposed asymmetric LoRA in medical lay language generation.", "was_revised": true, "final_justification": "Author's rebuttal enhances paper's empirical contributions, I raised my score", "rebuttal": "Thank you for your acknowledgment on the importance of our task and the quality of our manuscript. We are especially grateful for your critical comments, which prompted deep reflection on the limitations of our original submission and motivated us to substantially improve our work. Below, we address your concerns point by point.\n\n> **Response to Evaluation of the Actual Quality of Generated Lay Texts (Weakness#1)**\n\nThank you for your valuable suggestion. We would like to clarify that **our initial choice of BLEU and ROUGE metrics was based on established practices in prior MLLG literature [1]**, where these metrics have been widely adopted. However, we agree with you that such metrics do not fully reflect the actual perceived quality of generated lay texts.\n\nTo address this important limitation, we incorporated two complementary evaluation strategies:\n\n1. *Large Language Models as Evaluators (Full-scale Evaluation)*:\n\nFollowing your suggestion, we used `o3-mini` and `DeepSeek-R1` to rate outputs from Magical and all baseline systems on a 1–10 scale. These models acted as automatic evaluators simulating expert judgment across the full test sets. In this experiment, we selected `LLaMA3.1-8B-Instruct` as backbone LLM. As shown in the Table below, **Magical consistently outperformed all baselines across all datasets**, indicating its robust ability to generate high-quality lay texts.\n\n| Methods | Cochrane |  | eLife |  | Plos_genetics |  |\n|---|---|---|---|---|---|---|\n|  | `o3-mini` | `DeepSeek-R1` | `o3-mini` | `DeepSeek-R1` | `o3-mini` | `DeepSeek-R1` |\n| LoRA | 6.57 | 6.44 | 6.94 | 7.02 | 7.35 | 7.43 |\n| rsLoRA | 6.75 | 6.43 | 7.28 | 7.58 | 7.07 | 6.99 |\n| DoRA | 6.77 | 6.56 | 7.19 | 7.66 | 7.11 | 7.28 |\n| PiSSA | 6.54 | 6.33 | 7.30 | 7.36 | 6.83 | 6.87 |\n| Magical | **7.53** | **7.66** | **7.66** | **7.92** | **8.02** | **8.01** |\n\n2. *Human Expert Evaluation (Focused-scale Evaluation)*:\n\nTo further validate Magical’s quality in a real-world setting, we randomly sampled 20 examples from each MLLG dataset (60 samples total), and invited two medical data analysis experts to finish the **human evaluation**. Each expert was shown the expert-level source text, and two lay versions generated by LoRA and Magical—with the generation sources anonymized to ensure fairness. The results, presented in the Table below, show that **both experts consistently rated Magical higher than LoRA**, validating that Magical substantially improves the clarity and quality of generated lay texts.\n\n| Methods | Cochrane (#20) |  | eLife (#20) |  | Plos_genetics (#20) |  |\n|---|---|---|---|---|---|---|\n|  | expert-1 | expert-2 | expert-1 | expert-2 | expert-1 | expert-2 |\n| LoRA | 6.90 | 7.10 | 6.95 | 6.90 | 7.05 | 7.05 |\n| Magical | **8.55** | **8.35** | **7.85** | **7.40** | **8.60** | **8.50** |\n\nWe **acknowledge** that the current *Human Expert Evaluation*—based on only 60 samples—has significant **limitations**. We sincerely hope for your understanding that **conducting a comprehensive human evaluation is inherently a long-term effort**. This challenge is further compounded by the length of the data samples used in our study (as shown in Figure 1.a of the original manuscript, each instance averages approximately 300 words), which substantially increases the burden on human experts. Given the constraints of the rebuttal period (one week), it is challenging for us to complete a thorough human evaluation within such a limited timeframe.\n\nWe have completed the revision of our manuscript and have incorporated the details of **the human evaluation guidelines**, **the prompts used for LLM-based assessment**, and the **full results** into the updated version.\n\n> **Response to Full-Parameter Fine-Tuning (FFT) Baseline (Weakness#2)**\n\nThank you for your suggestion. We agree that FFT is an important baseline, as it helps readers better understand why existing MLLG studies tend to choose LoRA over FFT.\n\nIn response to your suggestion, we added FFT as a new baseline in our experiments. As shown in the Table below, **Magical consistently outperforms the FFT across all datasets and three different backbone LLMs**. Moreover, FFT introduces **over 150× more trainable parameters** compared to Magical. These results highlight Magical’s dual advantages in performance and parameter efficiency.\n\n| Methods | #params | Cochrane |  |  |  | eLife |  |  |  | Plos_genetics |  |  |  |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|  |  | R-1 | R-2 | R-L | BLEU | R-1 | R-2 | R-L | BLEU | R-1 | R-2 | R-L | BLEU |\n|`LLaMA3.2-3B-Instruct`|  |  |  |  |  |  |  |  |  |  |  |  |  |\n| FFT | 3,606M | 44.19 | 18.28 | 41.37 | 13.84 | 47.01 | 12.72 | 45.01 | 7.20 | 43.5 | 14.12 | 40.25 | 7.79 |\n| Magical | 24M | **45.33** | **19.39** | **42.36** | **16.66** | **49.16** | **14.68** | **46.91** | **8.30** | **47.50** | **15.47** | **44.03** | **10.24** |\n| `LLaMA3.1-8B-Instruct` |  |  |  |  |  |  |  |  |  |  |  |  |  |\n| FFT | 8,030M | 43.2 | 18.34 | 40.27 | 15.90 | 48.76 | 13.30 | 46.92 | 7.82 | 44.81 | 13.99 | 41.61 | 7.99 |\n| Magical | 42M | **45.71** | **19.52** | **42.79** | **16.68** | **50.44** | **15.49** | **48.02** | **8.67** | **48.77** | **16.64** | **45.06** | **11.86** |\n| `Qwen2.5-7B-Instruct` |  |  |  |  |  |  |  |  |  |  |  |  |  |\n| FFT | 7,615M | 45.99 | 19.62 | 42.58 | 15.81 | 47.73 | 14.49 | 45.36 | 7.20 | 45.49 | 15.64 | 43.20 | 9.97 |\n| Magical | 42M | **47.42** | **20.81** | **44.38** | **17.89** | **50.50** | **15.28** | **48.16** | **8.66** | **48.54** | **16.39** | **44.79** | **11.42** |\n\nOur **perspective on FFT *vs.