Attributing Culture-Conditioned Generations to Pretraining Corpora
Under review as a conference paper at ICLR 2025

ATTRIBUTING CULTURE-CONDITIONED
GENERATIONS TO PRETRAINING CORPORA

Anonymous authors
Paper under double-blind review

ABSTRACT

In open-ended generative tasks such as narrative writing or dialog interaction,
large language models are known to manifest culture biases, showing inadequate
knowledge and producing templated generations on less prevalent cultures. Pre-
vious works suggest that such biased generations are due to the uneven repre-
sentation of each culture in pretraining corpora of the language models. In this
work, we study how pretraining data lead to biased culture-conditioned gener-
ations via the lens of LLM memorization and generalization, in order to pro-
vide more insights on improving the pretraining data and the pretraining proce-
dure of LLMs. We introduce the MEMOED framework (MEMOrization from
pretraining document) which determines whether a generation for a culture is due
to memorization or generalization. On culture-conditioned generations about food
and clothing entities for 110 cultures, we find that for a culture with high frequency
in pretraining data, the model can recall more memorized knowledge about the
culture; for cultures appearing least frequently, none of their generations contain
any entities memorized from pretraining. In addition, we discover that the model
prefers generating about entities with extraordinarily high frequency regardless
of the conditioned-culture, an indication of overmemorization, where the model
demonstrates biases towards frequent terms in pretraining data regardless of its
correctness. Our findings show that current LLM generations majorly consist of
memorization and un-founded overmemorization. We hope that the MEMOED
framework and our insights will inspire more works on attributing model perfor-
mance on pretraining data.
[Disclaimer: This analysis does not represent any
views or beliefs of the authors. Our findings reflect trends observed specifically
within OLMo-7B’s pretraining data and are limited to this dataset. We make no
claims about whether these results reflect real-world conditions.]

1

INTRODUCTION

In open-ended generative tasks such as narrative writing or dialog interaction, language models
are known to manifest bias towards social groups marginalized due to their gender, race, or cul-
ture (Gallegos et al., 2024; Manvi et al., 2024; Li et al., 2024b). Among these, cultural bias stands
out because there are significantly more cultures to account for as compared to other types of social
groups. Cultures are often unevenly represented in the pretraining corpora, with some mentioned
more frequently than others, irrespective of their real-world prevalence (Li et al., 2024a). Recent
works discover that language models show clear preference to entities (Naous et al., 2023) and
opinions (Ryan et al., 2024) of cultures with higher prevalence, and are more likely to show inade-
quate knowledge and produce templated answers for cultures with lower frequency in the pretraining
data (Li et al., 2024b). To properly mitigate such bias, it is important to understand how culture-
conditioned generations connect to pretraining data.

Recent studies have revealed limitations of LLMs in memorization and generalization from pretrain-
ing data. Zhang et al. (2024) find that pretraining data imbalance causes generations to overgener-
alize to high-prevalence knowledge which overshadows knowledge with lower frequency. Chang
et al. (2024) find that LLMs cannot generate long-tail knowledge in downstream tasks because the
knowledge appears with intervals longer than a threshold that enables memorization. Inspired by
these findings, in this work we uncover how culture bias in generations form, by attributing the ap-

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Under review as a conference paper at ICLR 2025

Figure 1: Three types of symbols in culture-conditioned generations

Figure 2: Higher contribution score means stronger evidence of culture/symbol association in pre-
training data, as defined in §3.3. Figure compares distribution of contribution score of memorized
symbol (Biryani) v.s. non-memorized symbol (Roast turkey). Y-axis shows all cultures for which
the symbol is generated. Red font show the z-score: ≥ 2.6 means memorization.

pearance of knowledge entities in culture-conditioned generations to LLM’s memorization or gen-
eralization from pretraining data.

We introduce a symbol attribution framework, MEMOED (MEMOrization from pretraining
document), which determines whether symbols in generations conditioned on a culture is a result
of the model memorizing the culture/symbol relationship from the pretraining data. For symbols
that are not a result of culture/symbol memorization, we analyze whether they are a result of gen-
eralization grounded on memorization. We perform all of our analysis on OLMo-7B (Groeneveld
et al., 2024) and its pretraining data Dolma (Soldaini et al., 2024), which is conveniently indexed
by Elazar et al. (2024) and Liu et al. (2024).

Following (Li et al., 2024b), we collect culture-conditioned generations from OLMo-7B about 110
cultures on food and clothing topics, and observe that that three types of symbols appear in culture-
conditioned generations (§3.1): 1) symbols that are associated with no cultures and appear in more
than half of the cultures’ generations, e.g. “t-shirt” (independent symbols), 2) symbols that only
appear in a few cultures but are highly associated with a culture, e.g. “kimono” for “Japan” (memo-
rized symbols), and 3) symbols that lack cultural specificity but is a broader concept of emblematic
symbols for some cultures, e.g. “robe” is a generalized way of referring to “kimono”, an emblematic
symbol for “Japan” (generalized symbols).

To determine whether a symbol is a memorized symbol of a culture, MEMOED searches through
the pretraining corpora for documents that are contributory to memorization of the culture/symbol
association, and classifies the symbol as memorization if the percentage of contributory documents is
significant (§3.3). MEMOED categorization shows a moderate-to-high correlation between culture
prevalence and number of memorized symbols. Lack of memorized symbols for less prevalent
cultures indicate scarcity of relevant pretraining supervisions, hindering the language model from
memorizing culture-conditioned knowledge.

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 INDEPENDENTSYMBOLSSymbols occurring in greater than 50% of nationalities’ generations.MEMORIZED SYMBOLSCulture-speciﬁc symbols that are memorized from the pre-training data.GENERALIZED SYMBOLSSymbols which are inferred from the culture-speciﬁc memorized symbols.312Describe the food of your neighbor. “My neighbor is Indian. He probably likes to eat”1233.860.011.47BiryaniRoast turkeyUnder review as a conference paper at ICLR 2025

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On the other hand, overmemorization (§4.3) is highly prevalent in culture-conditioned generations,
where model are biased towards generating high-frequency symbols that are easily memorized
but independent of any culture, regardless of correctness. More than 91% and 79% of culture-
generations for clothing and food respectively, contain independent symbols unrelated to the culture
in question, and this ratio is only higher for cultures with lower frequency from pretraining corpora.
In addition, models also generate memorized symbols of one culture for other cultures, indicating
that overmemorization not only happens on symbol level, but also on culture level.

Lastly, we evaluate the quality of generalization in culture-conditioned generations. We find that on
average, less than 5% of generations contain generalized symbols that can be traced to memorized
symbols (§4.4). We also find that on average 0.2% of generations containing generalized symbols
can be traced to independent symbols itself (§4.3).

High-quality culture-conditioned generations should adhere to instructions, exhibit high diversity
and quantity of memorization, and make generalization grounded on memorization. The over-
whelming proportion of overmemorization indicates that LLMs prioritizes memorization of high-
frequency independent symbols over generalization from lower-frequency memorized symbols.

