CALM: Culturally Self-Aware Language Models
Authors: Lingzhi Shen
Xiaohao Cai
YUNFEI LONG
Imran Razzak
Guanming Chen
Shoaib Jameel
Venue: NeurIPS 2025 poster ()
Keywords: Cultural Awareness
Cultural Alignment
Cultural Reasoning
Large Language Models
Primary area: social_and_economic_aspects_of_machine_learning

Abstract: Cultural awareness in language models is the capacity to understand and adapt to diverse cultural contexts. However, most existing approaches treat culture as static background knowledge, overlooking its dynamic and evolving nature. This limitation reduces their reliability in downstream tasks that demand genuine cultural sensitivity. In this work, we introduce CALM, a novel framework designed to endow language models with cultural self-awareness. CALM disentangles task semantics from explicit cultural concepts and latent cultural signals, shaping them into structured cultural clusters through contrastive learning. These clusters are then aligned via cross-attention to establish fine-grained interactions among related cultural features and are adaptively integrated through a Mixture-of-Experts mechanism along culture-specific dimensions. The resulting unified representation is fused with the model's original knowledge to construct a culturally grounded internal identity state, which is further enhanced through self-prompted reflective learning, enabling continual adaptation and self-correction. Extensive experiments conducted on multiple cross-cultural benchmark datasets demonstrate that CALM consistently outperforms state-of-the-art methods.

CALM: Culturally Self-Aware Language Models
Lingzhi Shen
Xiaohao Cai
Yunfei Long Imran Razzak Guanming Chen C Shoaib Jameel
University of Southampton, Southampton, United Kingdom
Queen Mary University of London, London, United Kingdom
Mohamed bin Zayod University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
Abstract
Cultural awareness in language models is the capacity to understand and adapt diverse cultural contexts. However, most existing approaches treat culture as static background knowledge, overlooking its dynamic and evolving nature. This limitation reduces their reliability in downstream tasks that demand genuine cultural sensitivity. In this work, we introduce CALM, a novel framework designed endow language models with cultural self-awareness. CALM disentangles task semantics from explicit cultural concepts and latent cultural signals, shaping them into structured cultural clusters through contrastive learning. These clusters are then aligned via cross-attention to establish fine-grained interactions among related cultural features and are adaptively integrated through a Mfxture-of-Expertt mechanism along culture-specific dimensions. The resulting unified representation is fused with the model's original knowledge construct culturally grounded internal identity state, which is further enhanced through self-prompted reflective learning, enabling continual adaptation and self-correction. Extensive experiments conducted on multiple cross-cultural benchmark datasets demonstrate that CALM consistently outperforms state-of- of-the-art methods.
Introduction
As anthropologist Clifford Geertz stated in The Interpretation of Cultures D. foundational text cultural anthropology, culture is not merely the observable pattern of behaviour but "a system of inherited conceptions expressed in symbolic forms by means of which people communicate, perpetuate, and develop their knowledge about and attitudes toward life." In language understanding, cultural awareness enables individuals go beyond linguistic form (2] and comprehend how meaning shaped by social and cultural context. It involves internalizing shared norms and situational conventions (3) allowing interpretation consider not only literal meaning but also intent and appro- priateness. Without such awareness, language understanding becomes superficial and contextually detached, often leading pragmatic errors misunderstanding.
In language models, cultural awareness is key to social sensitivity and contextual appropriateness 4. enabling model behaviour to align with diverse communicative norms and expectations 6) This capability is essential for a wide range of cross-cultural applications. including dialogue systems 7. educational technologies [8], and content moderation [9]. Grounding language in cultural understanding allows models to move beyond surface-level fluency and generate responses that contextually coherent and socially responsible, thereby contributing to greater fairness and inclusivity in model outputs [0] Figure T Tillustrates how an AI system lacking cultural grounding misinterpret context and offer inappropriate suggestions.
Recent work has increasingly highlighted the importance of cultural awareness in large language models (LLMs). CultureBank [1] builds large-scale knowledge base by extracting structured
'The source code is available at https//githnb.ccm//ss/25///AAA
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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am going from Varanasi to Iapan. Can you suggest what local food should try there?
going from Varanasi Japan. Can you suggest what local food I should try there?
(User)
(User)
Japan known for many amazing dishes, should definitely try the famous Kobe beef!
Sure! You might enjoy tempura, noodles, or tofu dishes, delicious and vegetarian-friendly
(Chatbot)
(Chatbot)
Cows are holy for us I don't eat beef at all. What kind suggestion this?
Sounds good only! Thanks for giving veg options. Will surely try
(User)
(User)
Figure I: A case of culturally sensitive food recommendation. Left: Al assistant recommends beef to a user from Varanasi, resulting in discomfort. Right: Recommendations lead to a satisfying outcome.
cultural descriptors from online user-generated narratives, but its reliance on handcrafted heuristics treats culture as static labels and limits contextual adaptability. CulturePark [12 generates cross- cultural dialogue data through simulated multi-agent interactions, yet its synthetic nature lacks the grounding and diversity of real human communication. SeaLLMs [13] adopts language-as- culture perspective by combining continual multilingual pretraining, vocabulary expansion, and self- reff-rreferene alignment to better capture the sociopragmatic features of Southeast Asian languages. However, its focus on linguistic structures leaves higher-level cultural reasoning and transfer across non-linguistic dimensions underexplored.
In this paper, we introduce CALM, a framework for Culturally self-Aware Language Models. CALM first constructs an abstract cognitive space that extracts latent cultural signals and explicit cultural concepts beyond task semantics. These features are organized through within-type contrastive learning into structured semantic distributions that form clear cultural clusters. In the identity alignment pool, cross-attention [14] captures the interactions among related cultural features across these clusters, generating finely aligned cultural representations. The aligned representations are dynamically routed through a Mixture-of off-Eperts (MoE) [15] with an expert selection mechanism [16]. which enables culturally informed specialization along communicative dimensions. Residual fusion preserves essential cultural signals from the original features and combines them with high-level reasoning from experts to form unified cultural self-represennation.. Finally, CALM performs reflective reasoning through a self-corrective loop that integrates culturally conditioned prompt generation, culturally grounded reasoning, and identity calibration mechanism, allowing the model to self-adjust its cultural alignment when outputs deviate from its internal cultural representation.
Key Contributions: CALM first introduces novel disentanglement mechanism that separates task semantics from both explicit and latent cultural features within an abstract cognitive space. Building on this foundation, structured identity alignment pool unifies cultural signals through contrastive learning and cross-attention, producing coherent cultural representations. These representations dynamically routed through culture-informet MoE module that enables adaptive reasoning along communicative dimensions. Finally, CALM incorporates a self-corrective loop that ensures continual cultural adaptability. Together, these innovations advance the frontier of cultural reasoning, as demonstrated by superior performance across diverse benchmarks and supported by extensive ablation, qualitative, and quantitative analyses.
2 Related Work
Prior work on cultural alignment in language models falls into two main paradigms: data-centric training and prompt-based inference. Data-centric approaches embed culture into model parameters via pretraining or the-tunnng on curated resources such as multilingual corpora, annotated datasets, and knowledge bases [1711.. These methods typically encode culture as facts, values, symbols, or demographic attributes 23 often assuming culture is static and fully specifiable during training.
While these approaches can improve cultural factuality, they also risk reinforcing biases inherent in the training data [24] and often treat culture as a static construct, limiting adaptability to evolving cultural contexts 25 contrast, prompt-basee inference techniques manipulate inputs at runtime to simulate cultural perspective-taking using anthropological framing, demographic priming, national

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CALM: Culturally Self-Awre Longuage Models
Call beationtionn
6
Gorerator
d) ReRactive Rossoning ng Loop
Figure 2: Overview the CALM framework, comprising four stages that progressively embed cultural awareness into internal reasoning and enable self-consistency calibration.
Algorithm Pseudocode of CALM
Require: Inputs D, parameters θ, epochs N, batch size B, learning rate and criterion 7 Ensure: Task-appropriate, culturally aligned output
1: for i= to do
2: Sample batch X ~ D of size B
[S,E, + Extracte Exxtratee (X)) [, E] <- Contrast(E,
4:
[Cexplicits Clasml +- Partition (E, L); A Interact Cesplici) (Ha)a- SparseMap(A)): I Integrate (Ha),E, L, S)
5:
6:
P SelfPrompt(S, I); ô OutputHead P)
7:
I' +- IdentityEstimate()
8:
if (i, < then
9:
I [I, ôt OutputHead [I,
Update θ using loss L())
11: return
identity prompts 28) These methods are flexible and training-free 29] but treat culture as an external constraint, lacking integration into the model's reasoning process and often yielding brittle or inconsistent behavior across different scenarios 32) move beyond this dichotomy, CALM models culture an internal, dynamic reasoning state, enabling deeper and more adaptable cultural awareness.
3 Our Novel CALM Framework
As shown in Figure 2 and Algorithm T inspired by sociocultural theories of meaning construction 33 and metacognitive regulation 34 CALM models cultural awareness dynamic internal reasoning process. This process is implemented through a modular closed-loop architecture that consists of four components: perception, structural induction, identity construction, and reflective correction. Each component corresponds to a specific stage of cultural cognition.
3.1 Abstract Cognitive Space
Grounded in sociocultural theories of communication [35), we conceptualize culturally grounded language understanding as the interaction of three cognitive dimensions: task semantics, explicit cultural concepts, and latent cultural signals. This triadic decomposition reflects the layered structure of human communication, encompassing semantic content, symbolic representation, and pragmatic inference. To operationalize this, CALM employs a unified I.LM backbone that disentangles inputs into three parallel representations, each aligned with one of these dimensions.

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Given an imput sequence X = {I1, : Fnn. we represent it as:
HACS Hank 2 Hesplicit è Hhatent:
where Husk fins (X) Hesplicir = fesplicir (X) Hintent = fintem (X and ① denotes the concatena- tion of feature streams derived from the same LLM encoder.
The resulting HACS in Eqn ①I encodes multi-level abstraction that disentangles semantic intent, symbolic culture, and pragmatic norms, forming the cognitive foundation of CALM. While HAcs not used standalone variable in downstream equations, its subcomponents serve as the core representational inputs for all subsequent modules. Further theoretical explanations of the abstract cognitive space are elaborated in Appendix B.I
3.2 Contrastive Window
To enhance the structural regularity and discriminability of the abstract cognitive space, CALM incorporates contrastive window that refines explicit and latent cultural signals through type-specific contrastive learning. These two feature types originate from distinct linguistic levels: explicit concepts capture lexical and phrase-level elements (e.g., idioms, role titles) [40]. while latent signals encode sentence- and discourse-level stylistic traits (e.g., tone, formality) [41]. Contrastive learning encourages culturally coherent subspaces by maximizing intra-cultural similarity and minimizing inter-cultural overlap. Specifically, we apply type-specific projection heads (separate for explicit and latent features) on LLM-ennoded hidden states, obtaining normalized cultural embeddings via a SimCLR-stye setup [42] Positive pairs are sampled from semantically similar inputs within the same cultural group: negatives are drawn from culturally mismatched examples within the batch.
Let X, denote an input sequence sampled from culture c. We construct its positive pair X, by selecting another semantically similar sequence from the same culture For negative pairs, we draw X2 from sequences associated with a different culture c' * c within the same batch.
For each representation type & E {explicit, latent), we compute the corresponding projection Hi fx(),,, with fe being the projection function. The contrastive loss is then defined as:
exp(sim Hi, H.)/T)
(t)
contrast
log
(2)
exp(sim Hi. Ht)/T) + Ee' Ex Eor exp(sim(H), H/))
where sim(.) denotes cosine similarity and 7 is temperature parameter. We compute separate losses for each feature type and aggregate them as: Lwindow _ + _.llttent) This refinement encour- ages both Hesplicir and Hattet to organize into structured clusters that reflect intra-group cohesion and inter-group distinctiveness. After training, we obtain the corresponding refined representations say Hexphii and Hument-
Further theoretical explanations of the contrastive window can be found in Appendix B.2
3.3 Identity Alignment Pool
Cultural reasoning involves identifying symbolic cues and integrating them into coherent identity that reflects the communicative logic a cultural context. To this end, we introduce the identity
'Task semantics reflects the propositional content and domain-specific goals of an utterance. This aligns with Vygotsky's notion of functional meaning as shaped by task context and intentionality [36). We extract this component from the LLM's standard encoder output, yielding Has fuaa (X).
"Explicit cultural concepts correspond to overt symbolic forms, such as idioms, honorifics, and role nouns, which encode socially shared meanings and normative structures. Rather than focusing on factual knowledge, named entities, general commonsense, extract concepts because they operate higher level of abstraction (37] they organize lexical expressions into culturally salient semantic categories that reflect role expectations, politeness conventions, and institutionalized value systems. This draws on Halliday's systemic functional linguistics [38], emphasizing how culture is made explicit through lexical and phrase-level markers that realize social meaning. We extract this stream using span infilling objective: Hesplict = fexaaci(X)
$ 'Latent cultural signals capture implicit sentence/discourse level cues (i.e.. tone, formality, and indirectness). which modulate the interpretation of meaning across contexts. These are informed by Gumperz's is theory of contextualization cues [39], which describe how pragmatic markers convey culturally specific expectations. These signals modeled via contextual projection head: Hatem fmnen( (X).

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Expart-Cholat Machanism Salect Top Teouts
Soft Clus Lar As inn
Crass-netttion tmuls))
Cbx
Labint
Expliit Clus
by
Intr pemppms
Normatiitt We
Nap
Figure 3: Left punel: Cultural feature alignment process. Right panel: MoE schematic diagram.
alignment pool, which aligns implicit and explicit cultural features through cross cross-attention and routes them via an MoE structured around three communicative dimensions (see Figure 3
To align explicit and latent cultural features, we first summarize them into a set of semantic clusters. Specifically, we apply type-specific Softel-Softmax sampling (43) to contrastively refined represen-
tations Hexplici and Hastent Let H. represent either Hexplicit Haateu for simplicity. For each row in H. we compute a soft assignment over k clusters using
Softmax ((log π + g)/T),
where ~ Gumbel(0, 1), π denotes logits produced by cluster projection head, and r a temperature annealing parameter. We apply this operation separately to both cultural channels:
Cesplici Tlustergumbe(HHeppiie
Ciatent = Clustergunbed (Haseem)
Each row in C represents a soft cluster assignment produced from the corresponding row in H. This soft clustering technique allows differentiable clustering over heterogeneous cultural signals while preserving fine-grained alignment.