* LoRA** is as follows: \nWe actually considered using FFT for SFT, yet LoRA are a more **economic and effective** choice for MLLG task. The reason is that for MLLG, we often do not have a large amount of training data, and the FFT strategy may lead to **overfitting** (e.g., learning to respond with some specific tokens instead of learning what the style really depicts). Research works like [2,3,4] also demonstrate that FFT suffers from overfitting and cannot perform as good as other PEFT methods. Moreover, the relatively high training cost of FFT may pose a bottleneck in terms of **training efficiency**.\nTherefore, taking both training efficiency and effectiveness into consideration, we choose to apply LoRA as the optimization algorithm for SFT.\n\n> **Response to Large-Scale LLMs as Baselines (Question#1)**\n\nThank you for your suggestion. We believe that adding a large-scale LLM such as `o3-mini` as a new baseline would help readers better appreciate the performance advantages of Magical.\n\nFollowing your suggestion, we added `o3-mini` as a new baseline and applied the prompt-based method described in Section 4.1 of the original manuscript. As shown in the Table below, **the performance of `o3-mini` with prompting was suboptimal**, consistent with the conclusions of our original submission. We argue that simple prompt strategies are insufficient to comprehensively capture the target lay-style, making non-learning-based approaches poorly adaptable to the target dataset. This observation is also reported in recent literature. For example, [5] presents results showing that LoRA-based fine-tuned LLaMA-2 outperforms GPT-4.\n\n| Methods | #params | Cochrane |  |  |  | eLife |  |  |  | Plos_genetics |  |  |  |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|  |  | R-1 | R-2 | R-L | BLEU | R-1 | R-2 | R-L | BLEU | R-1 | R-2 | R-L | BLEU |\n| `o3-mini` | N/A | 46.50 | 14.30 | 42.82 | 9.00 | 38.15 | 9.48 | 34.75 | 2.46 | 42.59 | 10.42 | 38.76 | 5.65 |\n| Magical | 42M | **47.42** | **20.81** | **44.38** | **17.89** | **50.50** | **15.28** | **48.16** | **8.66** | **48.54** | **16.39** | **44.79** | **11.42** |\n\n> **Response to Formatting Issues**\n\nWe greatly appreciate your attention to detail. We sincerely apologize for the formatting errors caused by our oversight. In response, we carefully re-checked our full manuscript against the official NeurIPS formatting guidelines and have corrected all inconsistencies. We take this issue seriously and thank you again for bringing it to our attention.\n\n> **References**\n\n[1] A dataset for plain language adaptation of biomedical abstracts. Nature Scientific Data, 2023\n\n[2] Low-rank adaptation for multilingual summarization: An empirical study, NAACL, 2023\n\n[3] Parameter-efficient orthogonal finetuning via butterfly factorization. ICLR, 2024\n\n[4] AdaLoRA: Adaptive budget allocation for parameter-efficient fine-tuning. ICLR, 2023\n\n[5] Retrieval augmentation of large language models for lay language generation. Journal of Biomedical Informatics, 2024\n\n***\nWe sincerely thank you once again for your thoughtful guidance. Your feedback has been immensely valuable to us, and incorporating your suggestions has significantly improved the quality of our manuscript.\nIf you have any further questions or concerns, please feel free to contact us at any time. We are always available and look forward to further discussions with you.\n\nBest regards,\n\nAll Authors"}, {"review_id": "EYXyVjBSAI", "reviewer": "Reviewer_gwM8", "summary": "This paper introduces Magical, a novel asymmetric LoRA-based architecture designed for Medical Lay Language Generation, a task that involves simplifying complex biomedical texts for non-expert audiences. The authors propose: 1) A shared low-rank matrix A for semantic content modeling (abstractive summarization). 2) Multiple isolated low-rank matrices B for stylistic variation corresponding to heterogeneous lay styles. 3) A Semantic Invariance Constraint applied on matrix A using contrastive learning and semantic-relevant layer identification. 4) A Recommendation-guided Switch that leverages an external agent to dynamically select the appropriate B matrix based on the target lay style. Experiments are conducted across three real-world datasets (Cochrane, eLife, Plos_genetics) and three LLMs (LLaMA3.2-3B, LLaMA3.1-8B, Qwen2.5-7B). Magical achieves state-of-the-art performance compared to various baselines, including rsLoRA, DoRA, PiSSA, and HydraLoRA.", "questions": "1 - The proposed asymmetric LoRA framework closely resembles HydraLoRA [24] and Mixture-of-LoRAs [32], both of which use separate low-rank branches and routing mechanisms. Can the authors clarify in more detail how Magical provides a non-trivial architectural or theoretical advancement over these prior approaches? In particular, is there any scenario where HydraLoRA fails but Magical succeeds (beyond empirical results)?