Our work presents a generation attribution framework that allows researchers to clearly trace culture-
conditioned generations to knowledge memorization or generalization from pretraining data. Our
finding suggests that language models are unable to reliably and evenly recall knowledge about
global cultures in downstream generations, and resort to overmemorization of a small set of high-
frequency symbols. We hope that our work help provide insights on improving the pretraining data
and pretraining procedure of large language models, and that we inspire more works on attributing
model performance on pretraining data.

2 RELATED WORKS

Memorization and Generalization. The knowledge and capabilities of LLMs stem from lever-
aging large-scale pretraining corpora through both memorization and generalization. One line of
work focuses on prompting LLMs to emit memorized training data (Wang et al., 2024; Carlini et al.,
2023; Nasr et al., 2023; Zhang et al., 2023; Schwarzschild et al., 2024). Carlini et al. (2023) shows
that memorization increases with model size, example duplication, and prompt length. Another line
examines attributing memorization to internal features and its impact on generalization (Feldman,
2020; Feldman & Zhang, 2020; Zheng & Jiang, 2022; Zhang et al., 2023), with Zheng & Jiang
(2022) highlighting the importance of long-tail instances for generalization. Recent works extend
memorization to knowledge units like n-grams (Cao et al., 2020; Kandpal et al., 2023; Mallen et al.,
2022), and Antoniades et al. (2024) distinguishes memorization from generalization based on n-
gram similarity. Additionally, research explores how knowledge memorization affects generation
quality, with Zhang et al. (2024) and Chang et al. (2024) finding that pretraining data imbalances
and long-tail knowledge intervals hinder learning and generation.

Culture bias in culture-conditioned generation tasks. Recent work on probing and evaluating
cultural bias in LLMs spans multiple areas. One approach compares the Western-Eastern dichotomy
in model generations related to culinary habits (Palta & Rudinger, 2023), etiquette (Dwivedi et al.,
2023), commonsense knowledge Nguyen et al. (2023), and other facts Keleg & Magdy (2023);
Naous et al. (2023); Khandelwal et al. (2023); Li et al. (2024b). Another evaluates LLMs’ cultural
understanding using socio-cultural surveys originally designed for humans, such as the World Values
Survey and Pew Global Attitudes Survey (Ramezani & Xu, 2023; Tao et al., 2023; Durmus et al.,
2023). Additionally, works propose using LLM generation to create new resources and benchmarks
for cultural knowledge(Ziems et al., 2023; Huang & Yang, 2023; Fung et al., 2024).

3 ANALYSIS SETUP

3.1 SYMBOL CATEGORIES IN CULTURE-CONDITIONED GENERATIONS

As shown in Figure 1, the entity symbols can be categorized into three types: independent symbols,
memorized symbols, and generalized symbols.

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Symbol Type

Food Examples

Clothing Examples

Independent
Memorized
Generalized

Chicken, Rice, Meat
Miso Soup, Kalamari, Pho
Chicken with Rice, Noodle Soup

Jeans, Shirt, Sweater
Cheongsam, Yukata, Keffiyeh
Long Top, Gown, Blue Shirt

Table 1: Examples of the three types of symbols from Food and Clothing

Independent symbols appear in more than 50% of cultures’ generations, but they are not associated
with any specific culture. In addition, they appear with high frequency in pretraining corpora. The
average of counts of independent symbols is almost 350000 times of the average of counts of non-
independent symbols in pretraining corpora.

Memorized symbols are highly associated with a few cultures, and the association can be grounded
to the co-occurrence of symbols and cultures in pretraining corpora. Our proposed memorization
attribution framework (see §3.3) categorizes memorized symbols based on the ratio of pretraining
documents contributing to LLM memorization of the culture/symbol association.

Generalized symbols are not highly associated with any culture, identified by the lack of pretraining
documents contributing to culture/symbol association memorization. Different from independent
symbols, a generalized symbol stem from some memorized symbol, where it refers to a broader
concept that encompasses the memorized symbol.

Table 1 shows examples of each type of symbol for both food and clothing generations.

3.2 DATA COLLECTION PROCESS

Model and Data. We conduct all of our analysis on OLMo-7B (Groeneveld et al., 2024) and
its pretraining corpora Dolma (Soldaini et al., 2024), as OLMo-7B is the most capable generative
large language model with open-sourced and indexed pretraining data. The same analysis could be
extended to other models in future works, as long as their pretraining data is accessible.

Scope. Following the prompts and settings of (Li et al., 2024b), we collect 300 generations for
each of 110 cultures on food and clothing topics. We choose food and clothing among all topics
introduced in (Li et al., 2024b) due to the variation of symbols observed in their generations, where
different cultures have different emblematic symbols. The systematic cultural symbol generation
methodology is provided in Appendix B

Generation. We use the default model implementations from huggingface, setting tempera-
ture=1.0, top p=0.95, top k=50, max tokens=30 and num return sequences=100, and period (‘.’)
as the stopping criteria. Ablations on hyper-parameters is in Appendix E.

3.3

IDENTIFYING KNOWLEDGE MEMORIZATION FROM CULTURE-CONDITIONED
GENERATIONS

In this section, we demonstrate our MEMOED pipeline for classifying memorized symbols. We first
introduce how MEMOED determines whether one document contributes to culture/symbol memo-
rization, and describe how we determine from all contributory documents (Figure 3).

First, we determine if a document contributes to culture/symbol association. Given a training
document D and a query, Q = [C, S] where C corresponds to a culture (represented by both country
and nationality, e.g. China and Chinese) and S corresponds to a symbol, we propose the following
criterion to assess whether document D contributes to the culture-symbol memorization. We first
start with some definitions:

Definition 1 (Document-Signal to Noise Ratio) For any culture C, we calculate the log ratio of
its frequency to the sum of frequency of all other cultures appearing in the same document. With
t representing each n-gram that refers to a culture, we define Document-Signal to Noise Ratio or

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Under review as a conference paper at ICLR 2025

Figure 3: MEMOED pipeline, demonstrated with Malaysian culture on food topic.

dSNR(Q, D) as:

dSNR(Q, D) = log2

(cid:18) (cid:80)
((cid:80)

t∈D

1t=C
1t̸=C) + ϵ

t∈D

(cid:19)

(1)

Documents that strongly contribute to culture/symbol memorization should have high dSNR, as the
documents must have higher signals (target culture) than noise (other cultures).

Definition 2 (Minimum Token Distance) For culture C and symbol S, we compute the minimum
token distance of the two n-grams in document D. To simulate pretraining setup, we tokenize the
document for more accurate subtoken counts. Considering n-grams with multiple words, minimum
token distance includes the length of the n-grams1. Consequently, the metric dTOK(Q, D) is defined
as follows:

where S and C correspond to all occurrences in the training document.

dTOK([C, S], D) =

min
∀Si∈S,Cj ∈C

|Si − Cj|

(2)

For the association of C and S to be memorized during pretraining, they must appear within the con-
text window of maximum sequence length for the model. A dTOK(Q, D) exceeding model sequence
length cannot contribute to memorization.

Definition 3 (Minimum Sentence Distance) For any culture C and symbol S, Minimum Sentence
Distance or dSENT(Q, D) measures the number of sentences separating the two n-grams, by splitting
the document D along delimiters like full-stops.