We then apply multi-head gross-attention from latent clusters to explicit clusters, and obtain
Halign DaassAtttCCuussen Cexplici))
(5)
which captures higher-order symbolic-pres praggmaai consistency across modalities. This mechanism enables culturally linked clusters, such as indirect tone (latent) and honorific markers (explicit), to interact and form integrated pragmatic representations.
To perform cultural specialization, we categorise cultural variation along three high-level communica- tive dimensions: Contextuality, Interpersonality, and Normativity. These dimensions are not arbitrary; they are theoretically grounded and selected based on (i) their expressivity in linguistic behavior, (ii) their foundational support in cultural communication theory, and (iii) their cross-contextual generalizability in modelling pragmatic variation. The detailed definitions and theoretical motivations of these newly introduced dimensions are provided in Appendices A. A.2, and
While not exhaustive, these three dimensions effectively model language- cultural variation. Contextuality shapes meaning density and distribution, Interpersonality governs relational stance, and Normativity modulates appropriateness based on internalized values. Together, they span symbolic and pragmatic layers of communication, making them well-suited for integration into an expert- driven reasoning mechanism. Each dimension d E {Contextuality, Interpersooality K..
is associated with of experts (E!()) ( JA=1 where Ka denotes the number of experts under dimension d. Rather than assigning inputs to experts, we adopt expert-choice mechanism, i.c., each expert actively selects the inputs specializes in. A detailed theoretical explanation of the expert choice and routing mechanism are provided in Appendix B.3] B.3
Given the aligned cultural representation Halign obtained in Eqn (5). each expert & ((d) first computes selection score throughout the input sequence, i.c.,
(d)
(ogPool(Haatign)) w/ (4)
b
(6)
5

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where (( and ( are expert-specific gating parameters, and AvgPool(-) computes the global average of Halign across the sequence dimension. Each expert then selects the top-k inputs with the highest selection scores and computes the corresponding soft gating weights, i.c., for l E Id.
Ia = TopK(s k). ag = exp(s)
(/))
Σ JETJ
exp(s) (1))
(d) denotes the where Ia is the index set of the top-k experts selected under dimension d. and dg
normalized gating weight for expert e(d) Our inspiration is that not all features can be matched to corresponding cultural dimensions; by allowing each expert to self-select the inputs best specialises in, we promote soft and competitive specialization among experts.
The gated output for dimension d is computed as weighted aggregation of the selected expert outputs:
(d) Hxspert
ok
(d)
(Hallgg))
(8)
RE
Finally, the dimension-specifee expert outputs fused through MLP and combined with the original refined cultural features via a residual connection to form a unified cultural identity:
Hhelr MLP((H
Helpen HeSpem))) + (Hesplictt Histem))
Here, Helpens Helper and H (N refer to the expert outputs corresponding the three cultural dimensions of Contextuality, Interpersonality, and Normativity, respectively.
The resulting Haelf encodes a culturally grounded identity state that preserves symbolic structure, pragmatics, and value alignment, enabling CALM to reason in a manner that sensitive to cultural identity structured, multifaceted internal representation.
Further theoretical explanations of the identity alignment pool can be found in Appendix B.4
3.4 Reflective Reasoning Loop
Human cultural intelligence involves retrieving knowledge and reflectively revising behavior 44 To simulate this metacognitive process, CALM incorporates reflective reasoning loop, enabling culturally appropriate reasoning and revision of culturally inconsistent outputs. This loop consists of two phases: (i) cultural self-prompted reasoning and (ii) inverse identity calibration, triggered only upon detecting mismatches with the model's internalized cultural identity.
Given the task semantics Husk and the cultural reff-represnntaiion Hiatt in Eqn (. CALM first generates a self-prompt P using a lightweight Transformer decoder, i.c.,
p p= S (Hask @ Hselr)).
(10)
This prompt embeds culturally grounded rhetorical framing, stylistic tone, and value-sensitive expressions. For example. in high-context or hierarchical settings, the prompt may emphasize indirectness and deference ("In a hierarchical society, it is customary to..."), whereas in low-context or egalitarian cultures, may favor clarity and direct self-assertion. The prompt P prepended the input sequence X and passed to decoder frenson that performs culturally shaped reasoning, yielding
Y freason (P; X])-
(11)
This step completes the initial inference. no cultural inconsistency is detected, the output Yno accepted.
To detect cultural misalignment, we adopt a reverse inference strategy that estimates whether the generated output reflects the intended cultural identity. This strategy is likewise grounded in metacog- nitive models of self-monitoring [45), which emphasize the importance of shifting perspective to evaluate one's 's own outputs. Specifically, we prompt the base model with
Haevase fidentity (Yra))
(12)
where Fisemtiy is an inverse prompting head that generates a soft distribution or embedding over possible cultural profiles based the linguistic features of Y for classification tasks, it instead

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uses intermediate decoder representations prior to prediction. We then compare the reverse-inferred identity Hreverse with the original cultural self-representation Huelr using cosine similarity å = sim(Heet Haeverse)
If 8 < T, where T is calibration threshold, we consider the output culturally misaligned. This discrepancy triggers corrective cycle: CALM reactivates the identity alignment pool to revise H regenerates new prompt P", and re-enters the reasoning phase.
This self-reflective mechanism enables CALM to perform continual metacognitive correction in dynamic cultural environments, thereby maintaining cultural consistency.
4 Experiments
Tasks: Following prior survey works 46 47
we categorize cultural awareness evaluation into two domains: (i) Enowledge-oriented focusing on culturally grounded commonsense reasoning and value reasoning; and (ii) toxicity-sensitive targeting the detection of culturally harmful content such as hate speech and social bias.
Datasets: For commonsense reasoning, Table 1: The memory cost, inference cost, and parameter count of CALM across four datasets.
adopt CultureAtlas [48]. a fine-grained bench- mark spanning over 2,500 ethnolinguistic groups, 193 countries, and 10,000 cities, contain- ing cultural statements labeled as true or false across domains such as festivals, marriage, cloth- ing, food, education, and social behaviors. It further distinguishes between general facts and context-specific assertions (e.g.. age, gender, re- ligion), enabling nuanced assessment across dif- ferent resource levels. For value reasoning. we
Model Params (B)
Inference VRAM (CB)
32.92
Dataset
Inference FLOPs/sampl (T)
UniVaR
1.29
CultureAtlas CRHHaate
2.59
0.65
EMGSD
1.01
use the multilingual UniVaR dataset [49], comprising approximately million QA pairs generated by 15 LLMs in 25 languages, covering 87 human values derived from several foundational theories of cultural values [50. 51]. Paraphrased and translated prompts enhance cultural diversity, while answers are back-translated to English to support language-neutie embeddings. For hate speech detection, employ CREHate (52). cross-cultural English benchmark consisting of 1,580 social media posts annotated by raters from five English-speaking regions with distinct cultural backgrounds, namely Australia (AU), the United Kingdom (GB), the United States (US), South Africa (ZA), and Singapore (SG). The dataset integrates re-annotated samples from the SBIC corpus (53] and newly curated Reddit and YouTube posts collected using culture-specific hate-related keywords. For social bias detection, we use the EMGSD dataset (54), which contains 57,201 instances labeled for binary and multi-class classification across six demographic dimensions: gender, race, nationality, religion, profession, and LGBTQ+ EMGSD extends the MGSD dataset (55) with subsets from WinoQueer (56) and SecGULL [57]. using GPT-4 and Mistral for additional sentence generation while maintaining human-validated stereotypes annotations.
Implementation Details:
The implementation details are listed in Appendix [ Table [ and Figure summarize the training efficiency and computational cost of CALM across datasets.
200.0k
Training Time (min) Fpochs Sample Size (k)
103.6k
10"
73
46
40 Ok
We evaluate four selected tasks using established frameworks and corre- sponding metrics: accuracy for hate speech de- tection, Macro Fl score for social bias detec- tion, accuracy (including Acc @ I, Acc @ 5, and Acc@ 10) and F1 score for value reasoning, and precision, recall, and F1 score for commonsense reasoning.
Evaluation Metrics:
18.0
101.
9.0
7.0
6.0
UnNoR
CultursAtias
CREHate
EMGSD
Figure 4: Log-scale statistics of CALM's training time, epochs, and sample size across four datasets.
We follow prior frameworks by adopting the official baselines provided with each benchmark. The selection covers both pretrained encoder models and LLMss, including open- source, instruction- tunee, and proprietary systems. Most basclines are fine-tuned task-specific,
Comparative Models:
7

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representing the established state of the art. This consistent setup ensures fairness, continuity, and rigorous comparison for CALM.
4.1 Overall Performance
Table 2: Comparison (%) between CALM and baseline models the CultureAtlas dataset, evaluated by Precision (P), Recall (R), and Fl-score (F).
All Culture
High Resource
Mid Resource
Low Resource
Model
P
R
F
P
R
F
P
F
P
R
F
LLuMA-2-7B
58
84.2
42.1
56.1
86.8
45.6
59.8
83.3
42.9
56.6
87.0
20.7
33.5
LLaMA-2-138
63.6
77.1
69.7
56.1
80.9
66.3
64.1
75.5
69.3
53.3
20.5
29.6
Vicuna-7B 55
79.6
56.8
77.3
47.2
58.6
79.4
67.0
81.3
55.7
66.1
Vicuna-133
67.4
81.2
73.7
68.9
81.0
74.5
69.4
82.4
75.3
67.8
82.3
74.3
GPT-4 660
95.8
90.6
93.1
95.9
91.4
93.6
94.9
92.1
93.5
94.1
90.1
92.1
CALM (Ours)
93.6
87.7
89.1
95.0
90.9
92.4
92.5
90.3
91.2
93.2
86.3
88.6
Firstly, Table 2 reports that CALM achieves an Fl score of 89.1 % on cultural commonsense reason- ing, significantly outperforming all open-source baselines that have undergone human preference aligmment. More importantly, CALM demonstrates consistent improvements across high-, mid-, and low-resource cultural groups, while maintaining a strong balance between precision and recall. The definitions different resource levels are provided in Appendix E. This indicates that model not only adapts well to diverse cultural contexts but also generalizes robustly across cultural strata. Such robustness is particularly critical for low-resource settings, where linguistic coverage sparse and cultural norms vary more drastically. Despite not relying on additional supervision, CALM achieves performance comparable to the proprictary model GPT-4.
Table 3 assesses the ability of the model to encode and differentiate human values between languages and cultures; CALM achieves the highest scores across all five evaluation metrics, including Acc@I (23.04%), Acc@5 (50.68%), and Acc@ (65.41%) %), outperforming the best UniVaR variant by clear margin. We report both k-NN and linear probing results to cap- ture complementary aspects value rep- resentation: k-NN evaluates local clustering and proximity of value-aligned responses, while linear probing assesses whether value-
Table 3: Evaluation (%) of CALM and baselines on the VnR dataset. k-NN and Linear reflect the quality of cultural embeddings.
k-NN
Linear
Model
Ace
F1
Acc@1 1
Acc@
Acc 10
GloVe
61
2,27
2.26
5.45
17.19
27.72
BERT
62
1.78
1.82
10.57
28.87
42.20
RoBERTa
63
1.88
1.89
10.06
27.70
41.17
XLM-R
64
1.40
1.41
8.65
24.96
37.92
MPNet
65
1.40
1.49
15.74
25.80
LaBSE
66
4.03
3.94
11.76
32.16
47.48
VniVaR
20.37
16.84
18.67
45.75
61.70
CALM (Ours)
23.87
21.35
23.04
50.68
65.41
relevant dimensions are linearly separable in the embedding space. CALM achieves
superior performance on both metrics, indicating that its value encoding is not only semantically coherent but also structurally disentangled from non-value factors. Additional experiments and validations are provided in Appendices H.I and H.2
As presented in Table 4 CALM attains average accuracy of 83.66% in cross-cultural hate speech detection, exceeding GPT-4 by more than 5% and substantially outperforming all open-source baselines. Notably, CALM maintains balanced performance across all five countries, including Singapore and South Africa, which have the lowest antotaan agreement, demonstrating strong
Table 4: Accuracy (%) comparison across five countries and average (Avg) CREHate dataset.
Table 5: Comparison CALM and baseline models on the EMGSD dataset.
Model
GB
US
AU
ZA
SG
Avg
Model
Macro F1 (%)
Emissions (g CO,e)
Orca-2 67
69.99
69.09
69,80
68 80
68.61
69.26
GPT-40 70
64.8
Unknown
Flan 68
68.58
67.49
68.28
DistiBERT
80.6
156.48
OPT 69
66.25
69.29
64.68
66.94
64.11
66.25
DistiRoBERRaa
$3.9
Unknown
GPT-3.5
72.47
70.62
72.39
69.28
71,94
71.34
ALBERT
81.5
GPT-4
79.66
80.64
78.02
78.03
74.65
78.20
BERT
82.8
270.68
CALM (Ours)
85.03
85.29
83,19
83.42
81.38
83.66
CALM (Ours)
85.3
49.42

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generalizability across geographically distinct cultural interpretations within the same language. Additional experiments and validations are provided in Appendices G.1 and G.2
In the domain of social bias detection, 5 that CALM reaches a Macro FI score of 85.3%, surpassing fine-tuned BERT and ALBERT-V2 by 2.5% and 3.8%, respectively. This significant improvement indicates that CALM effectively mitigates group-specific bias while maintaining robust generalization across diverse social contexts. Notably, CALM delivers this performance while maintaining carbon emissions comparable to highly efficient models like DistilBERT, despite achieving significantly higher accuracy, which highlights the sustainability of its design. More impressively, CALM surpasses the proprietary GPT-4o model by over 20%. Additional experiments and validations are provided in Appendices F.1
and F3
4.2
A Ablation Study
Table 6: Comparison of CALM built on various backbone models of different sizes and architectures.