\n\n\n2 - The Recommendation-guided Switch is presented as a key component enabling layperson-tailored adaptation. However, the current implementation relies on manual selection of matrix B. Could the authors elaborate on why a basic learning-based selector (e.g., using dataset metadata, prefix tuning, or retrieval) was not attempted? Additionally, how would performance degrade under imperfect or noisy recommendation in real-world settings?\n\n3 - In Equation (2), the aggregation of BiAx using α_i is central, yet the mechanism for computing α_i during training and inference is under-specified. Are α_i values learned, externally set, or inferred dynamically? If the switch is hard-coded during training, how does this affect generalization to unseen or mixed-style data?\n\n4 - The proposed design is evaluated solely on medical lay language generation. Can the authors provide any evidence or discussion regarding how the method would generalize to other domains requiring stylistic or semantic transformation (e.g., legal summarization, educational simplification)?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n\n1 - MLLG is a high-impact application with societal relevance. The use of PEFT methods such as LoRA (and Magical) enables feasible deployment on limited-resource settings.\n\n2 - The authors perform a solid preliminary investigation showing that multi-source MLLG datasets are highly heterogeneous in structure and style. They identify specific shortcomings of single-LoRA, supporting their architectural decisions with empirical evidence (e.g., Table 1).\n\n3 - Magical achieves significant improvements in both ROUGE and BLEU, while reducing trainable parameters by ~31.66% (Table 2). Moreover, ablation studies (Table 3) are thorough and support the importance of each component.\n\n\n**Weaknesses**:\n\n1 - The core idea (separate low-rank branches + a selector) is incremental over HydraLoRA [24] and Mixture-of-LoRAs [32]. The only new twist is the semantic contrastive loss on A, whose technical depth is modest.\n\n2 - The Recommendation-guided Switch is central to the claim of “layperson-tailored adaptation,” yet the recommender is manually simulated. As stated in Section 4.1 and 5, “we adopt a manually specified matrix B,” which significantly reduces real-world applicability and novelty\n\n3 - Minor issues: two figures (Figs. 6-7) appear in the appendix but are referenced in Sec. 4 without page cues; typos (“BLUE” for BLEU in multiple tables).\n\n4 - While KDE plots (Fig. 4) are  good qualitative results; no quantitative semantic similarity metric (e.g., BERTScore, CheXplain) is provided to verify “semantic invariance”.", "was_revised": true, "final_justification": "After the rebuttal and the discussion with the authors, my final recommendation reflects that the paper's empirical contributions now outweigh its weaknesses, though some concerns remain.\n\nHere is a summary of my final assessment:\n\n* **Strengths (Resolved Issues):** The authors successfully addressed key weaknesses by adding new experiments. The inclusion of BERTScore provides robust quantitative validation for semantic fidelity, and the evaluation on a legal summarization task demonstrates commendable generalization beyond the medical domain. These additions substantially improve the paper's quality.\n\n* **Remaining Concerns:**\n    * The core architectural novelty remains incremental, adapting existing Mixture-of-LoRAs frameworks like HydraLoRA to solve a specific, albeit important, problem in MLLG.\n    * The \"Recommendation-guided Switch,\" a central component, relies on manual selection. This limits the system's practical autonomy and makes the \"layperson-tailored adaptation\" a proof-of-concept rather than a fully automated feature.\n\n* **Final Verdict:** The paper is a solid piece of empirical work, and the authors' efforts during the rebuttal have been exemplary. The confirmed strengths and thorough experimentation now justify acceptance. The \"Borderline Accept\" score reflects the balance between these strong empirical results and the lingering concerns about conceptual novelty and the lack of automation in a key component.", "rebuttal": "We sincerely thank you for acknowledging the importance of the task, the sufficiency of our preliminary investigation, and the comprehensiveness of our experiments. We deeply value the constructive criticisms you raised and respond to each concern in detail below.\n\n> **Response to Novelty (Weakness#1 & Weakness#2)**\n\nWe sincerely appreciate your valuable feedback and understand your concerns regarding the novelty of the manuscript. We would like to **clarify** that novelty in academic research should not be limited to proposing disruptive algorithms, but should also encompass the ability to **inspire interest, provoke community discussion, and open up new directions for future work**. In this regard, we argue that Magical achieves meaningful novelty through the following contributions:\n\n1. Magical provides a comprehensive review of existing approaches and proposes a **new perspective** backed by **empirical evidence**: *“Standard LoRA fails to meet the requirement for semantic fidelity and diverse lay-style generation in the MLLG task.”*\n\n2. Magical performs **timely adaptations** and responds to the unique challenges faced by LoRA in the MLLG setting by offering a **tailored solution** that directly addresses these shortcomings.