CRITERION FOR TRAINING DOCUMENT CLASSIFICATION

∀ (D, Q) =⇒






r(D, Q) = 1 if

(cid:26)dTOK(Q, D) ≤ 2048 & dSNR(Q, D) ≥ 0

dSENT(Q, D) ≤ 2

& dSNR(Q, D) ∈ [−1, 0)

r(D, Q) = 0 otherwise

Given that dSNR(Q, D) uses a logarithmic function to calculate the frequency strength of the target
culture in the pretraining document, scores greater than 0 signify a ratio ≥ 1, indicating that the
target culture is at least as frequent as all other cultures combined. Furthermore, with OLMo-7B’s
pre-training sequence length set at 2048, this value serves as the upper limit for dTOK(Q, D). A doc-
ument meeting both the positive dSNR(Q, D) criterion and the upper threshold criterion is considered
relevant to the culture/symbol association, i.e., r(D, Q) = 1.

Simultaneously, empirical observations indicate that documents with dSNR(Q, D) scores between −1
and 0 often contain excerpts that contribute significantly to the culture/symbol association, albeit not
extending to the entire document. For these cases, we apply the dSENT(Q, D) metric with a strict

1Algorithm is discussed in greater detail in Appendix 1

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🤖For dinner, my Malaysian neighbor probably likes eatingnasi lemak vegetable salad anchovies cucumber sushiMem.Gen.Pretraining CorporaMem.Gen.Mem.Gen.Mem.Gen.Mem.Gen.OLMo-7BMEMOedOver Mem.Over Mem.Over Mem.Over Mem.Over Mem.Contribution ScoreClassifyCollect SymbolsCulture=MalaysiaUnder review as a conference paper at ICLR 2025

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threshold of 2 to avoid over-counting. Relevant excerpts from various pretraining documents are
detailed in the Appendix F.

Second, we determine if a symbol is a memorized symbol of a culture. For a given symbol S
and any culture C ∈ CG (where CG denotes the set of cultures that generated the symbol S), we
retrieve a complete set of documents D. Dr ⊆ D represents the subset of documents classified as
contributory to the culture/symbol memorization using the criterion described above. Utilizing this
subset, we calculate the following metrics to determine if S is a memorized symbol for culture C:

Contribution Score. Contribution Score (CS) is the ratio of the number of contributory docu-
ments, denoted n(Dr), to the total number of documents in which the symbol S appears. This mea-
sure tells us for all documents where the symbol occurs, proportionally how many exhibit strong
association with given culture, helping us determine if the symbol is memorized for the culture. We
compute this measure for every culture C in CG, setting a lower bound at 1
N , where N represents
the total number of cultures in our set, i.e.110.

CS =

n(Dr)
n(S)

(3)

In scenarios where a symbol S is generated across
Determining memorization with z-score.
more than five cultures, i.e., n(CG) > 5, we calculate the ratios using Equation 3 for each culture
C ∈ CG. These ratios are then normalized to form a categorical distribution (See examples in Fig-
ure 2). We then compute the z-score of contribution scores for each culture within this distribution.
A threshold z-score of 2.6 (> 99.5%) is set to classify a symbol as memorized for a culture, which
means that the culture is in the top 0.5% percentile of cultures (top 1%, considering a total of 110
cultures) in terms of evidence for its association with the symbol being memorized.

CRITERION FOR MEMORIZATION CLASSIFICATION

∀ (C, S) =⇒






S ∈ m(C)

if

(cid:26)CS ≥ 1/N

CS ≥ 1/N & Z ≥ 2.6

if n(CG) ≤ 5
if n(CG) > 5

S ̸∈ m(C) otherwise

where m(C) corresponds to the set of memorized symbols for a culture C and N corresponds to
the number of cultures in our total set. If a symbol is generated for less than 5 cultures, we pick the
culture with the highest CS and accept it as memorization if its CS is higher than equi-probability.

3.4

IDENTIFYING OVERMEMORIZATION FROM CULTURE-CONDITIONED GENERATIONS

Besides memorized symbols that are found to have high culture/symbol association, models also
tend to generate independent symbols and memorized symbols from other cultures. These phenom-
ena suggest overmemorization: model is biased towards symbols or cultures with high frequency,
making retrieving these symbols easier during generations than symbols with higher association
with the culture. We identify two types of overmemorization: symbol overmemorization and culture
overmemorization.

Symbol Overmemorization. Symbol overmemorization occurs when certain symbols have sub-
stantially higher frequency in the pretraining corpora compared to other more culture-related sym-
bols, causing the model to prioritize generating the former over the latter. We hypothesize that most
symbol overmemorization occurs on independent symbols.

Culture Overmemorization. When the model generates memorized symbols of one culture
for a completely different culture, culture overmemorization happens. To understand the rea-
son, perform topic modeling on a subset of symbols and cultures. We extract all documents
containing both cultures and the generated symbol, and using LDA Blei et al. (2003) and
LLAMA-3.1-8B-Instruct Dubey et al. (2024) to extract common topic words in the docu-
ments in which the cultures co-occur2.

2See Appendix A for the topic modeling pipeline

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Under review as a conference paper at ICLR 2025

3.5

IDENTIFYING TRACEABLE GENERALIZATION FROM CULTURE-CONDITIONED
GENERATIONS

Topic

Food

Clothing

Memorised Symbol

Traceable Generalisation

Culture

Biryani
Ayam Goreng

Vegetable and Rice
Grilled Chicken

Salwar
Ao Dai

Long Top
Gown

Indian
Indonesian

Indian
Vietnamese

Table 2: Examples of Traceable Generalizations

Generalized symbols are neither identified by MEMOED as memorized symbols, due to insufficient
evidence in the pretraining data to confirm strong memorization for them, nor identified as an inde-
penent symbol that is overmemorized, due to its lower frequency in the pretraining data. However,
they may be inferred, or generalized, from memorized or overmemorized symbols.

To identify the generalized symbols that can be traced to memorized symbols, we resort to language
model’s own knowledge: if a model memorizes a symbol, it should be able to recite the definition
of the symbol, using phrases representing a broader concept of entities. For example, if a model
memorizes “kimono,” then it is able to define “kimono” as a type of “wrapped-front robe”.

We prompt OLMo-Instruct-7B to generate definitions of memorized symbols in a continued
generation task. Then, we map symbols who are previously categorized as non-memorized symbols
to these definitions using F1 score3. For symbols who find a mapping, they become generalized
symbols that are traced to the memorized symbol. Please note that these generalized symbols can
be cross-cultural in nature: a generalized symbol generated for one culture can as well be traced to
memorized symbols of a completely different culture. Some examples of traceable generalizations
are given in Table 2.

To identify generalized symbols that can be traced to overmemorized independent symbols, we look
for generations with symbols that partially contain or are a combination of independent symbols,
such as “black t-shirt” or “rice with meat.”

4 RESULTS

4.1 MEMORIZATION IS LIMITED FOR UNDER-REPRESENTED CULTURES

(a) Topic: Food

(b) Topic: Clothing

Figure 4: Geographical Distribution of Memorization

We observe a medium-to high correlation between 1) the number of memorized symbols for a culture
and 2) the count of documents in which the culture appears in the pretraining corpora. For food, we
obtained a Spearman correlation of 0.670 and a Kendall τ correlation of 0.507. For clothing, we
obtained a Spearman correlation of 0.540 and a Kendall τ correlation of 0.421.

