Venue: NAACL 2025

# CultureInstruct: Curating Multi-Cultural Instructions at Scale

Viet-Thanh Pham\({}^{1}\), Zhuang Li\({}^{2}\), Lizhen Qu\({}^{1}\), Gholamreza Haffari\({}^{1}\)

\({}^{1}\)Department of Data Science & AI, Monash University, Australia

\({}^{2}\)Royal Melbourne Institute of Technology, Australia thanh.pham1@monash.edu, zhuang.li@mit.edu.au, lizhen.qu@monash.edu, gholamreza.haffari@monash.edu

Corresponding author.

###### Abstract

Large language models, despite their remarkable success in recent years, still exhibit severe cultural bias. Therefore, in this paper, we introduce CultureInstruct1, a large-scale instruction-tuning dataset designed to reduce cultural bias in LLMs. CultureInstruct is constructed with an automatic pipeline, utilizing public web sources and a specialized LLM to generate instruction. Our data comprises 430K instructions, ranging from classic NLP tasks to complex reasoning. CultureInstruct also covers 11 most relevant topics to cultural knowledge, making it highly diverse. Our experiments show that fine-tuning LLMs with CultureInstruct results in consistent improvements across three types of cultural benchmarks, including (i) general cultural knowledge, (ii) human opinions and values, and (iii) linguistic cultural bias. Our best model, QWen2-Instruct 72B + CultureInstruct, outperforms GPT-4o Mini and GPT-4o with 18.47% and 13.07% average relative improvements on cultural benchmarks.

Footnote 1: Dataset is available at [https://github.com/thanbyv2102/ClutureInstruct](https://github.com/thanbyv2102/ClutureInstruct).

## 1 Introduction

Natural Language Processing (NLP) has seen drastic development in recent years, driven largely by advancements in Large Language Models (LLMs). These models have demonstrated remarkable capabilities in understanding and generating human language across a wide array of tasks, from question-answering Arefeen et al. (2024) to complex reasoning Huang and Chang (2023). However, a critical challenge of LLMs remains unsolved: the integration of cultural knowledge into these models to ensure they operate unbiased across diverse linguistic and cultural contexts.

In addressing the problem of cultural biases, some works have attempted to actively mitigate these biases and improve the performance of LLMs on culturally diverse tasks. Researchers have explored various methods, such as prompt engineering with culturally specific context Wang et al. (2024); Alkhamssi et al. (2024); Li et al. (2024c) to dynamically steer models toward generating culturally appropriate responses in real-time applications. However, prompt engineering is notorious for its unreliability across different downstream tasks. Several works tried to continue pre-train LLMs on non-English languages Lin and Chen (2023); Pipatanakul et al. (2023) but this approach is time-consuming and very costly, despite its effectiveness. Thus, it is better to fine-tune LLMs with instructions related to cultural knowledge. Recent works have proposed instruction-tuning datasets that are culture-specific, but they are either limited to a small set of instruction tasks Shi et al. (2024) or only cover one aspect of cultural knowledge, such as cultural norms Fung et al. (2023) and human values Li et al. (2024a, b). These datasets, despite their high value in mitigating cultural biases, are only able to solve specific challenges within limited domains of cultural understanding. This narrow focus restricts the generalization potential of LLMs, making them less capable of handling a wide range of cultural contexts.

To bridge this gap, we present CultureInstruct, a large-scale instruction dataset of cultural knowledge. CultureInstruct covers various topics related to cultural knowledge, including cultural norms, human values, history, cuisine, art, and more. To construct the dataset, we propose to **generate cultural instructions automatically from web documents**. Specifically, we implement a four-step pipeline to produce CultureInstruct (Figure 1). At the first step - **Document Filtering**, we select culture-relevant documents from Dolma Soldaini et al. (2024) based on keyword filtering and apply a fastText model Joulin et al. (2016) trained on an aggregated set of multiple public cultural

datasets to further filter the documents. Secondly, at the **Instruction Generation** step, we utilize an open-source LLM - Bonito(Nayak et al., 2024) and tune it to generate instruction samples from the collected documents. This set of samples then goes through the third step - **Instruction Diversification**. In the final step, **Data Decontamination**, we remove samples that closely resemble any of the evaluation sets.