\n\n3. Most importantly, Magical serves as a **catalyst** for rethinking LoRA-based approaches in MLLG, **encouraging the community to explore future directions** for semantic preservation and adaptive lay-style generation.\n\nWhile we acknowledge that Magical is inspired by HydraLoRA, we highlight that the two methods **differ in their applicability**. In fact, Magical identifies and remedies a limitation of HydraLoRA, which we elaborate on in the next section.\n\n> **Response to Comparison Between HydraLoRA and Magical (Question#1), and on Not Using a Basic Learning-Based Selector(Question#2)**\n\nThank you for your valuable suggestion.  In response, we revisited the tasks addressed in HydraLoRA, such as *Summarization*, *Closed QA*, and *Information Extraction*.\nIt is evident that these tasks are **clearly distinguishable**, and simple prompts are sufficient for an LLM to differentiate among them and select the appropriate matrix B.\nHowever, when it comes to heterogeneous MLLG tasks, it becomes **challenging to accurately convey the target lay-style using simple prompts** (as discussed in line 227 of our manuscript). This limitation hinders the effective automatic selection of matrix B.\n\nTo validate this point, we conducted a detailed experiment in **Appendix E.1: Dilemma of Router-Controlled Selection**, where we adopted HydraLoRA’s original design using a learnable router (i.e., **a basic learning-based selector, as you suggested**). The results demonstrate that the **router fails to learn meaningful selections**—our Figure 5 shows a confusion matrix where the learned routing behavior is indistinguishable from random selection.\n\nFurther evidence is provided in Table 3 (“Switch → Router” ablation), where replacing our manual switching with routing leads to a significant drop in performance.\n\nThese findings show some **limitations of HydraLoRA**: router-based selection collapses when the task cannot be explicitly described via simple prompts, which is often the case in MLLG. Although Magical uses a simpler, manually defined selection strategy (our **optimal setting** assumes that decisions are made based on a *Recommendation Agent*), **it addresses this core failure effectively**.\n\n> **Response to Sensitivity of Recommendation Agent Performance (Question#2)**\n\nWe share your concern about whether the overall performance of Magical is sensitive to the quality of the recommendation agent. In **Appendix E.2: Sensitivity of Recommendation Performance**, we present experiments that simulate reduced accuracy in recommendation. Notably, even when the agent's hit rate drops to 60%, Magical still outperforms standard LoRA.\n\n> **Response to Alpha in Equation (2) and Generalization to Unseen Data (Question#3)**\n\nIn Equation (2), the vector $\\alpha$ is a binary indicator: the entry corresponding to the selected matrix B is set to 1, while all others are set to 0.\n\nSame as you, we are also deeply concerned with Magical's generalization ability on unseen datasets. To explore this critical issue, we introduce a **new MLLG dataset** named EASI [1]. We selected `LLaMA3.1-8B-Instruct` as backbone LLM, the experimental settings and result are presented in the table below.\n\n|Methods|tuned modules|#Params|train data|test data|ROUGE-1|ROUGE-2|ROUGE-L|BLEU|\n|-|-|-|-|-|-|-|-|-|\n|Prompt|||||37.85|21.67|36.43|15.04|\n|LoRA|A&B|20M|Cochrane|ESAI|41.25|25.21|37.73|9.42|\n|LoRA|A&B|20M|eLife|ESAI|38.09|23.54|19.88|6.38|\n|LoRA|A&B|20M|Plos_genetics|ESAI|36.62|25.30|21.85|7.05|\n|LoRA|A&B|20M|ESAI|ESAI|51.74|33.01|46.82|28.09|\n|Magical|B|11M|ESAI|ESAI|**53.82**|**36.55**|**49.87**|**30.57**|\n\nExperimental results show that **both prompt-based and LoRA-based methods (Rows 1–4) exhibit poor generalization**, performing significantly worse than end-to-end fine-tuning with a newly initialized LoRA on the target dataset (Row 5). The prompt-based approach suffers from limited performance due to its inability to comprehensively capture the target lay-style. LoRA, on the other hand, is optimized toward a specific lay-style on the training set, and thus demonstrates a marked performance drop when there is a distribution shift between the training and test sets.\n\nWe evaluate Magical by reusing matrix A trained on Cochrane, eLife, and PLOS Genetics, and introducing a newly initialized matrix B, which is fine-tuned on EASI while **keeping matrix A frozen** (see Row 6). The results show that **Magical outperforms even the fully fine-tuned LoRA on EASI** (Row 5) across all evaluation metrics.\n\nOur **key insight** is as follows: each heterogeneous MLLG dataset corresponds to a unique lay-style. Current approaches—including prompt-based methods, LoRA, and Magical—are not yet capable of robust generalization under truly unseen lay-style conditions.\n\nNevertheless, we argue that **Magical’s matrix A constitutes a highly generalizable component**. This is because Magical explicitly decouples abstractive summarization from lay-style generation. Matrix A, which is responsible for summarization, is independent of the lay-style and thus enjoys broad generalizability.\n\nThis claim is supported by the above empirical results, where Magical achieves **superior performance** and **lower parameter** overhead compared to LoRA under equivalent fine-tuning settings.