Figure 7 shows the geographical distribution of memorized symbols. For food, 97 cultures out
of 110 have at least one memorized symbol and on average one culture has about 11 memorized

3See Appendix B.2 for details.

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symbols. In clothing, however, only 45 cultures out of 110 have at least one memorized symbol, i.e.
around 60% have no memorized symbols, and on average one culture has about only 2 memorized
symbols.

The limited memorization for under-represented cultures roots in the inadequate representation in
the pretraining corpora. According to the finds in Chang et al. (2024) that LLMs go through pe-
riodic forgetting of factual knowledge during pretraining, memorization requires the knowledge to
appear within intervals shorter than the forgetting interval. Therefore, symbols of under-represented
cultures are less likely to get memorized and generated within the top-k outputs; instead, symbols
not belonging to the culture (evidenced by how MEMOED find insufficient contributory documents)
are generated, a result of overmemorization(see analysis in §4.3).

4.2 MEMORIZED SYMBOLS ARE NOT ONLY CULTURALLY-EMBLEMATIC SYMBOLS

To dig deeper into the composition of memorized symbols, we recruit natives from the each respec-
tive culture and ask them whether each symbol “originates from” or “is emblematic to” their own
culture.

We annotate symbols of 8 cultures: American, Chinese, Filipino, Indian, Ghanaian, Japanese, Mex-
ican, Vietnamese. These are the only cultures having more than 25 active annotators who were born
in the culture but are currently in the United States. In total, we have recruited 257 annotators. Each
annotator is tasked with evaluating 11 questions, including one attention check question that was
designed as a simple verification question to ensure the reliability of the responses. An annotator
may annotate many times on different questions, and each symbol is annotated by 3 annotators. See
Appendix D for annotation instructions.

Overall, MEMOED’s classification of memorized symbols agrees with human classification of em-
blematic symbols, with a weighted F1 score of 0.845 on clothing and 0.670 on food.

However, not all memorized symbols are em-
blematic symbols to a culture. The rest of the
symbols consist of entities that are still used
in the culture a lot without being an emblem-
atic symbol: for example, “western style bridal
gown” is recognized as a memorized symbol
for Indian clothing, while “business suit” is rec-
ognized as a memorized symbol for Japanese
clothing. MEMOED is able to capture such as-
sociations from pretraining data that would oth-
erwise be neglected by human annotators.

4.3 OVERMEMORIZATION IS PREVALENT

count(Si)

Symbol Overmemorization. We count the
occurrence of all symbols using the Infin-
igram API Liu et al. (2024). We define
j count(Smj ) , where count(Si) is the
r =
count of an independent symbol in pretraining
data and count(Smj ) is the count of the j-th
unique memorized symbol among all genera-
tions in pretraining data.

(cid:80)

1
N

Figure 5: Ratio of 25 most frequently occur-
ring independent symbols in the pretraining cor-
pora (y-axis) over the 25 most frequently occur-
ring memorized symbols in the pretraining cor-
pora (x-axis) generated by the culture “China” for
the topic “food”. Independent symbols appear at
least as frequently and as high as 1000 times more
frequently as memorized symbols.

We find a moderate-to-high positive correlation for both clothing (spearman ρ = 0.551, Kendall
τ = 0.385) and food (spearman ρ = 0.519, Kendall τ = 0.385) on ratio r and the number of
cultures that the independent symbol is generated for. This indicates that high pretraining frequency
of independent symbols is magnitudes higher than the frequency of memorized symbols and
increases the chance of independent symbols being generated by cultures disassociated with the
symbols.

Culture Overmemorization.

We observe a moderate negative correlation for food (spear-

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man ρ = −0.521, Kendall τ = −0.364) between 1) the percentage of a culture’s response
containing another culture’s memorized symbol and 2) the number of topic-related pretraining
documents (see Table 8 for definition). We also observe a high positive correlation for both clothing
(Spearman ρ = 0.763, Kendall τ = 0.574) and food (Spearman ρ = 0.716, Kendall τ = 0.531)
between 1) the frequency of a culture’s memorized symbol being generated for some other culture
and 2) the number of topic-related pretraining documents. These results suggest that cultures
whose generations contain other cultures’ symbols tend to occur less-frequently in pretraining
documents, and cultures whose symbols tend to occur in other cultures’ generations are also those
more commonly appearing in pre-training documents. For additional results, see Appendix G.

We hypothesize that
if two cultures appear
in pretraining documents frequently, their re-
spective memorized symbols may leak to the
other cultures’ generations. Although a com-
prehensive study on each memorized symbol is
computationally impossible, we exemplify our
analysis with examples of “hijab”, “kimono”,
“biryani” and “churrasco” (See Appendix A for
execution details).

Each row in Table 3 shows a symbol,
the
culture for which it is a memorized symbol,
and the other culture for which it is generated
the second-most frequently. Table 4 shows the
rest of the cultures for which the symbols are
generated and their topic modeling results. Fig-
ure 6 shows an excerpt of a document in which
“hijab”, Iran and Saudia Arabia co-occur.

Figure 6: Excerpt from a relevant document for
“hijab”, “Iran” and “Saudi Arabia”.

Symbol

Mem.
Culture

Hijab

Iran

Kimono

Japan

Non-
Mem.
Culture
Saudi
Arabia
South
Korea

Biryani

India

Pakistan

Churrasco

Brazil

Chile

Topic Modeling Keywords

[woman, islamic, muslim, women, rights,
hijab, government, politics, people]
[culture, fashion, asian, art,
traditional, clothing, woman, tokyo,
wedding, food]
[food, recipe, restaurant, cooking,
recipes, biryani, chicken, dish,
dishes, cuisine]
[food, restaurant, experience, wine,
meat, rio, dining, fogo, bar, city]

Table 3: Keywords extracted from pretraining documents in cases of culture overmemorization.

4.4 TRACEABLE GENERALIZATION IS NOT CORRELATED WITH MEMORIZATION

On average, 3.1% and 5.0% of generations are generalized symbols for clothing and food, respec-
tively. Interestingly, higher number of memorized symbol does not lead to higher number of gener-
alized symbol. We only see a weak-to-none correlation (Spearman correlation of 0.17 and -0.03 for
clothing and food) between the two types of symbols. Table 9 shows the top and bottom 5 cultures
for memorized symbols and generalized symbols for the topic food. Mexico, India, Japan, Morocco
and Nigeria have the highest number of memorized symbols for food. However Morocco appears
among the top 5 cultures in generalized symbols while Japan appears in the bottom 5. Additionally,
cultures without any memorized symbols rank higher in number of generalized symbols (eg. Yeme-
nis for clothing and Tribagonian for food). Cultures such as these where the model wasn’t able to
memorise anything, prompts the need for generalisations in the next token distribution.

For symbols that partially contain or are a combination of independent symbols, we find that they
are generalizations which can be traced to independent symbols itself generated as a result of over-

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memorization of these symbols. These comprise of about 0.1% and 0.2% of generations on average
for food and clothing respectively but almost 1/3 of the unique symbols for clothing.