All Culture
High Resource
Mid Resource
Low Resource
Backbone
P
R
F
P
R
F
P
R
F
P
R
F
LLaMA-3.1-88 773
90.5
68.9
78.1
93.4
74.2
82.6
89.2
71.7
79.3
86.1
64.4
76.1
Gemma-3-12B 77
87.0
70.3
77.6
90.7
72.5
80.4
85.6
74.2
79.4
83.9
67.2
76.2
Gemma-3-27B
92.2
76.4
84.6
95.1
79.1
86.7
91.0
77.8
84.7
90.3
74.6
81.8
Qwen3-8B
89.7
67.8
76.8
92.5
73.1
81.5
88.3
69.8
77.9
86.9
62.1
72.7
Qwen3-14B
93.3
71.9
81.2
96.2
76.6
85.1
91.9
74.1
82.0
90.8
67.5
76.5
CALM (Qwen3-32B)
93.6
87.7
89.1
95.0
90.9
92.4
92.5
90.3
91.2
93.2
86.3
88.6
A potential concern arises from the scaling laws 75 which suggest that model performance often correlates with parameter size. To further examine this, we implemented CALM on range of backbone models with diverse architectures and parameter scales on the CultureAtlas dataset. As shown Table 6 the observed improvements originate from CALM's cultural reasoning architecture rather than model scale. This indicates that well-designed framework, rather than raw model size, the key to achieving superior performance. The results demonstrate that even when built upon models of comparable or smaller size, CALM consistently and significantly surpasses the official baselines each benchmark. This finding confirms that CALM exhibits strong generalization and transferability across model families including Qwen, LLaMA and Gemma, at 8B, 14B, 27B, and higher scales. Its robust performance stems from the proposed cultural alignment mechanisms, which provide stability and adaptability independent of the underlying foundation model. In essence, the backbone serves merely as an implementation carrier, while the cultural awareness design of CALM is the true driver of its effectiveness.
Table 7 presents further ablation studies on key components, showing that CALM's performance improvements are progressive and interpretable. To assess the contribution of cultural feature streams within the abstract cognitive space, we independently ablate the latent cultural signal and the explicit
Table 7: The ablation study highlights the contribution of each key component to overall performance on the CulaureAtaas dataset.
All Culture
High Resource
Mid Resource
Low Resource
Component
P
F
R
F
R
P
F
Latent Cultural Signal
91.7
85.2
87.8
93.3
88.4
90.7
90.6
87.9
89.2
90.7
82.8
86.1
Explicit Cultural Concept
91.2
84.1
87.1
92.5
87.2
89.7
89.9
86.4
88.1
91.2
83.9
87.4
Contrastive Window
91.0
83.7
86.9
92.3
87.0
89.5
89.5
85.9
87.6
90.3
82.2
85.6
Identity Alignment Pool
87.3
74.2
80.1
88.0
76.4
81.7
85.6
73.6
79.1
85.2
68.9
76.3
Replace Cross-Attm with Fusion
90.4
82.1
86.2
91.5
85.5
88.3
89.1
85.2
87.1
90.2
81.3
85.1
w/o Cross-Attn
89.1
78.5
84.5
90.3
81.7
85.8
87.2
80.2
83.5
87.7
74.1
80.2
w/o MoE
88.5
79.4
83.5
82.5
85.6
86.6
81.6
83.9
87.0
76.3
81.3
w/o Cultural Dimensions
88.0
78.6
82.9
89.2
81.8
85.1
86.1
80.7
83.3
86.4
75.2
80.6
w/o Expert-Choike
88.3
78.9
83.1
89.5
82.0
85,2
86.2
81.0
83.5
86.8
75.6
80.9
Reflective Reasoning Loop
89.6
81.9
85,3
91.0
84.5
87.5
88.0
83.7
85.7
88.4
79.1
83.2
w/o Prompt Generator Head
90.1
81.8
85.7
91.4
84.9
88.0
88.8
84.2
86.3
89.7
80.4
84.7
w/o Inverse Identity Calibration
90.5
83.3
86.7
91.6
85.2
88.3
89.4 4
85.8
87.5
90.3
81.4
85.6
CALM (Ours)
93.6
87.7
89.1
95.0
90.9
92.4
92.5
90.3
91.2
93.2
86.3
88.6

---

cultural concept branches, resulting in overall Fl score reductions of 1.3% and 2.0%, respectively. The removal of the latent cultural signal leads to a particularly pronounced degradation in low-ree low-resource settings, with a 2.5% drop in F1, indicating its role in capturing subtle socio-cultural variations that are not explicitly lexicalized. These results confirm that both cultural streams provide complementary signals: the latent stream enhances sensitivity to pragmatics in resource-scarce environments, while the explicit stream anchors interpretation in lexicalized cultural norms.
Removing the contrastive window leads to a 2.2% drop in overall FI, with consistent degradation across high-, mid-, and low-resource settings, indicating that cultural cues, particularly implicit ones, form non-um con-unffrmm clusters in the embedding space that benefit from contrastive shaping. Without this module, culturally adjacent classes become more confusable. The identity alignment pool yields the largest performance drop among all ablations: a 9.0% overall F1 decrease, and 12.3% in low-resource cultures where explicit cues are limited. These results validate our core hypothesis that effective cultural understanding requires not only extracting symbolic and pragmatic features, but also structurally integrating them to capture the communicative logic of diverse cultural contexts.
We further assess the role of cross-modal alignment by ablating the cross-attention mechanism. Replacement with shallow MLP that concatenates and projects the latent and explicit streams without token-level interaction yields a 2.9% drop in overall F1, suggesting limited complementary capture but insufficient fine- -ne-grained alignment. Fully removing the integration between the streams leads to a 4.6% F1 drop, with pronounced degradation in low-resource settings. These results underscore the importance of token-level alignment in resolving mismatches between stylistic and symbolic cultural cues, particularly when cultural meaning spans multiple linguistic layers.
We evaluate the cultural specialization mechanism within the identity alignment pool by ablating its three core components; expert structure, dimension-specific partitioning, and dynamic routing. Replacing the MoE with shared MLP results in a 5.6% Fl drop, highlighting the importance of struc- turally distinct expert pathways for modeling diverse cultural traits. Flattening the dimension-specific organization into single undifferentiated expert pool further degrades performance, confirming that organizing culture along axes such as Contextuality, Normativity, and Interpersonality improves cross-cultural generalization. Finally, substituting dynamic routing with uniform averaging yields a 6.0% Fl drop. indicating that selective expert activation is critical for cultural compatibility and minimizing representational interference.
Lastly, we assess the impact of reflective reasoning, designed to enable self-correction of cultural interpretations. Removing the entire reasoning loop results in 3.8% F1 drop, with pronounced effects in mid- and low-resource groups. To isolate component contributions, we ablate the prompt generator and inverse identity calibration heads. Excluding the prompt generator yields a 3.4% drop, underscoring the role of culturally grounded input formulations in initiating effective reasoning. Removing the calibration head leads to 2.4% decrease, indicating its importance in verifying cultural consistency pest-generation, particularly when implicit norms are underspecified.
5 Limitation and Discussion
Further discussion of CALM's design and limitation, including ethical considerations, is presented in Appendix D
6
Conclusion
This study proposed CALM, a culturally self-aware language model that integrates culture-hased self-representatiion into its reasoning process. CALM does not rely on external prompts or fixed cultural attributes; instead, it models culture as an internal and adaptive component of reasoning. Through comprehensive evaluations across multiple cultural reasoning tasks, CALM demonstrates strong generalization, cross-cultura robustness, and sustainable performance. Follow-up experiments further validate the model's ability to dynamically adapt to diverse linguistic and cultural contexts. We hope this work will inspire future research on culturally aligned reasoning and encourage the development of language models that engage more responsibly and empathetically with global communities. Future extensions may include modelling cultural dynamics over time, capturing evolving stances in conversational agents, or aligning cultural representations with other modalities such images, speech, or location metadata.
10

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A Terminology and Conceptual Definitions
A.1 Contextuality
Contextuality captures the degree to which communicative meaning is encoded implicitly or explicitly, This dimension is grounded in Hall's high-/low-context communication theory (76), foundational framework in intercultural pragmatics and sociolinguistics. Hall's theory distinguishes cultures by their reliance on shared background knowledge versus explicit verbal information during interaction. High-context cultures tend to prefer indirectness, inference, and omission (e.g., ellipsis or deixis), while low-context cultures rely on self-contained and overt encoding. In our model, this dimension guides expert specialization over discourse-level features such as information redundancy, implicature structures, and referential clarity.
A.2 Interpersonality
Interpersonality reflects how speakers negotiate social alignment, face needs, and interpersonal stance in communication. It builds on Brown and Levinson's politeness theory (77). one of the most widely cited frameworks in linguistic anthropology and pragmatics. The theory formalizes how cultures adopt different politeness strategies, including positive politeness, negative politeness, and off-record indirectness, to manage social distance and power asymmetry. It directly informs our model's treatment of sentence-level features such as directness, mitigation, hedging, and speech act formulation, all of which are critical for culturally appropriate relational framing.
A.3 Normativity
Normativity encodes how internalized cultural values influence what is considered appropriate or offensive in communication. It synthesizes insights from Hofstede's cultural dimensions theory (78) and Schwartz's 's theory of basic human values [19]. Both are foundational models in cross-cultural psychology and value research, extensively validated across more than 70 national cultures [80]. These theories explain how values such as hierarchy, tradition, and sacredness shape linguistic acceptability, taboo sensitivity, and topic framing. In our model, normativity regulates expert activation based on lexical sensitivity to religious or moral terms, modality preferences, and formality expectations, thereby incorporating value-laden constraints into reasoning.
B Theoretical Justifications
B.1 Abstract Cognitive Space
We examine the sufficiency and validity of the abstract cognitive space (ACS) modelling cultural complexity from both conceptual and empirical perspectives. Our goal is not exhaustively capture every dimension of culture, since this is a challenge that no existing model has yet achieved. Instead, we aim to construct structured and theoretically grounded abstraction that meaningfully organizes and represents the core aspects of cultural cognition.
Theoretical Motivation: Culture is inherently complex, involving explicit symbols, implicit norms, values, communicative strategies, and contextual practices. Our approach is inspired by leading studies in sociocultural and cognitive linguistics, which consistently identify three fundamental layers necessary for meaningful cultural modelling: semantic intent (propositional content), explicit cultural concepts (overt symbolic forms such as idioms, honorifics, or role markers), and latent cultural signals (pragmatic, implicit, and discourse- level cues such as tone, indirectness, and stance). This triadic decomposition is grounded in the theories of Vygotsky, Halliday, and Gumperz, reflecting how humans process and infer cultural information at multiple cognitive levels.
Representation of Cultural Complexity: Operationally, the ACS in CALM is not single vector but a structured, multi-channel representation. Husk encodes domain and task semanties, capturing goal-directed meaning. Hexpliet extracts high-level explicit cultural concepts, focusing on socially salient idioms, honorifics, and role nouns. Hustm captures implicit, sentence- and discourse-level stylistic features, analyzing cues such as formality, tone, and indirectness. This approach is not simple feature engineering but a learning- disentanglement developed on large-scale data. further refined through joint and contrastive learning to ensure that each stream captures unique and
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coherent aspects of cultural variation. At the theoretical level, explicit cultural concepts and latent cultural signals together capture both the breadth and the depth of cultural understanding, Explicit concepts refer to symbols, norms, institutions, and overt pragmatic rules, such as honorifics, role markers, and idiomatic expressions (including role titles like "Doctor" or "Madam,"" " institutional expressions such as Japanese honorifics, and group-identity terms), which reflect cultural dimensions like hierarchy, politeness, and collective identity. Latent signals, in contrast, include pragmatic and communicative features that are less easily defined but vital for cultural cognition. These encompass tone, indirectness, formality, ambiguity, emotional nuance, and politeness strategies, serving as contextualization cues and revealing differences in power distance. For instance, indirect speech is prevalent in high-context cultures, while humor, wordplay, and subtle implications reveal underlying cognitive and communicative styles. Therefore, explicit concepts and latent signals correspond to two complementary cultural dimensions: one encompassing symbols, norms, and institutionalized behavior, and the other involving pragmatics, style, cognition, and interaction. As emphasized in sociolinguistic and cross-cultural pragmatics research, cultural differences manifest not only in explicit symbols but also in deeper communicative styles and cognitive patterns. Both aspects essential for a comprehensive model of culture.
Our study focuses on two main categories of tasks. The first category includes weoowedde-orienteee tasks, which evaluate commonsense and value reasoning within cultural contexts. The second category includes eexicity-sensittii tasks, which assess the detection of culturally harmful content such as hate speech and social bias. The complementarity between explicit cultural concepts and latent cultural signals forms the foundation of our model design for both types of tasks. In knowledge-orientee identification, explicit cultural concepts enable the model to directly capture codified knowledge, including ceremonial language, social roles, and terminology related to festivals or institutions, all of which serve as explicit evidence for commonsense judgments and value assignments. For example, determining whether a festival is unique to a particular culture or whether a title reflects social hierarchy depends on explicit semantic markers. Latent cultural signals, on the other hand, help the model capture stylistic and interactional features that are less formalized but essential for contextual reasoning. Examples include euphemisms, hedging, communicative styles reflecting power distance, and subtle cues indicating group identity. These implicit signals often determine whether a statement perceived as commonsense or as a shared value within a culture. In toxicity-sensitive tasks such as hate speech and bias detection, explicit cultural concepts include sensitive symbols and stereotypical keywords, such as targeted slurs and discriminatory terms. The model must detect and interpret these high-risk words and their contexts accurately. At the same time, many covert forms of toxicity and social bias are not expressed directly; instead, they are embedded in tone, insinuation, sarcasm, passive aggression, or humor, and are especially common in low-resource or high-context cultures. If a model relies only on explicit features, will likely miss these hidden forms of toxicity and fail to offer adequate protection for marginalized groups. By combining explicit and implicit channels, our multi-channe channel modeling approach captures both direct knowledge and symbolic cues, as well as the nuanced cultural risks that arise from style and context. This enables the model to achieve robust performance across both categories of tasks, ensuring comprehensive and culturally sensitive understanding.