\n\n> **Response to Writing Issues (Weakness#3)**\n\nWe are grateful for your close reading and for pointing out the writing issues in our manuscript. We sincerely apologize for these oversights. In response, we have thoroughly reviewed and revised the manuscript to correct all writing and formatting issues. Thank you again for your careful attention.\n\n> **Response to the Use of a Quantitative Semantic Similarity Metric (Weakness#4)**\n\nWe appreciate your valuable suggestion regarding the evaluation of semantic similarity. In response, we have incorporated BERTScore as a complementary evaluation metric. We select `LLaMA3.1-8B-Instruct` as the backbone LLM and conduct our experiments accordingly. The new results are presented in the Table below, and **they align well with the trends we observed using KDE**, thereby further confirming the semantic fidelity of Magical.\n\n|Methods|Cochrane|eLife|Plos_genetics|\n|-|-|-|-|\n||BertScore|BertScore|BertScore|\n|Prompt|85.81|85.25|86.73|\n|LoRA|88.22|84.50|89.61|\n|rsLoRA|87.73|84.71|88.71|\n|DoRA|88.77|84.71|89.06|\n|PiSSA|89.03|84.83|90.01|\n|Magical|**90.79**|**86.80**|**91.94**|\n\n> **Response to Generalization to Other Domains (Question#4)**\n\nWe fully acknowledge your point that evaluating Magical on datasets from other domains is essential for assessing its generalizability.\n\nFollowing your suggestion, we further introduce a legal summarization task using a widely adopted dataset [2] in the community (IN-Abs, IN-Ext, and UK-Abs). We employ `LLaMA3.1-8B-Instruct` as the backbone LLM and follow exactly the same experimental settings as used for the MLLG task in the manuscript. The experimental results are presented in the table below.\n\n|Methods|#params|IN-Abs||||IN-Ext||||UK-Abs||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|||R-1|R-2|R-L|BLEU|R-1|R-2|R-L|BLEU|R-1|R-2|R-L|BLEU|\n|LoRA|62M|53.71|35.96|52.33|16.03|56.29|40.37|55.43|19.61|34.07|17.89|33.03|5.95|\n|rsLoRA|62M|57.23|38.47|55.73|19.17|56.46|41.24|55.36|21.78|**38.84**|21.34|37.70|7.25|\n|DoRA|64M|53.34|36.25|52.03|16.03|56.05|39.46|55.04|18.73|35.83|19.48|34.78|6.45|\n|PiSSA|62M|56.14|37.90|54.61|17.65|58.65|42.16|57.65|23.37|36.99|20.58|35.93|6.95|\n|Magical|42M|**59.23**|**39.79**|**57.45**|**20.35**|**60.07**|**44.81**|**59.10**|**26.73**|38.72|**22.28**|**37.70**|**8.36**|\n\nThe results show that **Magical outperforms all baseline methods across most metrics**, with the only exception being a slightly lower R-1 score than rsLoRA on the UK-Abs dataset. This demonstrates that Magical is also effective in the legal summarization domain.\n\n> **References**\n\n[1] Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts, AAAI, 2023\n\n[2] Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation, AACL | IJCNLP，2022\n\n***\nWe sincerely thank you once again for your thoughtful guidance. Your feedback has been immensely valuable to us, and incorporating your suggestions has significantly improved the quality of our manuscript.\nIf you have any further questions or concerns, please feel free to contact us at any time. We are always available and look forward to further discussions with you.\n\nBest regards,\n\nAll Authors"}, {"review_id": "tl1xdkbNiP", "reviewer": "Reviewer_1PZy", "summary": "This paper addresses the challenge of MLLG, focusing on transforming biomedical content into layman-accessible language. The authors critique the use of standard LoRA in handling semantic fidelity and stylistic diversity across heterogeneous multi-source datasets. They propose Magical, a novel asymmetric LoRA architecture that separates semantic abstraction (via shared matrix A) and lay-style adaptation (via multiple isolated matrices B). The contributions mainly focus on 1) Semantic invariance constraint on matrix A using semantic-relevant layer identification and contrastive learning to preserve semantic fidelity. 2) Layperson-tailored adaptation via dataset-specific B matrices and a recommendation-guided switch for selective matrix activation. 3) Empirical results on three MLLG datasets showing Magical consistently outperforms LoRA and its recent variants in both semantic fidelity and style diversity.", "questions": "1. Could Magical be extended to more domains such as multi-lingual lay language generation and other domains including legal simplification or educational content generation, and what changes would be needed to handle such complexity?\n2. Have you conducted or considered conducting any human subject evaluations to assess whether generated lay texts are truly easier to understand?\n3. How sensitive is Magical’s performance to the choice of the number of B matrices and the top-K semantic layers?", "limitations": "The paper does acknowledge that the Recommendation Agent is not implemented, and it also discusses challenges around modeling layperson profiles and proposes future directions such as meta-learning and RL. However, it could be further strengthened by discussing scalability, domain generalizability, data imbalance, and ethical risks caused by hallucination or oversimplification of the generated medical content.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Quality\n### Strengths:\n- Rigorous evaluation across three real-world datasets, including Cochrane, eLife, Plos_genetics.\n- Very detailed ablation studies, which effectively isolate component contributions.