4.5 AN OVERVIEW OF MEMORIZATION, GENERALIZATION, AND OVERMEMORIZATION

(a) Mexico

(b) Trinidad

Figure 7: While some cultures contain no memorizations in their generations (Fig7b), cultures like
Mexico’s almost 1/2 generations comprise of memorizations (Fig 7a)

In our analysis, we extract 2370 unique symbols for food and 1002 for clothing. Of these, 4.1%
(98 symbols) and 10.9% (110 symbols) appear in over 50% of cultures, categorized as indepen-
dent symbols for food and clothing, respectively. For food, 46.12% (1098 symbols) are identified
as memorization, and 31.3% (713 symbols) as generalized symbols traced to memorization.
In
contrast, for clothing, 25.78% (258 symbols) are memorization, and 31.6% (317 symbols) are gen-
eralization traced to memorization. Additionally, a smaller fraction of food symbols (7.6%, or 180
symbols) and a significant portion of clothing symbols (nearly one-third, or 332 symbols) are gen-
eralization traced to independent symbols. The remaining small proportion of symbols include
hallucinations, typos, and brand names, not fitting into these categories.

While independent symbols only comprise of a small proportion of the total unique symbols ex-
tracted from responses, they comprise a significant proportion (91.12% for clothing and 79.2% for
food) of the total responses, indicating that they are sampled multiple times during the generation
process and showing high overmemorization for them. Additionally, memorization is especially
scarce in generated responses, averaging only 0.76% for clothing and 4.12% for food while trace-
able generalization averages to 3.1% and 4.9% for both topics respectively. However, as seen in
Figure 7, the extent of memorization in responses has very high variance (from 0% for Trinidad to
almost 42.2% for Mexico in food). Culture overmemorization, while only averaging 4% and 11%
respectively for clothing and food, exhibits high variance with cultures with a high number of mem-
orized symbols having lesser cases of generating symbols memorized for other cultures. It is also
visible in cases when certain cultures show common themes related to the topic in their pre-training
document 4.

5 CONCLUSION

In conclusion, our work introduces MEMOED, a framework for attributing culture-conditioned gen-
erations of language models to either memorization or generalization from pretraining data. By ana-
lyzing the appearance of symbols in model outputs across 110 cultures, we uncover a clear imbalance
in how many symbols language models memorize for high-frequency and low-frequency cultures.
In addition, models tend to overmemorize high-frequency symbols that are not specific to any cul-
ture, while struggling to generalize from memorized cultural symbols with lower prevalence in the
pretraining data. This highlights significant limitations in current pretraining processes, where mod-
els prioritize frequently occurring, independent symbols at the expense of diverse, culture-specific
knowledge. Our findings underscore the need for improved pretraining data and methods, and we
hope this research sparks further work on linking model behavior to data-driven insights.

4As shown through keywords in Table 3

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LIMITATIONS

MEMOED uses each individual document as the unit of memorization, while it is possible that one
document may contain multiple excerpts of culture/symbol co-occurrence within minimum token
threshold. However, we cannot exactly reproduce the contexts of the pretraining process as the
training batches are randomly ordered in OLMo-7B training.

Our study is only conducted on OLMo-7B due to the fact that it is the model with highest language
capability that also has open pretraining data. How our conclusions may hold for other models is
unknown; however, our methodology introduced in §3 is transferrable for analyzing any model, as
long as their pretraining data is accessible.

REPRODUCIBILITY STATEMENT

Algorithm. We provide accurate description of our analysis framework in Section 3, and addi-
tional details in the appendix.

Prompt Engineering. The prompts we used for generating culture-conditioned generations,
prompting for traceable generalization definition and topic modeling are included in the appendix.

Data and Source Code. Data and source code will be released upon acceptance.

Crowdsourcing.

Instructions for Prolific annotators are available in Appendix D.

ETHICS STATEMENT

Data. All data we collected through LLMs in our work are released publicly for usage and have
been duly scrutinized by the authors. Data for all human studies that we conduct are also publicly
released with this work, with appropriate annotator anonymizations.

Crowdsourcing. All our crowdworkers are currently residing in the United States, with countries
of birth from US, China, India, the Philipines, Ghana, Mexico and Vietnam. For all our human
studies, the task is set up in a manner that ensure that the annotators receive compensation that is
accepted by the platform ($12/hour). Furthermore, we ensure that we correspond with crowdworkers
over direct message to address their queries.

Potential Use. Our framework MEMOED may only be used for analysis that follow the ethics
guideline of the community. Using MEMOED on mal-intentioned searching for proprietary data is
a potential threat, but the authors strongly condemn doing so.

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A TOPIC MODELLING

A.1 METHODOLOGY

′

′

r ⊆ Dcc′

For any culture C and its set of memorized symbols m(C), we select a symbol S ∈ m(C) and
G which also generated S but not through a memorization. For each
identify the set of cultures C
culture C ′ ∈ C
G and for C, we retrieve pre-training documents where the two cultures co-occur,
forming a set Dcc′
. We apply the metrics defined in Section 3.3 to filter these documents, obtaining
that are relevant to the association of the two cultures. We further refine Dcc′
a subset Dcc′
r
by removing documents that do not contain the symbol S, resulting in a final set Dcc′s
, which is
relevant to the association between cultures C and C ′ and contains the memorized symbol S.
Subsequently, we use a sliding window of size 2048 to create chunks from each document d ∈ Dcc′s
.
We employ Latent Dirichlet Allocation (LDA) (Blei et al., 2003) to model five topics from each set
of chunks corresponding to a document. The modeled n-gram phrases with corresponding topic
probabilities are then prompted to LLAMA-3.1-8B-Instruct (Dubey et al., 2024) The LLM
generates interpretable n-gram topic phrases, which are then filtered for repetitions using cosine
similarity scores calculated with XLM-RoBERTa-large embeddings (Conneau, 2019). Finally,
we extract the top five keywords from these topics using TF-IDF.

r

r

A.2 PROMPT

In figure 8, we provide the prompt used for prompting LLAMA-3.1-8B-Instruct with the LDA
input and generating the outputs corresponding to interpretable topics which are inferred from the
LDA and we use to generate keywords.

In Table 4, we extend our study of pre-training documents (Table 3) pertaining to cultural overmem-
orization from one culture to another for more cases of cultures which generate these memorized
symbols with a lower count of relevant documents than the cultures discussed before. We notice
suprisingly similiar themes in the pre-training documents such as the discussion around ”religion”
in documents where Hijab, Iran and any culture X co-occur. For Kimono and Japan, we notice
a similar common theme surrounding ”fashion”. We hypothesize that such common themes also
cause models to overmemorize and generate memorizations from one culture into another and not
necessarily when the model is devoid of memorizations in the next token space (which would be the
case for cultures which have no memorizations).

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Figure 8: Prompt for LLAMA-3.1-8B in Topic Modelling Pipeline

B PROMPTS

B.1 PROMPTS TO GENERATE CULTURAL SYMBOLS

Symbols are collected following the approach of Culture-Gen Li et al. (2024b) but on OLMo-7B. We
prompt the model in a continuing generation task where we use the following topic-wise prompts:

• Food: My neighbor is [culture]. At dinner, [he/she/my neighbor] probably likes to eat
• Clothing: My neighbor is [culture]. [he/she/my neighbor] is probably wearing

We sample 100 generations for male, female, and gender-agnostic settings, and thus, for each cul-
ture, we get 300 generations. Language models usually complete this prompt with one or more
symbols. We took this completion and used LLAMA-3-70b-instruct to extract the symbols from
this generation and cache them locally. The prompt for extracting symbols can be found in Culture-
Gen.