We validate the effectiveness of CultureInstruct by fine-tuning multiple series of LLMs, including Llama-3.1-InstructMeta (2024), Mistral-0.3-InstructJiang et al. (2023), Qwen2-InstructYang et al. (2024), OLMoGroenveld et al. (2024), and Gemma2-InstructTeam et al. (2024). Fine-tuning with CultureInstruct outperforms the base LLMs significantly on multiple types of cultural benchmarks, including: (i) benchmarks for general cultural knowledge - CulturalBenchChiu et al. (2024), CANDLE Nguyen et al. (2023), and EtiCorDwivedi et al. (2023), (ii) human values - GlobalOpinionQA Durmus et al. (2024); (iii) linguistic cultural bias - CAMeLNaous et al. (2024). These benchmarks are particularly relevant because they assess models on diverse aspects of cultural understanding. By evaluating models on such culturally sensitive tasks, we ensure that the fine-tuned models demonstrate not only general competence but also cultural inclusivity and fairness in global contexts. In summary, we make the following contributions:

* We propose an automated data construction pipeline for generating high-quality cultural instruction data from raw documents.
* Utilizing the proposed pipeline, we build CultureInstruct, a large-scale dataset of 430K multicultural instructions. CultureInstruct covers a wide range of 11 domains within cultural knowledge and includes multiple types of instruction tasks.
* Our experiments reveal that fine-tuning LLMs with CultureInstruct benefits models on cultural-related tasks (CANDLE and CulturalBench), reducing cultural biases of models in cross-lingual (CAMeL) and cross-cultural settings (EtiCor). CultureInstruct also makes LLMs more aligned with human values (GlobalOpinionQA). Our best model, Qwen2-Instruct 72B + CultureInstruct, outperforms GPT-4o Mini and GPT-4o with 18.47% and 13.07% average relative improvements on CANDLE, CulturalBench, and EtiCor.

## 2 Data Construction

In this section, we describe the pipeline for constructing CultureInstruct. As illustrated in Figure 1, the proposed pipeline consists of four processes: (1) selecting culture-relevant documents from DolmaSoldaini et al. (2024), (2) generating instruction data from the collected documents, (3) diversifying the instructions, and (4) decontaminate the data from the chosen cultural benchmarks.

### Document Filtering

The pipeline begins by filtering the documents from the Dolma corpus, which consists of two layers, keyword filtering and fastText retrieval. In the first layer, unlike previous works which only target one single aspect of cultural knowledge (eg. cultural norms Fung et al. (2023); Ziems et al. (2023)), we target a wider set of 11 cultural-related topics, including: general cultural knowledge, art, cuisine, cultural norms, festivals and national events, history, language, literature, music, religion, and social life. We first categorize the raw documents into these topics by applying a heuristic with multiple sets of keywords, corresponding to different topics (Appendix A.1). In the second filtering layer, we train a fastText model with seed data of 10 cultural-related datasets (Appendix A.2) and use the model to retrieve relevant documents. Specifically, we compute the embeddings of the documents and compute the similarity to samples in the seed data to select documents. After two filtering steps, the number of Dolma documents was reduced from an initial 2,532M to 12M after keyword filtering, and further down to 214K for instruction generation.

### Instruction Generation

Instruction data is generated using a tuned version of BonitoNayak et al. (2024). This model takes a document with a choice between 16 NLP tasks as input and generates the corresponding prompts & answers. We observe that the original Bonito model was trained with a large number of irrelevant documents to cultural knowledge (eg. scientific topics), hence it hallucinates when we provide cultural documents as input. Therefore, we construct a set of cultural instruction data to continue fine-tuning Bonito. To do this, we sample 200 

documents from each of the 11 topics, and prompt GPT-40 to generate the instruction data, resulting in 15,400 samples. The prompt template is given in Appendix B.1. We generate instructions following 6 NLP tasks (Extractive QA, Multiple-choice QA, Summarization, Textual Entailment, Topic Classification, and Sentence Completion), as well as an additional Chain-of-Thought reasoning task (Appendix B.3). The resulting data is used to continue fine-tuning Bonito. After fine-tuning, Bonito shows a large discrepancy in generation compared to its original version and can generate instructions effectively on cultural documents. Some comparisons of the models are described in Table 1, where the original Bonito hallucinates - revealing several problems, such as generating information not provided in the given context. Overall, 1.5M instructions are generated with our tuned Bonito.