\n### Weaknesses:\n- Due to the subjective nature of layman-oriented content comprehension, human evaluation metrics are necessary in this task setting to assess whether the model-generated content is acceptable from the perspective of real-world target users.\n\n## Clarity\n### Strengths：\n- The framework is well-illustrated through Figure 3 and the proposed methodology and key components are clearly presented in Section 3.2 and 3.3.\n### Weaknesses:\n- Figure 2 lack detailed captions or axis labels, reducing their standalone interpretability.\n- There is a lack of impact analysis of the number of B matrices and the choice of top-K semantic layers.\n\n## Significance\n### Strengths：\n- The paper addresses a meaningful social need, which is improving public access to biomedical knowledge.\n### Weaknesses:\n- Despite its significance, the model has limited generalizability beyond MLLG without adaptation, especially since it depends on labeled expert-lay pairs.\n- As the provided method is very domain-specific, there is limited discussion on how it can be integrated into existing clinical or public health communication pipelines.\n- A main concern is that, while the proposed method is specifically designed within the MLLG domain, many of its core innovations, such as asymmetric low-rank adaptation, semantic invariance constraints, and lay-style routing, are generally applicable to other text generation tasks that require semantic preservation and stylistic transformation. However, the paper currently does not explore this broader applicability. This could make the method appear overly domain-specific, which may limit its perceived impact and usefulness to the broader NLP community.\n\n## Originality\n### Strengths：\n- It seems to be the first to explicitly decouple semantic abstraction and stylistic transformation in MLLG via asymmetric LoRA.\n### Weaknesses:\n- While inspired by HydraLoRA, the originality of some technical aspects, such as the use of external switching agents is somewhat incremental.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns, particularly regarding generalization to unseen datasets and new domains, human evaluation, hyperparameter selection, and the paper's novelty.", "rebuttal": "We sincerely appreciate your recognition of the comprehensiveness of our ablation studies, the clarity of our figures and visualizations, and the importance of the addressed task. We have carefully considered the shortcomings you pointed out in our manuscript and provide detailed responses below.\n\n> **Response to Generalization**\n\nWe appreciate your suggestion regarding the issue of generalizability. We believe that evaluating the generalization capability of Magical is a crucial step toward improving the quality of our manuscript. Specifically, we investigate the generalizability of Magical in the following two important scenarios:\n\n1. **Generalization to unseen datasets within the MLLG task domain**\n\nTo explore this critical issue, we introduce a **new MLLG dataset** named EASI [1]. We selected `LLaMA3.1-8B-Instruct` as backbone LLM, the experimental settings and result are presented in the table below.\n\n|Methods|tuned modules|#Params|train data|test data|ROUGE-1|ROUGE-2|ROUGE-L|BLEU|\n|-|-|-|-|-|-|-|-|-|\n|Prompt|||||37.85|21.67|36.43|15.04|\n|LoRA|A&B|20M|Cochrane|ESAI|41.25|25.21|37.73|9.42|\n|LoRA|A&B|20M|eLife|ESAI|38.09|23.54|19.88|6.38|\n|LoRA|A&B|20M|Plos_genetics|ESAI|36.62|25.30|21.85|7.05|\n|LoRA|A&B|20M|ESAI|ESAI|51.74|33.01|46.82|28.09|\n|Magical|B|11M|ESAI|ESAI|**53.82**|**36.55**|**49.87**|**30.57**|\n\nExperimental results show that **both prompt-based and LoRA-based methods (Rows 1–4) exhibit poor generalization**, performing significantly worse than end-to-end fine-tuning with a newly initialized LoRA on the target dataset (Row 5). The prompt-based approach suffers from limited performance due to its inability to comprehensively capture the target lay-style. LoRA, on the other hand, is optimized toward a specific lay-style on the training set, and thus demonstrates a marked performance drop when there is a distribution shift between the training and test sets.\n\nWe evaluate Magical by reusing matrix A trained on Cochrane, eLife, and PLOS Genetics, and introducing a newly initialized matrix B, which is fine-tuned on EASI while **keeping matrix A frozen** (see Row 6). The results show that **Magical outperforms even the fully fine-tuned LoRA on EASI** (Row 5) across all evaluation metrics.\n\nOur **key insight** is as follows: each heterogeneous MLLG dataset corresponds to a unique lay-style. Current approaches—including prompt-based methods, LoRA, and Magical—are not yet capable of robust generalization under truly unseen lay-style conditions.\n\nNevertheless, we argue that **Magical’s matrix A constitutes a highly generalizable component**. This is because Magical explicitly decouples abstractive summarization from lay-style generation. Matrix A, which is responsible for summarization, is independent of the lay-style and thus enjoys broad generalizability.\n\nThis claim is supported by the above empirical results, where Magical achieves **superior performance** and **lower parameter** overhead compared to LoRA under equivalent fine-tuning settings.