B.2 TRACEABLE GENERALIZATION PROMPT

[instruction] in Table 5 refers to the instruction (note that any typos in the prompt are intentional and
included for the purpose of jailbreaking):

You are a helpful agent who is knewledgable about celtures from
all places. You are talking to a persen who is curious about
different cultures and their [topic]
Complete the sentence below with a few words or a phrase.
do not know the answer, print don’t know.

If you

C COMPUTING MINIMUM DISTANCE B/W TWO N-GRAMS

This section elaborates on the algorithm employed by us for computing minimum distance between
two n-grams in a pretraining document and reporting the dTOK(Q, D) metric. The metric defined
in Equation 2 calculates the context length difference between the n-grams C and S, as observed

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Symbol

Hijab

Hijab

Hijab

Hijab

Mem.
Culture
Iran

Non-Mem.
Culture
Iraq

Iran

Iran

Iran

Pakistan

Indonesia

Egypt

Kimono

Japan

Italy

Kimono

Japan

Kenya

Kimono

Biryani

Japan

India

El Salvador

Bangladesh

Biryani

India

Singapore

Biryani

India

Churrasco

Churrasco

Brazil

Brazil

Iran

Peru

Colombia

Churrasco

Brazil

Uruguay

Topic Modelling Keywords

[woman, government, islamic, war,
politics, kurdish, people, conflict,
protest, muslim]
[woman, muslim, islamic, women, hijab,
issues, government, rights, people,
culture]
[woman, islamic, muslim, hijab,
fashion, law, women, islam, culture,
government]
[woman, muslim, islamic, women, islam,
arab, government, hijab, culture,
politics]
[fashion, art, tokyo, culture, design,
food, hotel, experience, clothing,
travel]
[travel, fashion, art, experience,
culture, africa, african, design, food,
names]
[arts, martial, blue, color, dell,
laptop, ryu, asian, friends, indigo]
[food, restaurant, dish, recipe,
dishes, cuisine, cooking, bengali,
chicken, recipes]
[food, restaurant, dish, cuisine,
dishes, experience, biryani, chicken,
cooking, options]
[food, biryani, restaurant, cuisine,
dish, recipe, saffron, hyderabad,
spice, cooking]
[food, restaurant, experience, cuisine,
bar, dining, sushi, dish, london, city]
[food, restaurant, latin, bride,
dating, beach, cheese, recipe, meat,
travel]
[food, meat, restaurant, cuisine, rio,
american, bbq, experience, south, dish]

Table 4: Keywords modeled from pretraining documents in cases of culture overmemoriza-
tion(continued)

Topic
food
clothing

Prompt Template
[instruction]. [symbol] is probably a [culture] food item which is a type of
[instruction]. [symbol] is probably a [culture] clothing item which looks like a type of

Table 5: Prompts for generating definitions of memorized symbols to trace a generalisation to them

by the LLM during pre-training. We hypothesize that for a pre-trained language model with a
sequence length L, a smaller dTOK(Q, D) indicates more frequent co-occurrence of the two n-grams
across training batches. This frequent co-occurrence is likely to strengthen their association, thereby
increasing the relevance of a document to the relationship between C and S.

The algorithm described in Algorithm 1 computes the minimum token distance between two n-
grams within a text, using a tokenizer to process the input and mark relevant tokens. Initially, the
text is tokenized to capture each token’s positional offsets. The algorithm then marks tokens that
correspond to the specified n-grams, word and symbol, by iterating through the text to find these
n-grams and marking overlapping tokens with distinct values for each n-gram.

Following the marking phase, the algorithm calculates the minimum distance by iterating through
the marked tokens. It maintains pointers to the last positions of tokens related to word and symbol.

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Under review as a conference paper at ICLR 2025

When a token corresponding to one of the n-grams is encountered, the algorithm checks if the last
seen position of the opposite n-gram has been recorded and updates the minimum distance if the
current position is closer.

The procedure concludes by returning the minimum distance, which quantifies the proximity of the
n-grams and reflects their associative strength in the context of language model pre-training.

Algorithm 1 Calculate minimum token distance between two n-grams
1: procedure MINTOKENDISTANCE(text, word, symbol, tokenizer)
2:
3:
4:
5:

encoding ← tokenizer(text, return offsets mapping=True)
tokens ← encoding.tokens()
token of f sets ← encoding[′of f set mapping′]
marks ← [0] ∗ len(tokens)

▷ Mark tokens corresponding to symbol and word

for phrase ∈ {symbol, word} do

for start in text do

if text.f ind(phrase, start) ̸= −1 then

end ← start + len(phrase)
for i, (s, e) in enumerate token of f sets do

if s ̸= N one ∧ e ̸= N one ∧ s < end ∧ e > start then

marks[i] ← max(marks[i], if phrase = symbol then 2 else 1)

start ← end

min distance ← ∞
last symbol ← −1
last word ← −1

for i from 0 to len(marks) do
if marks[i] = 2 then
last symbol ← i
if last word ̸= −1 then

▷ Compute minimum distance between marked tokens

min distance ← min(min distance, i − last word)

else if marks[i] = 1 then

last word ← i
if last symbol ̸= −1 then

min distance ← min(min distance, i − last symbol)

return min distance

6:
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D HUMAN ANNOTATION SETUP USING PROLIFIC

We designed a human annotation task using Google Forms, automatically populated via Google
Apps Script with symbols related to food and clothing from eight different cultures. Figure 9 pro-
vides an overview of the form setup, while Figure 10 shows an example of a question where par-
ticipants were asked to evaluate whether a specific food is a cultural food item of some culture.
Annotators were required to select the most appropriate classification based on their knowledge of
the culture in question. This process enabled us to collect reliable data regarding culturally emblem-
atic food and clothing items.

E ABLATION STUDY

E.1 ABLATION ON HYPERPARAMETERS

In the original design of our decoding process, multinomial sampling was employed with a
temperature=1.0, top p=0.95, top k=50, max tokens=30, and
set of specified hyperparameters:
num return sequences=100. The stopping criterion was established as the period (‘.’) character. To
explore the impact of these parameters on the generation results, an ablation study was conducted
where top k values of 20 and 80, and temperature values of 0.75 and 1.25 were tested against the

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Figure 9: Example of Google Form Used for Cultural Food Annotation

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Figure 10: Sample Question from Google Form on Cultural Food Classification

Ordering

Food

From Top 10 (↑)

From Bottom 10 (↓)

Morocco - 107
Bangladesh - 99
Iceland - 99
Sweden - 96
Ethiopia - 90

France - 42
Singapore - 42
Britain - 38
Indonesia - 36
Australia - 35

w/ Clothing

Azerbaijan - 97
Bolivia - 96
Chile - 91
India - 76
Kenya - 74

Germany - 30
United States - 28
China - 26
Portugal - 24
France - 21

Table 6: Cultures chosen for ablating on OLMo-7B-0424 and their corresponding number of
unique symbols

original settings. We observed an overlap coefficient of greater than 90% in all the four cases. This
tells us that the sampling conditions did not cause or change our findings.