Cultural Features:
tasks such as cultural commonsense reasoning and value
Integration in CALM: ACS serves as the foundation of cultural reasoning rather than its endpoint. Handling cultural complexity is not the sole responsibility of ACS but the result of the entire CALM framework operating in coordination. Within CALM, ACS provides a theory-driven foundation that separates explicit and implicit cultural information from task semantics, supports modular reasoning, and maintains consistency with established linguistic and Al models. Through contrastive learning and type-specific objectives, the two cultural streams are explicitly distinguished and organized, which enhances both diversity and discriminative power. In the subsequent stages of the framework, ACS serves as the input to the Identity Alignment Pool, where explicit and implicit channels are aligned via cross-attention and further processed by a dimension-specifi mixture-of- experts mechanism that encompasses contextuality. interpersonality, and normativity. This integration allows the model to specialize and generalize across communicative dimensions while preserving a coherent representational base. ACS functions not as a statie lookup table but as dynamic cognitive state recalibrated through a reflective reasoning loop. This process enables the model to identify and correct cultural misalignments as they appear in context. Through these integrated mechanisms, CALM is constructed as a holistic system capable of addressing cultural complexity rather than as a set of isolated or independent modules.
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B.2 Contrastive Window
We further elaborate on the theoretical and practical motivations for introducing the contrastive window in CALM. The following discussion integrates complementary perspectives from theoretical grounding, neural representation, and generalization benefits.
Structured and separable cultural representations. Cultural features are not isolated points but tend form dense and semantically coherent groupings in the embedding space. Linguistic phenomena such as self-deprecating expressions or kinship-related terms often exhibit strong local coherence. This distributional structure is consistent with categorization theory in psychology [8T], which posits that human cognition naturally organizes complex concepts into structured groups. Contrastive learning provides a principled mechanism for discovering and rein- foreing such group structures. By pulling together representations belonging to the same cultural category and pushing apart those of different categories, it establishes clear cultural boundaries in high-dimensional space. Without this mechanism, models may struggle to capture intra- intra-group homogeneity and inter-group heterogeneity within cultural representations.
Theoretical Foundation:
Correcting cultural dilution in pretraining. LLMs trained under self- supervised objectives typically optimize for average predictive likelihood across tokens spans. This objective emphasizes statistical regularities and often dilutes fine-grained cultural distinctions, as models tend prioritize universal linguistic patterns over culturally specific cues. The contrastive window explicitly clusters both explicit and latent cultural features, enforcing semantic separation within the representation space. This structural encoding allows downstream modules to effectively retrieve by culture and mitigates the loss of cultural variation that may occur during generalization.
Neural Representation:
Generalization Benefit: A structural prerequisite for downstream modules. Modules such as Gumbel-Softmax clustering, cross-attention, and MoF-based expert routing depend on structured and disentangled input representations operate effectively. Without the contrastive window, cultural features may become entangled and indistinguishable, which weakens the ability of downstream components make selective decisions. The contrastive window pre-aggregates each cultural cluster, providing spatial organization that enables experts to identify and specialize in relevant cultural dimensions. This process directly enhances routing selectivity and specialization, both of which are essential for accurate cultural reasoning.
B.3 Expert Choice and Routing Mechanism
The expert choice and routing mechanism in CALM is designed ensure interpretable and theory- driven specialization across cultural dimensions. Its formulation can be understood from four complementary perspectives: theoretical priors, generation of the routable cultural representation, expert routing and specialization, and the relationship between abstract features and dimensional specialization.
Theoretical Priors: Building upon well-established theories in cross-cultural communication, we decompose cultural differences into three complementary communicative dimensions: contextuality, interpersonality, and normativity. Contextuality is grounded in Hall's theory of high- versus low- context communication and relates to information density and omission. Interpersonality draws on Brown and Levinson's 's politeness theory and captures sentence-level strategies for expressing social stance. Normativity inspired by Hofstede's and Schwartz's 's value theories and corresponds to lexical and syntactic preferences associated with social appropriateness and value alignment. These three dimensions jointly define the theoretical space which linguistic phenomena can interpreted as learning targets, providing explicit guidance for the downstream expert networks.
Routable Cultural Representation: The MoE routing mechanism operates on a unified cultural representation Halign. which is produced through sequential process. The LLM encoding is first decomposed into task semantics (Htask), explicit cultural concepts (Hexplictt), and latent cultural signals (Haateut). The explicit stream is optimized through masked reconstruction to learn idioms, honorifics, and other symbolic concepts that typically appear at the word or phrase level. The latent stream leverages style and value cues such as tone, formality, and indirectness at the sentence or discourse level. To promote cultural separability, contrastive losses are applied to both streams, encouraging representations from the same cluster to move closer while pushing apart those from different clusters. Subsequently, Gumbee-Soottaa clustering used for each stream, and multi-head cross-attention aligns implicit (latent) and explicit clusters. This mechanism enables, for example,
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implicit "polite tone" clusters to align with explicit "honorific" clusters. The resulting representation Halign encodes discourse structure, politeness strategies, and value cues, serving as the input to the expert routing network.
Expert Routing and Specialization: For each cultural dimension (contextuality, interpersonality, and normativity), a dedicated expert pool is constructed. Each expert is implemented as lightweight Transformer layer (hidden size 512, FFN size 2048) initialized according to the Qwen scheme, with weights sampled from (0, 0.02) and biases set to zero for stability. The routing follows an "expert choice" principle: within each dimension, every expert seans the entire batch, computes an affinity score for each input, and selects the top-K inputs it is most suited to process. Only these selected inputs are routed through the corresponding expert. The gating scores are softmax normalized noftmax-nrmaaizzed across the chosen imputs, ensuring that activation remains sparse and competitive. To avoid load imbalance, a data- hata-balancing regularization term ensures that all experts are engaged across the batch. The specialized experts thus learn to represent distinct cultural subspaces for their selected inputs. Outputs from all three dimensions are concatenated, passed through an ML.P, and then combined residually with the aligned representation to form the unified cultural identity representation Haelf-
Abstract features retain meaningful structure because the model's gradient signals and contrastive constraints continue to enforce separability within high-level repre- sentations. In particular, task-driven gradients from downstream objectives, such as value recognition, politeness classification, and taboo detection, provide distinct learning signals for each expert pool, guiding the model to focus on patterns specific to each cultural dimension. Contrastive learning and cross-attention constraints further preserve clear cultural cluster boundaries and ensure coherent alignment between implicit and explicit features within Halign- Sparse activation and wad-balancing regularization also support dimensional specialization by ensuring that dispatch loss remains low and that each expert pool maintains even usage, preventing the model from collapsing into a single dominant expert.
Dimensional Differentiation:
B.4 Identity Alignment Pool
The goal of the Identity Alignment Pool is to provide the language model with a unified and culturally grounded identity representation that reflects the hierarchical nature of human cultural understanding. This design draws upon theories in sociocultural cognition and psycholinguistics, which suggest that deep cultural understanding extends beyond the recognition of isolated cues. It involves the structured integration of explicit symbolic concepts and implicit pragmatic signals into a coherent internal state.
To achieve this, we first apply Humbel-Softman clustering to the cultural features extracted through contrastive learning. Both explicit cultural concepts and latent cultural signals are clustered separately using this differentiable mechanism. The core idea is that cultural knowledge should not be modeled as flat, token-level facts but as structured and organized representations. real-world communication, cultural elements such as honorifies or taboos, well as pragmatic strategies such indirectness or power distance, often occur in identifiable yet internally coherent groups While contrastive learning encourages representations to self-organize into clusters, explicit clustering makes this structure observable and accessible to downstream modules such as cross-modal interaction or expert routing. The differentiability of the Gumbel-Softmal operation allows the entire clustering process to be trained end-to dod-t--nd with the rest of the model, enabling it to capture both static groupings and dynamic adaptations to each input and task.
Next, we apply multi-head cross-attention between the explicit and latent cultural clusters to generate aligned representations. This mechanism enables the model to connect explicit entities, such as family roles or religious groups. with latent cues such as deferential tone or indirect speech. Cultural understanding fundamentally relies on the integration of symbolic and pragmatic dimensions, many norms and conventions can only be interpreted correctly when both levels are considered together. Token- phrase-level features alone cannot capture such higher-order cultural logic. Cluster-level cross-attention allows the model to move beyond isolated fragments and learn abstract co-occurrence patterns, for instance, associations like "religious entity imperative tone." The use of multiple attention heads facilitates manyyttomanyy mappings between symbolic and pragmatic dimensions, reflecting the richness and variability of cultural phenomena in real-world communication.
To represent the multidimensional nature of cultural variation, the aligned features are organized along three theory- driven cultural dimensions: contextuality, interpersonality, and normativity. For each
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dimension, a dedicated expert pool is allocated. Within the routing mechanism, each expert actively scans the entire imput batch, computes an affinity score with each input, and then selects only the top-k inputs it is best suited to process. This design assumes that cultural variation follows interpretable axes, with distinct phenomena dominated by specific dimensions. For example, collectivist tendencies in East Asian contexts are often associated with high contextuality, while cross-cultual differences in power distance correspond to the broader dimension of normativity. Assigning each dimension to a separate expert pool reduces interference and encourages specialization. This design is inspired by the psychological principle of the division of cognitive labor [82], which posits that specialized units in both cognitive and social systems lead to more efficient and robust reasoning. Routing by expert choice within each cultural dimension outperforms conventional token-level routing by allowing experts to focus on culturally relevant patterns, reducing competition, and promoting the emergence of meaningful expert clusters. Moreover, enabling each expert to select only its top-k inputs rather than processing all inputs reinforces specialization and minimizes redundancy.
Finally, we employ an MLP to integrate the outputs from all cultural dimensions, with residual connections applied to preserve salient cultural signals from the original representations. This structure allows the model to incorporate high-level reasoning from dimension-specifie experts while retaining foundational cultural features. The residual mechanism, inspired by architectures such as ResNet and Transformer, prevents the loss of valuable information during deep fusion. Compared with simple averaging or weighted summation, concatenation followed by an MLP supports nonlinear integration of heterogeneous expert outputs, enabling more complex cultural interactions. The resulting identity representation captures both the breadth and depth of cultural knowledge, providing a strong foundation for reflective and culturally sensitive reasoning throughout the CALM framework.
C Implementation Details
We use Qwen3-32B 83
the backbone. Unless otherwise specified, all MLP-based projection heads are implemented as 2-layer networks with hidden size 512, ReLU activation, and a dropout rate of 0.1. The explicit concept stream is trained with a span-infilling objective, where idioms, honorifics, and culturally salient role expressions are randomly masked by sentinel tokens and reconstructed autoregressively. The latent signal stream is trained on sentence-level cultural style and value classification objective, conditioned on stylistic cues such as tone, formality, indirectness, and inferred cultural stance. Weak cultural group labels derived from language, region, or community metadata are used to supervise this component.
The contrastive window applies contrastive learning separately to the explicit and latent channels using independently parameterized MLP projection heads. Positive pairs are constructed by sampling semantically similar sentences from the same cultural label (e.g.. country or language group), while negative pairs are drawn from culturally mismatched examples within the same batch. We use the NT-Xent loss with temperature - = 0.07 and batch size 64.
In the identity alignment pool. Gumbel-softmax clustering (K 5) is applied to both cultural streams, with temperature cosine-decayed from 1.0 to 0.2. The cluster projection head's hidden size is 256. Multi-head cross-attention (h- = 8) is computed from latent to explicit clusters. Each communicative dimension contains four experts, each implemented as a 2-layer transformer block with hidden size 512 and FFN size 2048. To avoid expert collapse and promote balanced usage, we apply sparse dispatch loss with top-k activation (k- 2) and a load balancing regularization term [84]
The prompt generator is implemented rotary positional encoding, and gated residual connections. This module is trained independently using teacher forcing to autoregressively generate culturally grounded natural language prompts from the joint semantic and identity representation. The inverse identity head is implemented as a 2-layer MLP classifier that produces a softmax distribution over cultural group labels. It is trained using cross-entropy loss on retained cultural supervision signals such as language or regional tags.
lightweight 6-layer causal decoder with hidden size 768,
The final prediction layer is a task-specific classifier, trained using cross-entropy loss depending on the downstream objective. For classification tasks such as hate speech detection and stereotype recognition, CALM directly predicts the task label from the semantically grounded representation.
We allow at most one reflective cycle. We use a temperature of 0.7 for reasoning generation to encourage culturally diverse outputs. For identity calibration, deterministi decoding with temperature
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0.0 is used to ensure consistency and reproducibility. All trainable modules are optimized using Adam W with a learning rate of 3 x 10-5, weight decay of 0.01, and linear warmup over the first 10% of training steps. Reported results are averaged over ten runs. All experiments were conducted on an NVIDIA H200 GPU cluster.
D Limitation and Discussion
Throughout this paper, we use terms such as "construct," "understand,"" and "reflect" to describe model-internal operations over cultural representations. These expressions are not meant to imply the model possesses human-like cognitive abilities, but rather to emphasize CALM's distinction from existing approaches. Unlike prior methods that treat culture label or a prompt-level modifier, CALM operationalizes cultural understanding internally, using structured reasoning to integrate, monitor, and adjust cultural representations during inference. This internalization reflects the model's design goal to handle culture as a dynamic reasoning dimension rather than fixed external attribute.
Use of Technical Terms:
external
Cultural Complexity: We recognize that culture is not static entity, but a dynamic and multifaceted system shaped by history, interaction, and context. We also acknowledge that no existing approach can fully capture the vivid, evolving, and layered nature of human cultural identity. While CALM abstracts certain prominent communicative dimensions of culture to enable stable operational modelling, its goal is not to fully replicate the fluidity of human cultural experience, but to support adaptive reasoning structured cultural representations. By embedding cultural modelling into the internal reasoning process of language models, CALM takes a principled step toward the development of more culturally aware Al systems.
major gap in current culturally aware tasks lies in the absence of universal evaluation framework. Existing benchmarks often overrepresent individual cultural traits or focus narrowly on specific groups, while a unified standard for evaluating diverse cultural dimensions across populations remains lacking. As previously discussed, the complexity of culture poses significant challenges to the development of a truly comprehensive multicultural evaluation paradigm. We acknowledge that our current work is also constrained by this limitation and look forward to future efforts that may address it.
Evaluation Framework:
While CALM introduces structured cultural modelling, it inherits common L.LM-based limitations. First, it relies on cultural signals extracted from large cor- pora, potentially encoding biases or reinforcing stereotypes. Although our identity alignment balances implicit and explicit signals, it cannot fully prevent amplifying dominant norms or marginalizing underrepresented groups. Second, predefined cultural dimensions, despite dynamic selection, may oversimplify cultural diversity, risking essentialist assumptions [85). such as generalizing "collectivist" or "hierarchical" behaviors. Finally, though CALM simulates reflective reasoning, it lacks genuine cultural understanding. lived experience, or ethical deliberation, and should not be interpreted as a cultural classifier, advisor, decision-make in sensitive contexts.