\n\n2. **Generalization to new domains**\n\nFollowing your suggestion, we further introduce a legal summarization task using a widely adopted dataset [2] in the community (IN-Abs, IN-Ext, and UK-Abs). We employ `LLaMA3.1-8B-Instruct` as the backbone LLM and follow exactly the same experimental settings as used for the MLLG task in the manuscript. The experimental results are presented in the table below.\n\n|Methods|#params|IN-Abs||||IN-Ext||||UK-Abs||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|||R-1|R-2|R-L|BLEU|R-1|R-2|R-L|BLEU|R-1|R-2|R-L|BLEU|\n|LoRA|62M|53.71|35.96|52.33|16.03|56.29|40.37|55.43|19.61|34.07|17.89|33.03|5.95|\n|rsLoRA|62M|57.23|38.47|55.73|19.17|56.46|41.24|55.36|21.78|**38.84**|21.34|37.70|7.25|\n|DoRA|64M|53.34|36.25|52.03|16.03|56.05|39.46|55.04|18.73|35.83|19.48|34.78|6.45|\n|PiSSA|62M|56.14|37.90|54.61|17.65|58.65|42.16|57.65|23.37|36.99|20.58|35.93|6.95|\n|Magical|42M|**59.23**|**39.79**|**57.45**|**20.35**|**60.07**|**44.81**|**59.10**|**26.73**|38.72|**22.28**|**37.70**|**8.36**|\n\nThe results show that **Magical outperforms all baseline methods across most metrics**, with the only exception being a slightly lower R-1 score than rsLoRA on the UK-Abs dataset. This demonstrates that Magical is also effective in the legal summarization domain.\n\n> **Response to Human Evaluation**\n\nWe fully understand your concern regarding the need for **layperson evaluation** to assess whether the generated texts meet the expectations of target end-users. However, we would like to clarify that evaluating the quality of generated text also crucially involves **verifying factual accuracy** and **content correctness**. For lay users, detecting subtle or domain-specific inaccuracies in the generated lay text can be extremely **challenging**. Therefore, we chose to conduct **expert evaluations** instead of layperson assessments. \n\nWe sincerely **apologize** for the space limitations. We **warmly invite** you to refer to our discussion with *Reviewer TVd2* under the section \"**Response to External Validations for Automated Metrics**,\" where we provide **two evaluation methods** for assessing real-world quality, including **human evaluation**.\n\n> **Response to the Analysis of the Number of B Matrices and the Choice of Top-K Semantic Layers**\n\nThank you for highlighting this point. We agree that analyzing the sensitivity to the choice of top-K layers is important, as it supports our core claim that *different layers of LLMs encode distinct functional representations* (see Line 174 of the manuscript).\n\nFollowing your suggestion, we have added a sensitivity analysis of the top-K selection based on `LLaMA3.1-8B-Instruct`. The experimental results, shown in the table below, reveal that both overly small values of K (e.g., K = 8) and overly large values (e.g., K = 24 or 32) **result in degraded performance**. This is because selecting too many layers introduces semantic constraints to layers that are not semantically relevant, leading to interference with the LLM’s functional integrity. Conversely, too few layers may fail to impose constraints on all semantically relevant layers, resulting in semantic shift.\n\n|Methods|Cochrane||||eLife||||Plos_genetics||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n||R-1|R-2|R-L|BLEU|R-1|R-2|R-L|BLEU|R-1|R-2|R-L|BLEU|\n|K=8|44.74|19.18|41.91|15.5|49.28|15.02|47.02|8.56|48.76|**16.79**|45.06|**12.09**|\n|k=16|**45.71**|**19.52**|**42.79**|**16.68**|**50.44**|**15.49**|**48.02**|**8.67**|**48.77**|16.64|**45.06**|11.86|\n|K=24|45.11|19.26|42.22|15.96|49.77|15.08|47.54|8.51|48.25|16.64|45.03|11.99|\n|K=32|44.02|18.78|41.32|14.28|49.37|14.94|47.13|8.27|48.49|16.55|44.67|11.78|\n\nRegarding the number of B matrices, we assign one matrix B per heterogeneous dataset. This is **not treated as a hyperparameter** in our experiments but rather as **a direct reflection of the number of heterogeneous sources involved**.\n\n> **Response to the Clarity of Figure.2**\n\nThank you for your valuable suggestion. We are highly attentive to the readability and clarity of our manuscript. *In Figure 2, we project the representations of expert text samples and the corresponding generated lay text samples onto the top-2 singular directions of the semantic subspace, which form the x- and y-axes of the KDE plot.* In response, we have revised the caption of Figure 2 to improve clarity and added axis labels to the plot. \n\n> **Response to Novelty**\n\nWe sincerely appreciate your valuable feedback and understand your concerns regarding the novelty of the manuscript. We sincerely **apologize** for the space limitations. We **warmly invite** you to refer to our discussion with *Reviewer gwM8* under the section \"**Response to Novelty**,\" where we clarify our perspective on the novelty of Magical.\n\n> **Response to Clinical Application and Limitation Discussion**\n\nThank you for your valuable suggestion. We believe that expanding the discussion on how Magical can be applied to real-world clinical settings, as well as elaborating on its more limitations, is important. This would greatly enhance the clarity of our work’s practical applicability.\n\nTo this end, we offer some **preliminary insights** on how Magical might be translated into practical clinical use. We propose that its deployment follow four key periods:\n\n1. **Expert-Guided Validation**: Initially, the system should be open to medical experts to ensure the accuracy and reliability of the generated content.