E.2 ABLATION ON OLMO-7B VARIANTS

In order to verify that conclusions we find on OLMo-7B hold on other modalities, we reproduce
some of the experiments on a newer variant of OLMo-7B, OLMo-7B-0424. We collect culture-
conditioned generations for both food and clothing on OLMo-7B-0424, which is trained on Dolma
1.7. Although OLMo-7B-0424is the same model family as OLMo-7B, Dolma 1.7 contains pre-
training documents that are not in Dolma 1.5, and OLMo-7B-0424 is trained with an updated al-
gorithm from OLMo-7B. Other models supported by the WIMBD API, such as Pythia(Biderman
et al., 2023), are not particularly capable of instruction following culture-conditioned generations,
and therefore, analyzing their generations is less informative.

Due to the time constraints of the rebuttal, we only reproduce two main correlations in the main
paper:

The number of cultures an independent symbol is generated for and the number of pretraining
documents it appears in (Section 4.3) For OLMo-7B-0424, we obtain a moderate-to-strong
correlation for both clothing (spearman ρ = 0.507, Kendall τ = 0.362) and food (spearman ρ =
0.416, Kendall τ = 0.313). Compared to OLMo-7B with clothing (spearman ρ = 0.521, Kendall
τ = 0.367) and food (spearman ρ = 0.358, Kendall τ = 0.260), we see that even though the
models and training data are different, the Spearman and Kendall correlations for food and clothing
remain the same (both moderate-to-strong correlations). This means that the number of cultures
an independent symbol was generated for and the number of pretraining documents it appears in is
positively correlated, regardless of the model.

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(a) dSNR: 6.599 ; dTOK: 4

(b) dSNR: -0.982 ; dSENT: 0

Figure 11: Examples of excerpts from relevant pre-training docs for Culture: “Indian” and Symbol:
“Naan”:

The number of memorized symbols for a culture and the number of pretraining documents it
appears in (Section 4.1) For OLMo-7B-0424, exhaustively searching for all memorized sym-
bols of all 110 cultures requires running MEMOED on all symbol-culture pairs, which is not feasible
due to the rebuttal time constraint. Therefore, we select 10 cultures out of 110, 5 from the 10 cul-
tures with the highest number of unique symbols generated by OLMo-7B-0424 and 5 from the 10
cultures with the lowest number of unique symbols generated by OLMo-7B-0424.

We obtain a moderate-to-strong correlation for both clothing (spearman ρ = 0.591, Kendall τ =
0.507) and food (spearman ρ = 0.829, Kendall τ = 0.659). Compared to OLMo-7B (on 110
cultures) with clothing (spearman ρ = 0.540, Kendall τ = 0.421) and food (spearman ρ = 0.670,
Kendall τ = 0.507), we see that even though OLMo-7B-0424 is tested on smaller number of
cultures, for both clothing and food, the correlation of OLMo-7B-0424. Therefore, the conclusion
still holds that higher pretraining document counts of cultures increase the number of memorized
symbols in culture-conditioned generations.

E.3 ABLATION ON Z-SCORE FOR MEMOED

We study whether selecting a different z-score threshold would change the conclusions of MEM-
OED on memorized symbols for all cultures. We perform an ablation study on setting the z-score to
2, which statistically means that the value is about 97.7 percentile. Empirically, a z-score below 2
does not indicate outliers, so we focus our ablation analysis only on cases where the z-score is 2.

When z=2, we get still get a moderate-to-strong correlation between 1) the number of memorized
symbols for a culture and 2) the count of documents in which the culture appears in the pretraining
corpora: for clothing, we obtain a spearman correlation of 0.569 and a Kendall correlation of 0.445;
food food, we obtain a spearman correlation of 0.688 and a Kendall correlation of 0.519. This
correlation is lower but similar to the original correlations found for z=2.6 (food: Spearman=0.670
and Kendall=0.507; clothing: Spearman=0.540 and Kendall=0.421), showing that our conclusion on
the relationship between a culture’s memorized symbols and the culture’s frequency in pretraining
data is robust to different z-score threshold.

In addition, we examine how lowering the z-score from 2.6 to 2 changes memorized symbols dis-
covered for each culture. We compare each metrics’s agreement with human evaluation on clothing:
when z=2.6, the weighted F1 score is 0.845, and when z=2, the weighted F1 score is 0.840. We can
see that z = 2 has a slightly lower agreement with human categorization, suggesting that additional
symbols that are marked as memorized symbols when z=2 are non-emblematic symbols according
to human culture experts.

F TRAINING DOCUMENT EXCERPTS

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(a) dSNR: 5.584 ; dTOK: 17

(b) dSNR: -0.841 ; dSENT: 0

Figure 12: Examples of excerpts from relevant pre-training docs for Culture: “Chinese” and Symbol:
“Wonton Noodle Soup”:

In this section, we present excerpts from the pre-training documents classified as contributory to a
culture-symbol association using MEMOED’s dSNR, dTOK and dSENT metrics.

In Figure 11, we present excerpts from two pre-training documents classified as contributory to the
association between the culture: Indian and the symbol: Naan. We also report the relevant metric
scores used to determine this. For Figure 11a, since the dSNR is greater than zero, the dTOK metric is
used to ascertain the classification of this document. As visible in the excerpt, the culture ”Indian”
appears numerous times and in close proximity to the symbol ”naan”. Additionally, upon seeing
the remaining part of the excerpt, we see that it is talking about Indian food items which indicates
the relevancy of this document towards the association. On the other hand, for Figure 11b, since
the dSNR is between 0 and -1, we use the dSENT metric as explained in Section 3.3. We can observe
similarly that although the ratio is less than zero, the document is not noisy and the local context is
about Indian food item.

Similarly, in Figure 12, we present excerpts from two pre-training documents classified as contrib-
utory to the association between the culture: Chinese and the symbol: Wonton Noodle Soup. We
can observe that the training document with a positive dSNR is not really talking about Chinese food
items but rather talks about a prominent Chinese festival i.e.Chinese New Year and mentions the
food delicacies being prepared then. Thus, through this it contributes to the association between the
culture and symbol. On the other hand, for the document with negative dSNR, we observe a rela-
tively high concentration of cultural mentions in this excerpt and on a global level, the topic being
discussed is restaurants in China when the food cultural symbol is mentioned. Hence we see how
this document potentially contributes to the culture-symbol association.

G ADDITIONAL RESULTS

G.1 CULTURE OVERMEMORIZATION

To further evaluate cultural overmemorization
across all 110 cultures, we obtain: (1) the per-
centage of a culture’s responses that contain an-
other culture’s memorized symbols; (2) the fre-
quency of overmemorization for each culture,
i.e. how often is a culture’s memorized symbol
generated for some other culture. Additionally,
we calculate the correlation between each cul-
ture’s metrics (1) and (2) with the frequency of
topic-relevant occurrences of that culture in the
pre-training corpora.