Limitations and Ethical Considerations:
E
Additional Experiments on the AultureAtlas Benchmark
E.1
Resource-level Definition
The CultureAtlas benchmark dataset classifies cultural groups into high-resource, medium-resource, or low-resource categories based on two main criteria: (1) the availability of linguistic and digital resources, and (2) the overall level of socioeconomic development associated with each group.
resource,
Specifically, righ-resource groups (e.g., the United States, China, France, Spain, Japan) are charac- terised by abundant training data, broad population coverage, and advanced digital infrastructure. Medium-resourd, groups (e.g., Türkiye, Egypt, Iran, Malaysia, Argentina) exhibit moderate levels of data availability and representativeness. Low-resource groups (e.g., Laos, Bhutan, the Democratic Republic of the Congo, Serbia) refer to cultural communities with very limited data coverage. These groups are often underrepresented or marginalised, either due to economic constraints or because they use minority or poorly documented languages.
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Additional Experiments on the EMGSD Benchmark
F.1
Cross-model Cultural Bias Evaluation
60
54.3
50.4
50.8
48.9
46.7
45.7
43.2
43.8
41.5
36.6
20
10
Claude-2
Claude Sonneet Sonnet
Claude 3 5- Sonnet
Gemini-1 .-PPo
Gemini-1 1... Pro
GPT -iurrb turbio
GPT-4 GPT-A-Tirtia Tirbo
GPT-4o
Llama 3-708
Uama--3 3.1 4-405
Mistral Mediumi
Mistral- Large-
Figure 5: Proportion of stereotypical responses generated by each model on 1,000 culturalyy neutral
prompts derived from the EMGSD benchmark.
To examine the presence of unintended cultural bias in LLMs, we evaluate twelve widely used L.LMs by quantifying the proportion of stereotypical responses they generate when prompted with 1,000 culturally neutral sentences derived from the EMGSD benchmark. Formally, for model M, the stereotype prevalence is defined as:
1 PM
1 1).
(13)
n
where n denotes the total number of generated responses and ds E {0, 1} represents the binary prediction of stereotype presence produced by our CALM framework.
As illustrated in Figure the results reveal substantial variation across model families. Claude-3.5. Sonnet exhibits the lowest proportion of stereotypical outputs (36.9%), followed by GPT-4o (38.6%) and Gemini-1.5 1. (43.2%), indicating stronger alignment with culturally neutral intent. In contrast, models from the LLaMA and Mistral families produce markedly higher proportions of stereotypical responses, with LLaMA-3-70B and LLaMA-3, 1400BB reaching 57.6% and 50.8%, respectively.
These findings demonstrate that even saate-of-the-are LLMs still reproduce stereotypical or culturally sensitive content under neutral prompting conditions. Such disparities highlight persistent cross- family differences in cultural safety and underscore the importance of enhancing cultural awareness within LLMs mitigate unintended bias and ensure culturally appropriate behaviour.
F.2
Demographic-level Analysis of Stereotypes
To further examine how stereotypical content is distributed across different social dimensions, categorize model responses by demographic group as defined in the EMGSD benchmark and compute the average stereotype proportion per category, as detected by CALM. Figure 6 presents the mean stereotype proportions across six major groups: gender, profession, nationality, race, religion, and LGBTQ+. We observe the highest prevalence of stereotypical content in responses related to profession (79.7%), followed by nationality (52.5%), religion (50.4%), and race (48.3%). In contrast, the lowest bias levels found in the gender (34.2%) and LGBTQ (14. 1)) categories.
These findings indicate that stereotype risks are not uniformly distributed but instead vary substan- tially across demographic backgrounds. Importantly, stereotypical patterns observed across all
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80
70
60
52.5
50.4
50
48.3
40
34.2
30
20
14.1
Gender
Profession
Nationality
Race
Religion
LGBTQ+
Figure 6: Mean proportion of stereotypical responses by social group, aggregated across all evaluated models. The proportions are computed using the CALM framework on 1,000 culturally neutral prompts derived from the EMGSD benchmark.
demographic groups, suggesting that cultural bias is both pervasive and multifaceted. Even small proportions stereotypical responses have disproportionate social impact when models are de- ployed in sensitive contexts. Such disparities underscore the necessity of fine fine-grain ane-grained, group-specific evaluation ensure the cultural robustness and ethical reliability of LLMs, and further highlight the importance of enhancing cultural awareness to mitigate these biases and foster socially responsible model behaviour.
F.3
Group-wise Validation of CALM
100
98.3
86.9
80.5
80
74.9
76.9
40
Gender
Profession
Nationality
Race
Religion
LGBTQ+
Figure 7: Macro Fl scores of CALM across six demographic groups on the EMGSD test set.
To further validate CALM's bias detection capability under demographic variation, conduct fine- grained evaluation of its classification performance across six social groups on the EMGSD test set. Figure reports the macro scores obtained for the "Gender". "Profession". "Nationality", "Race", "Religion", and "LGBTQ+" categories. The results show that CALM maintains consistently strong detection accuracy across all groups, with the highest scores observed for "LGBTQ+" (98.3%) and "Race" (86.9%) categories, where stereotypical expressions are typically more explicit and lexically identifiable, Relatively lower still robust performance is achieved for "Gender" (68.6%) and "Profession" (74.9)), which often involve subtler, context-dependent linguistic cues. These findings
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confirm the reliability of CALM's performance and its ability to generalise effectively across diverse demographic contexts, reinforeing its robustness and interpretability for fine-graines stereotype detection. Such consistency further substantiates the preceding cross-model and group-level analyses, demonstrating that CALM provides a stable and culturally aware foundation for evaluating bias in LLMs..
F,4
Method-level Analysis
of Attribution Consistency
Table 8: Similarity between SHAP and LIME attribution vectors for CALM across 1,000 EMGSD
test instances.
Metric
Mean (Std. Dev.)
p-value
Interpretation
Cosine Similarity
0.672 (0.251)
< 20.001
Moderate alignment
Pearson Correlation
0.639 (0.266)
< 0.000
Moderate alignment
Jensen-Shannon Divergence
0.224 (0.101)
< 0.001
Low divergence
To evaluate the internal consistency of CALM's token-level explanations, compare the attribution vectors produced by SHAP and LIME across 1,000 text instances sampled from the EMGSD test set. SHAP (SHapley Additive exPlanations) attributes each token's contribution by estimating its marginal effect on the model's output through all possible feature combinations, while LIME (Local Interpretable Model-agnostic Explanations) perturbs the input text and fits local surrogate model to approximate token-level importance. For each instance, we generate a pair of attribution vectors (di, 8.) using SHAP and LIME respectively, and compute three similarity metrics: cosine similarity, Pearson correlation, and Lensen-Shannol (JS) divergence. These metrics quantify the alignment of token importance distributions across explanation methods.
We compute the mean similarity M and sample standard deviation across the K = 1000 samples as follows:
M
M 3d)
(14)
K
i=1
K
8M
(M) M)²
(15)
To determine whether the observed mean similarity is statistically significant, perform one- sample Z-test against the null hypothesis of no similarity. The statistic and two-tailed p-value are computed as:
M T'
(16)
SM VK"
== x P(Z > 12]).
(17)
Here, T' denotes the theoretical threshold of no similarity: T' = 0 for cosine similarity and Pearson correlation, and T'M- for JS divergence. A low p-value indicates that the observed similarity is significantly different from the null hypothesis baseline.
As shown in Table 8 CALM demonstrates statistically significant consistency between SHAP and LIME across all metrics. The average cosine similarity (0.672) and Pearson correlation (0.639), together with a low Jensen-Shannon divergence (0.224), indicate that CALM produces coherent and method-agnostic token-level explanations. The observed standard deviations (~0.25) reflect natural variance across instances, suggesting that the explanation alignment is stable but not artificially uniform. These results support the conclusion that CALM maintains robust internal attribution consistency across perturbation-base based and model-based explanation paradigms.
F.5
Token-level Analysis of Attribution Consistency
We further examine the visual and semantic alignment of token-level attributions between SHAP and LIME, Figure presents joint visualization of the top 50 tokens ranked by their average attribution
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0.8
0.6
0.4
0.2
Top-50
Top-50 LIME
0.0
0.2
0.4
0.6
0.8
Figure 8: Combined visualization of the top 50 tokens ranked by average SHAP and LIME attribution values using CALM over the EMGSD test set.
scores across both methods over EMGSD test set. Each token's position corresponds to its mean SHAP score the z-axis and its mean LIME score the y-axis, providing intuitive view of attribution consistency between perturbation- based and model-based interpretability frameworks. Tokens located near the diagonal line exhibit strong cross-method agreement, whereas those deviating from indicate attribution divergence between SHAP and LIME. Consistent with the quantitative findings, the visualization reveals high degree of overlap in tokens that both methods identify as strongly influential.
Representative examples such as "terrorist", "criminals", and "sexist" consistently receive high positive attributions under both methods, reflecting culturally loaded or discriminatory semantics that bias model predictions toward stereotypical interpretations. Other examples such as "femme", "nerdy". and "fanatical" show moderate but aligned attributions, illustrating subtler social stereotypes embedded in gendered intellectual discourse. Overall, these results demonstrate that CALM captures semantically coherent and cross-method consistent attribution patterns, reinforcing the stability and interpretability of its cultural reasoning process.
G
Additional Experiments on the CREHate Benchmark
G.1
Cultural Robustness on Consensus Samples
Table Comparison CALM with baselines on posts with unanimous in-country agreement from the CREHate dataset.
Model
GB
US
AU
ZA
SG
Avg
GPT-4
94.29
95.25
93.54
92.82
87.11
92.60
GPT-3.5
85,22
82.60
85.41
83.68
85.09
84.40
Orca-2
82.56
81.89
82.35
82.76
80.02
81.92
Flan-T5
82.22
80.58
80.91
81.19
OPT
77.76
80.99
76.44
77.62
74.95
77.95
CALM (Ours)
94.57
94.05
We aim examine cultural robustness under a high-confidence setting that minimises annotation noise. In the CREHate benchmark, different countries may disagree whether post is hateful. To obtain reliable cultural signals, we focus on the subset in which all annotators within each country reached full agreement the label. Each column in Table 9 normative judgement, allowing us to evaluate whether model can align with culturally grounded majority expectations rather than relying ambiguous or disputed examples. For each country (GB,
therefore represents a country-specific
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US, AU, ZA. SG). we retain only the test samples that received unanimous in in-country agreement and use these as gold-standard references. All models are evaluated under identical conditions, and accuracy is reported as the main metric. This controlled design ensures that any remaining performance difference reflects the model's intrinsic cultural robustness rather than inconsistencies in human annotation.
Our CALM achieves the highest accuracy across all five countries, with an average 94.05%. The performance remains consistently strong across culturally diverse regions, including Singapore and South Africa, where human consensus is usually harder to reach. CALM not only exceeds the best- performing proprietary baseline in overall accuracy but also exhibits a much smaller variance across countries, indicating its balanced adaptation distinct cultural contexts. These findings demonstrate that CALM effectively captures culturally grounded cues and maintains stable, identity-sensitive predictions across diverse English-speaking contexts. Its consistent accuracy both culturally homogeneous and heterogeneous societies, such as Singapore and South Africa, indicates that the model has internalised broader cultural reasoning rather than overfitting to region-speciio linguistic patterns. Overall, CALM achieves superior accuracy, fairness, and cross-cultural stability, confirming its strong capacity for culturally informed language understanding.
G.2 Evaluation of Instruction Adherence
Table 10: Comparison of CALM with baseline models in terms of out-of-choice (OOC) rates on the CREHate binary prompt task.
Model
O0C Rate (%)
GPT-4
0.09
GPT-3.5
0.01
Orca-2-7B
0.00
Flan-T5-XXL
0.00
OPT
0.11
CALM (Ours)
0.00
Generative models occasionally fail to produce answers in the required format (e.g., "b", "hate", "non-hate"). a phenomenon referred out-of-choice (OOC). OOC responses indicate model's inability to strictly follow the prompt instruction, such as when it outputs a full-sentence explanation or an unrelated completion instead of predefined choice. Under the CREHate binary prompt setup, we evaluate the proportion of such outputs to measure instruction-following reliability, which can be formally defined as:
N Σ N
OOC
Mù & Vvanial
(18)
where Is denotes the model output for input 24, Vealid is the predefined set of permissible answer choices (e.g., hate or non-hate), and I[-] the indicator function. The metric thus quantifies the proportion of responses that violate the instruction format.
Table TO reports the OOC rates for all compared models. Instruction-tume generative models like GPT-4 and GPT-3.5 achieve relatively low OOC rates, yet they still occasionally violate the format constraint by producing elaborated or hedged responses. In contrast, our model CALM records an OOC rate of zero, yielding perfectly valid labels for all inputs. While CALM retains full generative capacity, its prompt alignment and deterministic decoding strategy naturally constrain generation to task-relevant tokens without any explicit vocabulary masking. This design ensures complete adherence to prompt specifications, independent of linguistic variation or cultural context, and demonstrates CALM's superior reliability and stability in instruction based prediction tasks.
H
Additional Experiments on the UniVaR Benchmark
H.1
Cross-cultural Value Generalization
Ensuring a balanced representation of diverse value systems is critical to prevent overfitting to particular cultural dimensions normative structures. Our training process does not rely direct
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Table 11: Value identification accuracy (%) across four benchmarks with k-NN and Linear probing.