\n\n2. **Lay-Style Generation**: Leveraging Magical’s strengths, it can then produce diverse lay-style text, allowing users to freely select the most suitable ones based on their **latent prefenence**.\n\n3. **User-Centered Feedback and Learning**: User preferences can be collected in this phase to construct a *Recommendation Agent*.\n\n4. **Personalized Lay Text Recommendation**: Based on user feedback, the system can ultimately provide precise recommendations of preferred lay-style outputs, facilitating efficient and personalized communication.\n\nIn response to your concerns, we have added a **new Discussion section** in the revised manuscript, where we further elaborate on the plans for clinical application, as well as additional limitations of Magical, including those you mentioned such as scalability, generalizability, ethical risks.\n\nThank you again for your constructive suggestions.\n\n> **References**\n\n[1] Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts, AAAI, 2023\n\n[2] Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation, AACL | IJCNLP，2022\n\n***\nWe sincerely thank you once again for your thoughtful guidance. Your feedback has been immensely valuable to us, and incorporating your suggestions has significantly improved the quality of our manuscript.\nIf you have any further questions or concerns, please feel free to contact us at any time. We are always available and look forward to further discussions with you.\n\nBest regards,\n\nAll Authors"}]

Author rebuttal:

Meta-review: The paper addresses the important problem of Medical Lay Language Generation (MLLG), which seeks to make complex biomedical texts understandable for non-expert audiences, with direct implications for health literacy and clinical communication. The authors identify limitations of existing parameter-efficient fine-tuning methods, particularly standard LoRA, which struggle to simultaneously preserve semantic fidelity and adapt to heterogeneous lay styles across datasets. To address this, they propose Magical, an asymmetric LoRA framework that employs a shared matrix for abstractive summarization, isolated matrices for diverse lay-style generation, and a semantic invariance constraint to ensure content fidelity. In addition, a recommendation-guided switching mechanism is introduced to better adapt to style variation. Experiments on three medical datasets across multiple LLM backbones demonstrate consistent performance improvements over standard LoRA and several of its variants, with the added benefit of parameter efficiency.

The strengths of the paper lie in its timely and socially impactful application domain, the solid empirical investigation demonstrating the inadequacies of existing approaches, and the comprehensive experimental validation across datasets, backbones, and baselines. The method is straightforward to implement yet effective, showing both performance gains and reduced parameter costs. Reviewers noted that the paper is clearly written and accessible, making it an engaging contribution for the broader community. The rebuttal phase further strengthened the work: the authors responded constructively by incorporating human evaluation and LLM-based assessments to validate perceived quality, adding Full-Parameter Fine-Tuning and large-scale LLMs (e.g., o3-mini) as baselines, and introducing semantic similarity metrics (BERTScore). They also expanded the scope of evaluation to a legal summarization dataset, demonstrating generalization beyond the medical domain. These additions address the major concerns raised in the initial reviews.

The weaknesses primarily concern the incremental nature of the architectural contribution relative to HydraLoRA and Mixture-of-LoRAs, and the fact that the recommendation-guided switch currently relies on manual selection rather than a fully automated recommender. While these are valid points, the authors convincingly argue that router-based approaches fail in the fuzzy task boundaries of MLLG, and that Magical’s decoupling of summarization and style generation provides a pragmatic and empirically validated solution. The manual switch is acknowledged as a conceptual placeholder, and sensitivity analysis shows that even under degraded recommendation performance, Magical continues to outperform baselines. Thus, while the novelty is more empirical than conceptual, the contribution is nonetheless valuable and timely.

During the rebuttal and discussion, reviewers’ concerns about evaluation realism, semantic fidelity, generalization, and baseline completeness were addressed through new experiments and expanded analyses. Reviewers who initially had reservations either maintained or raised their recommendations after these clarifications, with some explicitly confirming that their concerns were resolved. The overall consensus is that the empirical contributions are strong, the problem setting is important, and the paper’s improvements are meaningful for both research and real-world applications.

In summary, despite some incremental aspects, this paper makes a well-executed and timely contribution to a high-impact problem. Its thorough experimentation, careful analysis, and strong rebuttal efforts justify acceptance. While it may not merit a spotlight due to modest architectural novelty, it stands as a solid and valuable addition to the conference program, and I recommend acceptance.
Decision: Accept (poster)