Figure 13: Correlation b/w Extent of Overmem-
orization and Pre-Training Counts for a Culture

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Under review as a conference paper at ICLR 2025

Culture

Trinbagonian
Macanese
Salvadoran
Zambian
Nicaraguan
Puertorrique˜na
Egyptian
Saudi
Andorran
Hong Konger

Overmemorizing Culture

Pre-Training Count Rank (/110)

American (0.4%)
American (0.5%)
American (1%)
American (0.6%)
American (0.4%)
American (0.6%)
Iranian (2.9%)
Iranian (6.2%)
French (0.3%)
French (0.6%)

101
100
99
94
85
70
27
45
110
38

Table 7: Cultures Identified from Leave-One-Out-Correlation

Topic

Keywords

favorite music

music, song, songs, album, albums, band, bands, singer, singers,
musician, musicians, genre, genres, concert, concerts
music instrument, music instruments, instrument, instruments
exercise, routine, workout, sport, sports
favorite show or movie movie, movies, film, films, TV show, TV shows, TV series, cinema

music instrument
exercise routine

food

picture
statue
clothing

food, foods, cuisine, cuisines, dish, dishes, meal, meals, recipe,
recipes, menu, menus, breakfast, lunch, dinner, snack, snacks
picture, pictures, painting, paintings, portrait, portraits
statue, statues, sculpture, sculptures
clothing, clothes, apparel, garment, garments, outfit, outfits, attire,
attires, dress, dresses, suit, suits, uniform, uniforms

Table 8: Keyword list that we use to filter for topic-related pretraining documents.

For (1), we observe a moderate negative correlation for food (spearman ρ = −0.521, Kendall
τ = −0.364) indicating that cultures with high culture overmemorization tend to occur less-
frequently in food-related pre-training documents. We have shown this correlation using a scat-
ter plot in Figure 13. However for clothing, we observe a weak negative correlation (spearman
ρ = −0.099, Kendall τ = −0.061). To investigate this, we conducted a leave-one-culture-out
experiment. In this analysis, we recalculated the correlations while systematically excluding one
culture at a time. We then identified and listed the top ten cultures causing the highest variation.
Notably, these cultures were predominantly those with significant overmemorization from regional
cultures or those less frequently mentioned in the pre-training data, such as Trinbagonian. We have
listed these ten cultures with the highest overmemorizing cultures in their responses (along with
the percentage of responses being these overmemorized symbols) and their pre-training occurrence
ranked out of all 110 cultures in Table 7. We observe that a majority of cultures have the highest
cultural overmemorization from America while Egypt and Saudi have a significant percentage of
their generations memorized from one culture i.e. Iran.

For (2), our observations indicate that 34 cultures related to clothing and 86 related to food were
overmemorized at least once in the generations. Upon calculating correlations with these cultures,
we observed moderate-to-high correlations for both clothing (Spearman ρ = 0.763, Kendall τ =
0.574) and food (Spearman ρ = 0.716, Kendall τ = 0.531). These results suggest that cultures
frequently overmemorized are also those more commonly appearing in topic-related pre-training
documents. We show this correlation through scatter plots for both clothing and food in Figure 14.

G.2 RESULTS OVERVIEW

Continuing from Section 4.5, in this section we expand upon our findings and present some more
results across the 110 cultures.

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(a) Topic: Food

(b) Topic: Clothing

Figure 14: Cultural Overmemorization

Ordering

w/ Memo

w/ Traceable Gen

Top 5 (↑)

Bottom 5 (↓)

Mexico
India
Japanese
Morocco
Nigeria

Qatar
South Africa
Tajikistan
Trinidad
Yemen

Trinidad
Venezuela
South Korea
Morocco
Georgia

Germany
Japan
United States
Italy
Denmark

Table 9: Memorization and Generalization Stats for Food

In Tables 9 and 10, we present the memorization and generalization statistics for food and clothing,
respectively. Specifically, we provide the names of the top 5 and bottom 5 cultures, ranked by the
percentage of their responses classified as either memorization or traceable generalization. Cultures
with the highest percentage of memorized responses tend to correspond to those that appear more

Ordering

Top 5 (↑)

Bottom 5 (↓)

w/ Memo

India
Saudi Arabia
Japan
Pakistan
Canada

Uruguay
Venezuela
Vietnam
Yemen
Zambia

w/ Traceable Gen

Uruguay
Venezuela
Vietnam
Yemen
Zambia

Colombia
Peru
Nicargua
Venezuela
United States

Table 10: Memorization and Generalization Stats for Clothing

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(a) China

(b) India

(c) Japan

Figure 15: Distributions of China, India and Japan responses for Food

frequently in the pretraining dataset. However, notable exceptions exist, such as the culture United
States, which, despite occurring frequently in the pretraining data and having a large number of
memorized symbols, exhibits only 3.01% of its total responses as memorized, as shown in Figure
17a.

We also observe that a culture with a high percentage of memorized responses does not necessarily
have a large number of unique memorized symbols. For instance, Pakistan ranks 4th in memoriza-
tion count for the topic of clothing but has relatively few unique memorized symbols. This indicates
that for some cultures, OLMo-7B tends to repeatedly generate the same memorized symbols when
sampled multiple times. Additionally, Table 10 shows that the bottom 5 cultures, which have the
lowest percentage of their responses classified as memorized, exhibit the highest percentage of trace-
able generalizations in their responses.

We further provide the distribution of additional cultures, similar to the analysis presented for Mex-
ico and Trinidad in Section 4.5. Figure 15 illustrates the distribution of Chinese, Japanese, and
Indian cultures for the topic of food. Notably, despite these three cultures being relatively high-
frequency in the pretraining data, all exhibit very high symbol overmemorization rates, exceeding
60% in each case. Interestingly, we also observe considerable variance in the overall presence of
memorization, ranging from almost 30% for India to only 11.5% for China. Additionally, all three
cultures exhibit a relatively low percentage of culture overmemorization. This is likely due to their
high frequency in the pretraining data, which results in their symbols being overmemorized in other,
less frequently occurring cultures.

In Figure 16, we compare the distributions of two less-frequently occurring cultures, i.e., Myanmar
and Yemen, for the topic of clothing. We observe that, apart from exhibiting very high symbol
overmemorization rates (greater than 70% in most cases), these cultures have no memorizations
according to the classification provided by MEMOED. Consequently, there are no percentages
of memorization recorded in their responses. Yemen, in particular, demonstrates a notably high
percentage of cultural overmemorization, approximately 21.1%.

Finally, in Figure 17, we present the distributions for the USA and Saudi Arabia within the topic
of clothing. The results for the USA are particularly striking, as it is one of the most frequently
occurring cultures in the pretraining dataset, yet nearly 96% of its responses consist solely of symbol
overmemorization. Despite containing a substantial number of unique memorized symbols, only
3% of its responses qualify as memorization. In contrast, Saudi Arabia exhibits greater diversity,
with significant percentages of both memorization and cultural overmemorization in its generated
outputs.

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1348
1349

(a) Myanmmar

(b) Yemen

Figure 16: Clothing Stats - Mynammar and Yemen

(a) USA

(b) Saudi Arabia

Figure 17: Clothing Stats - USA and Saudi Arabia

25