WVS
PVQ-RR
GLOBE
ValuePrism
Model Name
k-NN
Linear
k-NN
Linear
k-NN
Linear
k-NN
Linear
1.31
4.25
3.11
5.82
2.49
3.72
2.18
8.00
BERT
1.15
8.57
2.99
11.34
1.88
7.45
1.11
14,92
RoBERTa
1.36
7.82
2.83
10.94
1.95
6.99
1.39
14.51
XLM-R
0.75
7.12
2.53
8.85
1.56
6,23
0.76
12.38
MPNet
0.83
4.36
1.75
4.83
1.49
2.86
1.51
8.47
LaBSE
2.44
9.97
5.99
11.55
3.61
9.31
4.08
16.20
UniVaR
21.10
19.14
17.53
16.34
21.34
18.66
21.51
20.55
CALM (Ours)
23.31
21.62
19.90
18.66
23.36
20.91
23.71
22.57
supervision from any single value taxonomy. Instead, it clicits value-relevant behaviour through multi-view contrastive learning culturally grounded dnrestion-answer pairs, followed by translation into a shared language space to minimise linguistic bias. The model learns a compact embedding that focuses value-salient rather than stylistic information. The four evaluation corpora, WVS [86), PVQ-RR 87), GLOBE [8). and ValuePrism (8). differ substantially in their conceptual scope and structural assumptions. WVS and PVQ-RR follow formalised survey taxonomies. GLOBE focuses on societal and organisational cultural practices. ValuePrism emphasises pluralistic human values, rights, and duties. As shown in Table IT] CALM achieves consistently strong performance across all four benchmarks, showing stable behaviour despite their distinct modelling paradigms. These results remain consistent across languages and cultural settings, indicating that the model's behaviour is largely unaffected by linguistic variation. The stability observed across heterogencous corpora suggests that CALM effectively distinguishes value-related semantics from other contextual or lexical factors that are unrelated to the underlying cultural meaning. In contrast with systems that depend on direct value labels or handcrafted taxonomies, CALM demonstrates structural abstraction and cultural transfer through semantically grounded representation learning.
H.2 Cross-domain Linguistic Robustness
Table 12: Cross-domain generalization accuracy (%) on text-only and paraphrase tasks.
text-only
paraphrase
Model Name
Acc@1
Acc@5
Acc@1
Acc@5
GloVe
12.34
63.44
13.75
65.59
BERT
17.22
66.84
26.97
72.63
RoBERTa
15.20
66.76
19.98
69.93
XLM-R
17.59
67.37
19.60
70.40
MPNet
15.33
65.85
26.73
72.13
LaBSE
14.66
63.08
25.95
72.44
UniVaR
8.33
58.73
16.73
63.16
CALM (Ours)
7.95
56.80
62.17
To further examine whether the learned representation relies on translation artefacts or stylistic regularities, evaluate cross-domain generalisation using two settings derived from multilingual translation corpora. The first setting, text-only, provides English sentences without any contextual prompts, while the second, paraphrase, wraps each sentence within a question-answer style template to match the structure used during training. As shown Table 12 traditional sentence- seatennceembbdding models display higher sensitivity to the lource-language signal and thus achieve stronger performance in both settings. By contrast, CALM yields lower scores on this diagnostic task, which confirms that its embedding space is less affected by translationese patterns surface linguistic traces. This behaviour is consistent with the broader multilingual evaluation, showing that CALM exhibits minimal dependence specific languages and preserves coherent representations across cultural groups. The consistent outcomes across both settings indicate that CALM maintains stable and conceptually grounded embeddings even when linguistic traces or stylistic variations are intentionally introduced. Together, these results confirm that CALM generalises value semantics rather than encoding superficial language-dependent correlations.
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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction accurately summarize CALM's contributions: disentangling cultural information, aligning them through structured MoE routing. internaliz- ing cultural awareness into the model reasoning process, and enabling cultural self-correction via reflective reasoning. These claims are directly supported by detailed methodology and empirical results.
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Answer: [Yes]
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Reviews:
[{"review_id": "HgezT8Kz6o", "reviewer": "Reviewer_MxFb", "summary": "This paper introduced Culturally Self-Aware Language Models (CALM) to integrate cultural knowledge into language models. They first disentangle task semantic from explicit cultural concepts and latent cultural signals. It then aligns these signals via contrastive learning, cross-attention and mixture of experts with culturally grounded expert routing mechanism. Extensive experiments are performed across four benchmarks, covering commonsense recognition, value identification, hate-speech detection, and social-bias detection, to show the effectiveness of the proposed method.", "questions": "See the above weaknesses section for questions.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The proposed CALM outperforms strong open-source LLM baselines, and surpasses GPT-4 on the CREHate dataset across five countries.\n- Extensive ablation study to show the impact of different components in Table 5.\n\nWeaknesses:\n- The comparison setup is not sufficient from Table1 to Table4. First the actual backbone detail is hidden in Appendix C line 545, which should be highlighted in the main text. As Qwen3-32B is used as the backbone, why not reporting results with a vanilla Qwen3-32B model in all the tables? This way we can see the impact of CALM on top of Qwen3-32B, instead of comparing Qwen3-32B+CALM with other models like (llama2, vicuna, flan-t5, opt, etc.). It could just be that Qwen3 is a more advanced model than some of the LLMs published long ago, which makes the results less convincing.\n- CALM introduced additional complexity to the model architecture with additional encoders and experts, which increases memory cost, inference cost and parameters. Please also report results about the performance cost with CALM, to make it a good reference for practitioners. \n- For multilingual evaluation tasks (UniVaR) in this paper, how does the proposed CALM perform across different languages? Additional per language results (in Appendix) could be useful.", "was_revised": true, "final_justification": "The authors addressed my concerns, by providing consistent results across backbones.", "rebuttal": "First of all, we sincerely appreciate your valuable feedback and thoughtful comments.\n\n**Q1:** backbone detail...should be highlighted in the main text.\n\n**A1:** We originally intended to include this in the main text, but due to space constraints, the complete implementation details (including the backbone specification) were moved to Appendix C. Line 225 of the main paper directs readers: \"The implementation details are listed in Appendix.\" That said, we would like to emphasize a broader point: the backbone model is merely a tool for implementation, not the centerpiece of the contribution [1,2]. Our design is modular and agnostic to any particular foundation model. It can be Qwen, LLaMA, Gemma, DeepSeek, or others. As shown in our supplemental experiments (see response to reviewer QL7m), our architecture generalizes across multiple backbones. Selecting a specific model often comes down to practical preference, not scientific necessity. The focus of the paper is on our design rationale, technical contributions, and empirical findings, which are where the core novelty resides. Nonetheless, we respect the reviewer’s concern and are happy to prominently highlight the backbone detail (i.e., Qwen3-32B) in the main text of the revised version.\n\n**Q2:** ...why not reporting results with a vanilla Qwen3-32B model in all the tables?\n\n**A2:** We chose not to include the vanilla Qwen3-32B baseline in all performance tables because we consider it part of our ablation study rather than an independent comparison baseline. Readers interested in understanding CALM's improvements over Qwen3-32B can refer to the ablation results in Table 5, which show the progressive gains contributed by each module. Furthermore, introducing vanilla Qwen3-32B into the main result tables could create confusion. Readers might misinterpret it as a competing method, when it is in fact only the unmodified backbone. This could obscure the primary contributions of our paper, particularly the advances in design and methodology, and could disrupt the intended logical structure and narrative clarity, ultimately affecting the reader’s comprehension and experience.\n\n**Q3:** ...instead of comparing Qwen3-32B+CALM with other models... It could just be that Qwen3 is a more advanced model.\n\n**A3:** We explained our rationale for selecting these comparison models in lines 237–238: “Our selection remains consistent with previous frameworks.” The reason for this choice is to ensure a fair comparison. Therefore, all comparison models listed in Tables 1 to 4, such as LLaMA2, Vicuna, Flan-T5, and OPT as noted by the reviewer, are not arbitrarily chosen nor the result of a strategic trick. They are the official baselines provided by the benchmark datasets, and we faithfully report their results as originally documented, without re-running or modification. In many cases, these baselines are not raw pre-trained models; they have been fine-tuned or otherwise adapted to the datasets. Including an unadapted Qwen3-32B as a comparison would not be fair or meaningful. In this context, CALM is the appropriate point of comparison. It is our responsibility to compare with these established baselines in order to demonstrate the improvements introduced by CALM. This approach aligns with the standard expectations of competitive benchmarking and academic writing.\n\nRegarding the reviewer’s concern that CALM might only perform better because Qwen3 is more advanced than older models, we acknowledge that newer LLMs often have stronger capabilities. However, evaluating how a new model generalizes to culturally grounded tasks is a meaningful contribution in its own right. Moreover, we would like to reiterate the point made in our response to Q1: the backbone is not what drives our results. It could be any LLM, and we do not advocate for Qwen3 in particular. Our ablation study shows that CALM’s performance gains are progressive and interpretable. Without the CALM modules, the base model lacks the ability to perform strong cultural reasoning, regardless of whether it is built on a newer or older foundation. As shown in Appendix F, the performance gains are statistically significant and are driven by the design of our method, not by merely using a newer model. This is not a trick.\n\n**Q4:** ...increases memory cost, inference cost and parameters... Please also report results about the performance cost with CALM\n\n**A4:** Certainly. We have actually included the training time, number of epochs, and sample sizes in Figure 3, and carbondioxide emissions in Table 4. As requested by the reviewer, we have conducted additional experiments to calculate memory cost, inference cost, and parameter count, as shown in the table below: \n\n| Model Params (B) | Inference VRAM (GB) |\n|:----------------:|:------------------:|\n| 32.92            | >67                |\n\n| Dataset      | Inference FLOPs/sample (T) |\n|--------------|:-------------------------:|\n| UniVaR       | 1.29                      |\n| CultureAtlas | 2.59                      |\n| CREHate      | 0.65                      |\n| EMGSD        | 1.01                      |\n\nWe are happy to include any additional metric the reviewer would like to see.\n\n**Q5:** For multilingual evaluation tasks (UniVaR)...how does the proposed CALM perform across different languages?\n\n**A5:** Appendix E of the supplementary material provides evidence that CALM outperforms the baselines across different cultural value frameworks and demonstrates its robustness to non-value factors such as translation artifacts and linguistic style. Put simply, language has little influence on CALM. To further verify this, we have conducted the following supplementary experiments and additional analyses:\n\n  1. UMAP Visualization: We apply UMAP to project high-dimensional value embeddings from 15 LLMs across 25 languages into a two-dimensional space. The resulting clusters exhibit strong cultural coherence. For instance, Chinese, Japanese, and Korean form a Confucian cluster; Arabic, Persian, Malay, and Indonesian group into an Islamic cluster; and English, French, German, and Spanish are grouped into a Western cluster. This structure closely mirrors the Inglehart–Welzel World Cultural Map [3], suggesting that the value representations of LLMs are deeply influenced by both language and the underlying cultural systems.\n  \n  2. Comparative Evaluation: We compared CALM's performance on value-oriented QA, non-value QA, and translation source detection. CALM performs significantly better on the former tasks, indicating that it captures value semantics rather than superficial linguistic styles. This further validates its ability to effectively disentangle value-relevant information from confounding non-value signals. Some of the results are shown in the table below:\n\n|Model Name|WVS (k-NN)|WVS (Linear)|PVQ-RR (k-NN)|PVQ-RR (Linear)|GLOBE (k-NN)|GLOBE (Linear)|ValuePrism (k-NN)|ValuePrism (Linear)|\n|---|---|---|---|---|---|---|---|---|\n|BERT|1.15%|8.57%|2.99%|11.34%|1.88%|7.45%|1.11%|14.92%|\n|RoBERTa|1.36%|7.82%|2.83%|10.94%|1.95%|6.99%|1.39%|14.51%|\n|XLM-R|0.75%|7.12%|2.53%|8.85%|1.56%|6.23%|0.76%|12.38%|\n|MPNet|0.83%|4.36%|1.75%|4.49%|1.49%|2.86%|1.51%|8.47%|\n|LaBSE|2.44%|9.97%|3.69%|11.55%|3.61%|9.31%|4.08%|16.20%|\n|UniVaR|21.10%|19.14%|17.53%|16.34%|21.34%|18.66%|21.51%|20.55%|\n|**CALM**|**23.27%**|**21.33%**|**19.84%**|**18.71%**|**23.69%**|**20.89%**|**24.28%**|**22.93%**|\n\n|Model Name|text-only Acc@1|text-only Acc@5|paraphrase Acc@1|paraphrase Acc@5|\n|---|---|---|---|---|\n|BERT|17.22%|66.84%|26.97%|72.63%|\n|RoBERTa|15.20%|66.76%|19.98%|69.93%|\n|XLM-R|17.59%|67.37%|21.79%|70.40%|\n|MPNet|15.33%|65.85%|26.73%|72.13%|\n|LaBSE|14.66%|68.05%|23.95%|72.44%|\n|UniVaR|8.33%|58.73%|17.12%|63.16%|\n|**CALM**|**7.95%**|**56.80%**|**15.98%**|**62.17%**|\n\n  3. Language-Level UMAP: We visualize the embeddings of CALM and baseline models separately for each of the 25 languages. Each subplot corresponds to a single language, revealing the structure of value representations both within the same language and across different models.\n\nAll of these findings are consistent across the WVS, PVQ-RR, GLOBE, and ValuePrism corpora. In other words, CALM exhibits stable performance across different languages, whereas the baseline models are more sensitive to cross-lingual variation. This is because CALM achieves stronger decorrelation and suppression of “non-value features” (such as translationese and source language) at the embedding level, which passively reduces its ability to encode source language information. These experiments highlight CALM’s ability to generalize value representations rather than language-specific features. Following the reviewer’s suggestion, we will include these visualizations, tables, and qualitative analyses in the appendix of the revised version.\n\n[1] Raffel, C., Shazeer, N., et al. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67.\n\n[2] Liu, Z., Mao, H., et al. (2022). A ConvNet for the 2020s. CVPR, 11976–11986.\n\n[3] Inglehart, R. (2006). Mapping global values. Comparative Sociology, 5(2–3), 115–136."}, {"review_id": "HIMwTtncjr", "reviewer": "Reviewer_ABkN", "summary": "The authors are creating a whole framework for addressing culture awareness in LLMs, they are depending on attentions and mixture of experts to enable the awareness of the LLMs and the framework can provide reasoning for the behavior.", "questions": "- Are you planning to opensource the code and provide simple approaches to apply?\n- Why these exact llms for comparison?\n- Would you illustrate aside from equations the idea behind IdentityAlignmentPool and would you justify the usage of contrastive window as I did not totally get why it was important to use?\n- How would you define high, medium and low.resource? this should have been defined in the work", "limitations": "Some limitations are mentioned by the authors.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n- Novel approach, the effort done to make the framework is apparent.\n- Good evaluation and ablation studies to show the work\n\nWeaknesses:\n- Following the equations and the methodology was a bit tiring for me. (also I would need the other reviewers to check the credibility of all the equations and its writing as some equations were very confusing)\n- The framework is a bit complicated to apply in reality, so I wonder how the authors would provide it in an easy way to apply.", "was_revised": true, "final_justification": "The authors clarified many comments.", "rebuttal": "First of all, we sincerely appreciate your valuable feedback and thoughtful comments.\n\n**Q1:** ...check the credibility of all the equations and its writing as some equations were very confusing.\n\n**A1:** Thank you very much. We fully understand that some of the mathematical formulations, especially in the core technical section, may be challenging to follow. This difficulty may arise from several factors: the cross-module dependencies of certain equations, the abstraction required to model cultural concepts, the intrinsic complexity of the mathematics involved, and the novelty of our overall design. To address these challenges, we have made substantial efforts to present the equations in a modular and context-aware fashion. For each module or new concept introduced, we explain the design rationale and provide clear definitions for all mathematical symbols. We also include cross-references, such as “in Eqn. (5)” at line 160, to help readers easily locate previous formulations. These writing decisions are carefully considered to improve readability and ensure conceptual continuity.\n\nRegarding the credibility of the equations, we would like to assure the reviewers that all mathematical content has been carefully reviewed by several experts in mathematics on our team. That said, we also recognize that mathematical exposition is inherently challenging, especially in a framework like CALM that introduces new components and technical novelties. We greatly appreciate your feedback and will continue refining the presentation of our equations in future revisions. This may include adding step-by-step derivations, more intuitive explanations of variables, and additional diagrams to support understanding.\n\nIf you have any specific points of confusion, we would be happy to clarify them in detail. Should we have the opportunity to attend NeurIPS in person, we would also welcome the chance to discuss these equations face-to-face at the conference.\n\n**Q2:** Are you planning to opensource the code and provide simple approaches to apply?\n\n**A2:** Yes, absolutely. As stated in our response to Checklist Question 5 (lines 742–743), \"Upon publication, the code will be officially released, along with scripts to reproduce all the key results described in the supplementary materials.\" We fully recognize the importance of open-sourcing for scientific transparency and community progress, and we view it as a core commitment.\n\nFollowing the paper acceptance, we will release a complete codebase along with comprehensive documentation, modular APIs, and end-to-end instructions for both training and inference. Preconfigured scripts will also be provided to simplify setup. To support a wide range of use cases, we will provide a user-friendly toolkit and several plug-and-play examples that allow practitioners to selectively enable or disable specific modules (such as Identity Alignment or Reflective Reasoning). This design aims to make it easy to adapt or deploy CALM in diverse environments without requiring extensive technical intervention.\n\nThe code will be released under the MIT License to maximize flexibility for modification and redistribution. Furthermore, we are committed to actively maintaining the repository. If users report issues, we will engage with them directly to ensure that the project remains accessible, functional, and impactful in practice.\n\nConsidering that some users may have limited computational resources, we have also explored lighter-weight variants of the framework using smaller backbones, while still achieving competitive results. This is already reflected in our response to Reviewer QL7m and the supplementary experiments. \n\n**Q3:** Why these exact llms for comparison?\n\n**A3:** We selected these particular LLMs for three main reasons. First, they are the official baselines provided by the benchmark datasets we used. These models are often specifically fine-tuned or configured for strong performance on the respective datasets and represent the previous state of the art. It is standard practice in both academic writing and competitive evaluation to compare against the strongest prior models. Demonstrating improvements over such baselines lends greater credibility to the effectiveness of our proposed design. Second, as mentioned in lines 237–238, “Our selection remains consistent with previous frameworks.” Using the same comparison models as prior work ensures fairness and continuity. We also faithfully report the performance numbers documented in previous publications, rather than re-running or modifying those baselines ourselves. This decision reflects our respect for prior work and allows the community to clearly isolate and evaluate the gains brought by our approach. Third, the selected LLMs, including GPT-4, GPT-4o, Orca-2, Flan-T5, BERT, Vicuna, and LLaMA, collectively represent the major categories of modern LLMs. These include open-source models, instruction-tuned models, and proprietary systems. This coverage ensures that our evaluation is representative and rigorous, capturing a wide range of architectures and training paradigms. We believe this selection provides a fair, transparent, and meaningful assessment of CALM’s performance across different scenarios.\n\n**Q4:** Would you illustrate aside from equations the idea behind Identity Alignment Pool\n\n**A4:** We are very happy to provide further clarification. In Section 3.3 (Identity Alignment Pool), we have already described the conceptual motivation behind each step, including in lines 131–132, 142–143, 145–147, and 166–168. We follow this practice throughout the paper to help readers understand the reasoning behind our design decisions.\n\nThe goal of the Identity Alignment Pool is to endow the language model with a unified, culturally grounded identity representation that reflects the hierarchical nature of human cultural understanding. This design is inspired by theories in sociocultural cognition and psycholinguistics (as cited in the paper), which argue that deep cultural understanding goes beyond identifying isolated cues. It involves the structured integration of explicit symbolic concepts and implicit pragmatic signals into a coherent internal state.\n\nDue to the space limit in the rebuttal format, we were unable to add the further detailed illustration for this point that we have prepared, but **will provide them to you immediately when the author-reviewer discussion period starts**.\n\n**Q5:** would you justify the usage of contrastive window?\n\n**A5:** Yes, we are very happy to clarify the motivation and justification behind the use of the contrastive window. We have already provided both theoretical and empirical support in the paper. For example, in line 110, we state the primary rationale as “to enhance the structural regularity and discriminability of the abstract cognitive space.” Lines 114–116 further elaborate that “contrastive learning encourages culturally coherent subspaces by maximizing intra-cultural similarity and minimizing inter-cultural overlap.” In the ablation study (Section 4.2, Figure 5), we show that removing the contrastive window leads to a significant drop in both cultural separability and classification accuracy. Lines 293–300 provide qualitative analyses of this effect, such as: “indicating that cultural cues... form non-uniform clusters in the embedding space... Without this module, culturally adjacent classes become more confusable,” and “effective cultural understanding requires... structurally integrating them to capture the communicative logic of diverse cultural contexts.” Due to space limitations, we were unable to elaborate further in the main text. \n\nHere, we have provided additional clarification on the underlying theoretical and practical motivations from the perspectives of theoretical foundation, neural representation, generalization benefit, and empirical support, which **will be provided to you immediately when the author-reviewer discussion period starts** (due to the space limitation in this rebuttal).\n\n**Q6:** How would you define high, medium and low resource? This should have been defined in the work\n\n**A6:** Thank you for your suggestion. We acknowledge that the definition of high-, medium-, and low-resource settings was not explicitly stated in our paper. Due to the page limit, our initial assumption was that these terms would be familiar to readers who have reviewed the original benchmark dataset, where the classification was introduced. In lines 260–261, we briefly refer to low-resource settings as “where linguistic coverage is sparse and cultural norms vary more drastically.” However, we agree that this may not be sufficiently clear or intuitive for readers without direct access to the original dataset documentation. Below, we provide the detailed classification, which is based on the definitions presented in the dataset's original publication: Cultural groups are categorized as \"high-resource,\" \"medium-resource,\" or \"low-resource\" according to two primary criteria: (1) the availability of linguistic and digital resources, and (2) the overall level of socioeconomic development associated with each group. Specifically, high-resource groups (e.g., the United States, China, France, Spain, Japan) are characterized by large amounts of training data, extensive population coverage, and advanced digital infrastructure. Medium-resource groups (e.g., Turkey, Egypt, Iran, Malaysia, Argentina) have moderate data availability and representation. Low-resource groups (e.g., Laos, Bhutan, the Democratic Republic of the Congo, Serbia) refer to cultural communities with very limited data coverage. These often correspond to underrepresented or marginalized populations, either due to economic constraints or the use of minority or under-documented languages. We will make sure to clearly include this definition in the revision of the paper to avoid ambiguity."}, {"review_id": "HFrc9cEhl5", "reviewer": "Reviewer_z9ZR", "summary": "The paper propose a novel framework to endow language models with cultural self-awareness. The experimental results verify the effectiveness of the method.", "questions": "1. How to train the model? What is the training data?\n2. As shown in Table 1, the performance of your model is worse than ChatGPT on all cultures. The results is not good enough.\n3. Regarding \"ChatGPT\", what is the version of \"ChatGPT\" in the paper?\n4. No comparison with other cultural models, such as CulturalBank, CultureLLM. You can also compare with some culturally specific model, such as SeaLLM.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength:\n1. The paper propose a novel framework to endow language models with cultural self-awareness.\n2. It introduced an interesting idea that teach model the deep representations of cultures rather than shallow representations.\n3. The experiments setting is solid.\n\nWeakness:\n1. How to train the model? What is the training data?\n2. As shown in Table 1, the performance of your model is worse than ChatGPT on all cultures. The results is not good enough.\n3. Regarding \"ChatGPT\", what is the version of \"ChatGPT\" in the paper?\n4. No comparison with other cultural models, such as CulturalBank, CultureLLM. You can also compare with some culturally specific model, such as SeaLLM.", "was_revised": false, "final_justification": "", "rebuttal": "First of all, we sincerely appreciate your valuable feedback and thoughtful comments.\n\n**Q1:** How to train the model? What is the training data?\n\n**A1:** Please refer to lines 208–225 for a detailed introduction of the four datasets used in our work; references [52], [53], [54], and [55] cite the original dataset papers. For training procedures, please see Appendix C (Implementation Details), which provides comprehensive information on module implementation, inference strategies, hyperparameter settings, experimental environment, and more. If you are interested in resource consumption, Figure 3 summarizes the training time, number of epochs, and sample statistics. Additionally, in our response to reviewer MxFb Q4, we conducted further experiments to report VRAM usage, inference costs, and model parameters. If you have any specific concerns about the data or training process, we would be happy to address them in detail. We also include the pseudocode (Appendix B) and are committed to releasing the complete training scripts to ensure full reproducibility.\n\n**Q2:** ...the performance of your model is worse than ChatGPT on all cultures. The results is not good enough.\n\n**A2:** Thank you for your question. We would like to politely highlight that this might be a misunderstanding. Please see our detailed clarification below. \n\nWe acknowledge that CALM is slightly outperformed by ChatGPT on the CultureAtlas dataset (Table 1). We did not avoid this fact, but reported it honestly and transparently (see lines 261–262): “Despite not relying on additional supervision, CALM approaches the performance of the proprietary model ChatGPT.” For all baseline results in Table 1, we did not re-run or reproduce the ChatGPT or other baseline results ourselves. Instead, we faithfully cited the scores reported by the original dataset paper, out of respect and appreciation for their contributions to the field. This means our comparison with ChatGPT is entirely based on published results, the authenticity and exact evaluation setup of which are determined by the original dataset creators. \n\nWhile model performance on CultureAtlas is an important metric, it is not the only consideration when evaluating cultural awareness. CALM demonstrates significant advantages over GPT-4 and GPT-4o on other important benchmarks: on the CREHate dataset, CALM outperforms GPT-4 by over 5\\% (Table 3); on the EMGSD dataset, CALM exceeds GPT-4o by more than 20\\% (Table 4). Furthermore, in the supplementary material (Appendix C, Table 1), CALM achieves the highest accuracy across all five countries on consensus posts in CREHate, clearly surpassing both GPT-4 and GPT-3.5. This demonstrates CALM’s superior ability to align with cross-cultural consensus judgments. In Appendix D (Table 2), CALM also achieves a good OOC rate, outperforming GPT-4 and GPT-3.5 in instruction adherence and output validity, consistently generating valid and well-formed answers for all inputs.\n\nWe believe that a fair evaluation should not focus solely on a single aspect where the model is slightly weaker, but rather consider the comprehensive strengths that CALM demonstrates across multiple challenging, culturally sensitive tasks. CALM is an open model trained without proprietary data or instruction tuning, offering reproducibility and transparency, and yet already matches or surpasses the performance of much larger, proprietary models such as GPT-4 and GPT-4o. Even top-performing models from Alibaba, Google, Meta, and Anthropic do not always outperform previous models in every dimension, and GPT-series models are widely recognized as the strongest competitors in the field. \n\nGiven the above clarification, we sincerely hope that the misunderstanding is cleared and you agree that CALM indeed has demonstrated such competitive performance on several important benchmarks, particularly considering its scale and open approach.\n\n**Q3:** ...what is the version of \"ChatGPT\" in the paper?\n\n**A3:** Thank you for raising this question. As noted above, the ChatGPT results reported in our tables are faithfully cited from the original dataset papers (CultureAtlas), without any modification on our part. The authors of the CultureAtlas dataset described their baseline as \"a closed-source foundation model from OpenAI, trained with alignment data,\" but did not specify the exact version or API date of ChatGPT used in their experiments. Upon careful examination, we have identified its reference that appears to be incorrect. We have made efforts to contact the dataset authors for clarification, but have not received a response thus far. We have thoroughly verified all reported baselines in the dataset, and for all other open-source or versioned models included in our comparisons, we have carefully documented their versions to ensure transparency and fairness. We believe that faithfully reporting the original baseline data is essential to maintaining the integrity of the comparison. For full transparency and reproducibility, all results for CALM presented in this paper are obtained from our own experiments, with detailed implementation settings disclosed. For closed-source models like ChatGPT, we rely on the most authoritative and publicly documented sources currently available. We will continue our efforts to contact the dataset authors to clarify the exact ChatGPT version used, and will update our manuscript accordingly should we receive further information.\n\n**Q4:** No comparison with...CulturalBank, CultureLLM...also compare with...SeaLLM\n\n**A4:** We appreciate the reviewer’s familiarity with advanced methods in cultural awareness research, such as CulturalBank, CultureLLM, and SeaLLM. We have actually compared our approach with all three models. We have carefully reviewed and discussed the contributions and limitations of these prior works in our related work section. For instance, in the Introduction (lines 41–44), we introduce CulturalBank in our discussion of prior work, and in lines 47–51, we describe CultureLLM and discuss their limitations, as well as those of other cultural models like CulturePark. In the following paragraph (lines 52–56), we summarize the general limitations of existing methods in order to motivate our own framework. This constitutes a typical horizontal comparison in academic writing [1].\n\nOur CALM framework is designed to address a broader and more challenging set of objectives. As stated in lines 87–88, “To move beyond this dichotomy, CALM models culture as an internal, dynamic reasoning state.” We have thoroughly reviewed the CultureBank and CultureLLM datasets. CultureBank focuses primarily on knowledge-base fine-tuning and does not include country or language distinctions. CultureLLM’s experiments are based on WVS-derived data and cover only nine cultures. In contrast, CALM is evaluated across four comprehensive and diverse benchmarks: commonsense reasoning (CultureAtlas), value classification (UniVaR), hate speech detection (CREHate), and bias detection (EMGSD). These datasets are not only larger in scale but also more culturally diverse. For example, the WVS subset used in CultureLLM is only a part of the UniVaR dataset, and the integration of low-resource cultures in CultureLLM is less extensive than in CultureAtlas. CALM is developed specifically to overcome the limitations of previous cultural modeling approaches, such as their static, externalized, and label-driven nature, and the lack of dynamic reasoning and generalization ability. Our goal is to advance the state of the art rather than focus on single-task knowledge retrieval or narrow fine-tuning, which would not align with the scientific ambitions of our framework. Due to these fundamental differences in datasets, tasks, evaluation frameworks, and methodology, direct benchmarking between CALM and CultureBank or CultureLLM is not technically feasible and would not be scientifically meaningful. Such a comparison would fail to reflect the main strengths or intended contributions of our framework. By selecting the most challenging benchmarks and covering a much broader spectrum of cultures, our aim is to demonstrate both the breadth and forward-looking nature of our method.\n\nAs for SeaLLM, our rationale is similar to that described above. it is a region-specific language model, as described in its original paper: “SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian (SEA) languages.” By contrast, CALM addresses a much wider range of languages, geographic regions, and cultures. Even so, our model has been indirectly compared with SeaLLM. In the UniVaR dataset study, SeaLLM was included as one of the multilingual LLMs used for value extraction and embedding analysis to test the robustness of the proposed UniVaR representation. Since our CALM framework outperforms UniVaR across all four value-oriented corpora and multilingual evaluations (see Table 2), CALM also surpasses the value extraction capabilities demonstrated by SeaLLM in this context. Moreover, as detailed in Appendix E of our supplementary materials (and as explained in our response to Reviewer MxFb Q5), we have provided extensive additional experimental evidence to validate the superior performance of CALM compared to UniVaR (and SeaLLM) across multiple aspects. These include language-level UMAP visualizations, the separation of value-related and non-value-related features, and robustness across different corpora. CALM demonstrates stronger generalization and value-oriented robustness in cross-lingual and cross-cultural domains.\n\n[1] John M. Swales and Christine B. Feak. Academic Writing for Graduate Students: Essential Tasks and Skills (3rd Edition). University of Michigan Press, Ann Arbor, MI, 2012."}, {"review_id": "4aQD6cXcCj", "reviewer": "Reviewer_QL7m", "summary": "This paper highlights the LLMs’ potential cultural conflict and sensitivity under different cultural scenarios. It poses an approach to enable LLMs’ deeper understanding of the inputs’ cultural context by embedding cultural features to prompt generation with calibrated identity, thereby enhancing the appropriateness of the output to conform to that cultural context. The performance of the proposed framework is then validated based on different tasks and ablation tests.", "questions": "Q1: I am confused with the concept of expert choice and routing mechanism. How can the experts specialize in these dimensions (contextuality, interpersonally, and normativity)? Could you explain more on how the expert is initialized for the three dimensions at their meaning (Line 153-155) and how the Ctx/Int/Norm gate works? How do the dimensions match with the highly abstract features?\n \nQ2: The baseline result in Table 1-4 is taken from the papers [52]-[56] respectively to show that CALM achieves almost the best performance across various types of datasets. As stated in the Appendix, the backbone model of CALM is Qwen-32B. Since CALM is implemented based on a 32B large model, do the favorable results stem from the underlying model itself? For example, in Table 1, the scale of baseline models are 7B and 13B, which are much smaller than CALM and are expected to have poorer results. It would be more compelling if the author could provide some experiment results based on different underlying models that have a relatively same scale as other baseline models.\n\nS1: The use of footnotes is nonstandard. The content length of footnotes in P3 and P5 is already comparable to or even exceeds that of the main text. It is recommended to incorporate key information into the main body and place non-essential explanations in appendices. \n\nS2: Irregular use of bold text in tables. In terms of font style in Tables 1-4, bold carries different meanings. If bold is used to indicate the best-performing entry, then should the \"Emissions\" column in Table 4 be bolded for ALBERT-V2?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. This paper addresses an essential issue in LLMs’ awareness of cultural context.\n2. The proposed methods are novel and sound. They skillfully incorporated cultural context into cue word generation.\n3. The ablation test is clear.\n\nWeakness:\n1. Important information is missed in the main body, such as the experiment setting in the Appendix and the very long footnotes. Also, as the paper suggests a complicated network, a more detailed figure or table of the network structure to address these components’ details will be helpful.\n2. The experiment of CALM under different backbone models is missing but seems important for the method's effectiveness. It will be better to conduct a comparison between the underlying LLM with and without CALM.\n3. The related work section can be improved by adding relevant articles on the key techniques adopted to make the novelty in terms of techniques clearer, such as contrastive learning, MoE, etc.", "was_revised": true, "final_justification": "I appreciate the authors' response and clarifications. While their responses were helpful, I find it difficult to fully agree with the statement in their rebuttal: 'The ACS provides a minimal, theory-driven foundation that separates explicit and implicit cultural information...' and their assertion that the entire framework functions effectively. My primary reservation stems from the data-dependent nature of the CALM framework. This inherent dependency presents significant challenges when handling the inherent complexity of cultural phenomena. Consequently, I retain some reservations about the overall coverage and robustness claimed for the framework.", "rebuttal": "First of all, we sincerely appreciate your valuable feedback and thoughtful comments.\n\n**Q1:** Important information is missed in the main body, such as the experiment setting...\n\n**A1:** Due to page limitations in the main text, we included the experimental settings in Appendix C. This is a standard academic writing practice, frequently adopted in many top-tier venues, including NeurIPS [1, 2]. We chose to place the experimental details in the appendix because we consider the main contributions, including the design, methodology, and theoretical innovations, to be of greater importance and deserving of priority in the main body. This decision is also consistent with NeurIPS’s policy of allowing authors to provide appendix. Moreover, as explicitly indicated in line 225 (“The implementation details are listed in Appendix.”), readers interested in experimental setup and reproducibility can easily locate the necessary information. Therefore, regarding the reviewer’s concern, we would like to clarify that our approach fully complies with academic conventions and NeurIPS guidelines. It is simply a matter of writing style and space optimization. Nonetheless, we value the reviewer’s suggestion and are willing to consider moving implementation details into the main text on additional page in a future revision.\n\n**Q2:** ...a complicated network, a more detailed figure or table...will be helpful\n\n**A2:** We fully agree that clear figures and tables are important for understanding our work. In fact, we have taken steps in this direction. For example, on the right side of Figure 2, we provide a detailed illustration of the expert selection mechanism, as the cultural specialization across communicative dimensions is a major innovation in our paper. To make the workflow clearer and reduce the reading burden, we have visualized this process. Additionally, our ablation table (Table 5) presents comprehensive quantitative results for this complicated network, complemented by thorough qualitative analysis. The architecture diagram in Figure 2 also presents the overall design and detailed workflow of our framework. Note that, some engineering modules, such as Gumbel-Softmax and SimCLR, are well-established components in the literature; providing separate detailed figures for these is not within the scope of our work, but we have cited the relevant original papers appropriately (e.g., lines 118 and 136). Although these components are part of our design, their technical details are already thoroughly documented in the original works, and interested readers can refer to those sources for further information. If you have any specific requests regarding figures or tables that would enhance clarity, we welcome your suggestions and will take on board.\n\n**Q3:** The related work section can be improved by adding...such as contrastive learning, MoE, etc.\n\n**A3:** Contrastive learning and MoE are indeed key techniques in our design. However, the central theme of our paper is “cultural awareness.” In the related work section, we organized the literature according to the two mainstream approaches currently used in this area, as our primary aim is to provide readers with a clear understanding of progress on cultural awareness itself. This is a relatively novel research topic, and as discussed in lines 77–88, most existing work focuses on dataset creation and prompt engineering. While engineering innovations such as the application of contrastive learning or MoE represent important technical contributions in our work, these methods have not previously been popular applied to cultural awareness tasks, and thus are not the main focus of prior research in this field. Including extensive discussions of these techniques in the related work section may introduce unrelated details from other tasks, which could confuse readers about the scope and context of our paper. Nevertheless, we appreciate the reviewer’s perspective and are willing to add brief introductions to key techniques such as contrastive learning and MoE in the appendix.\n\n**Q4:** I am confused with the concept of expert choice and routing mechanism...Could you explain more....\n\n**A4:** We are very happy to clarify these points in detail. First, we would like to clarify that we have systematically introduced the construction of the input in lines 96–147, including mathematical formulations, theoretical footnotes, and descriptive explanations. Lines 160–174 further formalize and explain the MoE workflow. All implementation details for every step of the methodology are provided in Appendix C.\n\nHere, we have provided additional explanation from the perspectives of theoretical priors, generation of the routable cultural representation, expert choice and routing mechanism including a simplified code snippet of the routing module, and why abstract features still enable dimensional specialization, which **will be provided to you immediately when the author-reviewer discussion period starts** (due to the space limitation in this rebuttal).\n\n**Q5:** ... do the favorable results stem from the underlying model itself? ... provide some experiment results based on different underlying models.\n\n**A5:** Thank you for asking this. We acknowledge that model performance often correlates with parameter scale, as indicated by scaling laws. However, numerous influential studies have shown that strong architectural design can enable smaller models to outperform those with larger parameter counts [3]. In addition, on some tasks, simply increasing the number of parameters does not guarantee better results, as evidenced in prior literature [4].\n\nIf one attributes the strong results in Table 1 only to the parameter size of Qwen3-32B, it is difficult to explain why CALM achieves even better results than much larger proprietary models such as GPT-4 (as shown in Table 3, with an improvement of over 5\\%), GPT-3.5 (an improvement of 9.65\\% in Supplementary Appendix C), and GPT-4o (more than 20\\% improvement in Table 4). These OpenAI models have substantially more parameters than Qwen3-32B, and the GPT-4 series in particular is widely considered among the strongest language models available. Even the official results reported by the Qwen3 team are only comparable to those of GPT-4. In our experiments, however, CALM built on Qwen3-32B is able to outperform GPT-4o by a wide margin. These empirical results support our claim that CALM’s improvements stem from its model design rather than parameter size. Furthermore, Table 4 in our paper includes models such as DistilBERT, ALBERT-V2, and BERT, as reported by the dataset authors. These models contain only a few hundred million parameters but, when fine-tuned on the target datasets, can outperform GPT-4o on certain tasks. This further demonstrates that thoughtful model design, rather than simply increasing the number of parameters, is essential for superior performance.\n\nTo further address your question and to provide a more comprehensive evaluation of CALM, we have conducted additional experiments using various backbone models with different sizes and architectures, including but not limited to the baselines suggested by the reviewer.\n\nDue to the space limit in the rebuttal format, we were unable to add the further details (such as the experimental results and analyses for these backbone models) that we have prepared, but **will provide them to you immediately when the author-reviewer discussion period starts**.\n\n**Q6:** The use of footnotes is nonstandard.\n\n**A6:** As we explained in our response to Q1, placing foundational theoretical explanations in footnotes is a standard academic writing practice. Please refer to examples in [5, 6] which adopt this approach. This choice is not related to page limits or any other external factors. Using footnotes to highlight the key theoretical underpinnings of our design helps draw the reader’s attention to these important elements without interrupting the main flow of the text, and our use of footnotes fully complies with academic writing standards and NeurIPS formatting guidelines. We do not believe this should be considered a weakness of our submission (kindly note that we are experienced academic writers and have received prestigious best paper awards). Nevertheless, we respect the reviewer’s perspective and are willing to incorporate this content into the main text or the appendix in our revised submission, should the reviewer prefer that format.\n\n**Q7:** Irregular use of bold text in tables.\n\n**A7:** We acknowledge that in Table 4, the “Emissions” boldface should have been applied to ALBERT-V2, rather than CALM. This was an oversight on our part, and we will correct it in the revised version. For all other tables, the use of bold text is fully consistent: boldface always indicates the best performance, which is the standard convention in academic writing for computer science and artificial intelligence papers unless otherwise specified. Thank you.\n\n[1] D. Zhou, C. Brix, et al. Scalable neural network verification with branch-and-bound inferred cutting planes. NeurIPS, vol. 37, pp. 29324–29353, 2024.\n\n[2] Z. Shi, A. X. Yang, et al. Instruction tuning with loss over instructions. NeurIPS, vol. 37, pp. 69176–69205, 2024.\n\n[3] H. Touvron, T. Lavril, et al. LLaMA: Open and efficient foundation language models. 2023.\n\n[4] W. Fedus, B. Zoph, and N. Shazeer. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. NeurIPS, vol. 34, pp. 8491–8505, 2021.\n\n[5] J. M. Swales and C. B. Feak. Academic Writing for Graduate Students: Essential Tasks and Skills. University of Michigan Press, 2004.\n\n[6] W. C. Booth, G. G. Colomb, and J. M. Williams. The Craft of Research. University of Chicago Press, 2016."}]

Author rebuttal:

Meta-review: This paper introduces a novel framework for incorporating cultural self-awareness into LLMs. The framework leverages contrastive learning, mixture-of-experts, and other mechanisms to integrate cultural knowledge, thereby enabling LLMs to handle diverse cultural contexts more effectively. The authors present robust experimental results, demonstrating that CALM outperforms various state-of-the-art models across several culturally sensitive tasks, such as commonsense reasoning, bias detection, and hate speech classification. The contributions are evaluated through extensive ablation studies, which highlight the significance of each component in the framework. While the novelty and technical soundness of CALM are widely acknowledged by reviewers, some concerns were raised. These included issues with clarity, especially regarding the complexity of the network, the model’s reliance on specific backbones, and comparisons with other cultural models. However, the authors addressed these concerns effectively in their rebuttal, clarifying the methodology, providing additional results, and improving the manuscript's presentation. Notably, despite some reservations about the framework's ability to fully capture the complexity of cultural phenomena, the majority of reviewers recognized CALM's potential as a significant advancement in the field.
Decision: Accept (poster)