### Instruction Diversification

We found that many of the generated instructions have semantically similar or duplicated contents, due to the repeated information across web pages. To deduplicate and diversify the data, we first compute the embeddings2 of the instruction data. We then calculate the cosine similarity scores between every pair of embeddings and consider a pair of embeddings is duplicated if their similarity score is high (\(>0.90\)). From the filtered instructions, we further diversify them by applying the prototypical data pruning method [8]. This approach starts by applying k-means clustering to the embedding space and progressively removing data points based on their distance to the closest cluster centroid. Following other works, we empirically set the value of k for k-means clustering to be the square root of the number of data points divided by two. We remove \(10\%\) of the data in each cluster. By doing this, the most semantically diverse samples remain. From the set of 1.5M raw instruction samples, more than 430K samples are kept after deduplication and diversification.

Footnote 2: We use sentence-transformers/all-mpnet-base-v2 model to obtain the embeddings.

### Data Decontamination

We perform data decontamination using two strategies, lexical-based and semantics-based:

* **Lexical-based** We follow Shao et al. (2024); Yue et al. (2024) to remove training samples that have a 10-gram matching with any of the samples in the benchmarks. For benchmark samples that are shorter than 10 grams but have at least 3 grams, exact matching is applied to filter out contaminated training samples.
* **Semantics-based** We compute the embeddings of samples using the same model in Section 2.3 with and remove samples that have cosine similarity of 0.9 or above with any of the test samples.

After the decontamination process, 99% of the data is retained, resulting in the final version of CultureInstruct.

## 3 CultureInstruct

### Dataset Statistics

We present the statistics of CultureInstruct and some other cultural training datasets in Table 2. Overall, CultureInstruct covers a broader range of 11 domains, making it particularly valuable for applications that require a comprehensive

Figure 1: Illustration of our pipeline for building CultureInstruct.

 

cultural understanding. With 430K samples, CultureInstruct provides a substantial amount of data as a robust resource for training models that require large and diverse datasets. While CANDLE is larger in terms of sample size (1.1M), its focus is only on cultural norms and commonsense knowledge, hence less diverse than CultureInstruct. CultureInstruct also covers data from 183 countries, making it highly diverse in cultural knowledge. Examples of CultureInstruct are provided in Appendix B.2.

### Data Analysis

Topic Distribution.Figure 2 presents the topic distribution of CultureInstruct, illustrating the proportions of various cultural topics represented within the dataset. Religion and History are the most prominent, followed by Cultural Norms and General topics. Notably, Social Life constitutes only 4.35% of the dataset, making it the least represented category. This is because data related to Social Life is more challenging to collect from public web corpora, as such content often resides in more culturally nuanced or user-generated contexts that are harder to filter. This data type needs human evaluation in the data processing pipeline to ensure quality, such as done in CultureBank(Shi et al., 2024). Despite this limitation, the overall distribution reflects a well-balanced range of cultural

\begin{table}
\begin{tabular}{l|p{79.6pt} p{113.8pt}|p{113.8pt}} \hline \multicolumn{1}{c|}{**Document**} & \multicolumn{1}{c}{**Uetnamese cultureis**} & \multicolumn{1}{c}{**Ontological Question**} & \multicolumn{1}{c}{**Generated Answer**} & \multicolumn{1}{c}{**Problems**} \\ \hline Extractive QAA & Bonito & How long does it take to cook Photo & A few minutes & Answer not mentioned in the document \\ \cline{2-4}  & Tuned Bonito & What are the characteristic flavors of Vietnamese cuisina? & Its characteristic flavors are sweet (augar), spicy (Bird’s eye chili), sour (lame), nucem (fish sauces), and flavored by a variety of mint and basil. & - \\ \cline{2-4}  & Tuned Bonito & What are the characteristic flavors of Vietnamanese cuisina? & Its characteristic flavors are sweet (augar), spicy (Bird’s eye chili), sour (lame), nucem (fish sauces), and flavored by a variety of mint and basil. & - \\ \hline Summarization & Bonito & Guess a valid title for the document: & Vietnam & Title is too general \\ \cline{2-4}  & Tuned Bonito & Give a little for the provided text & Vietnamese Cuisine & - \\ \hline Sentence Completion & Bonito & Pho is traditionally served with ... & a plate of bean spots, basil, lemon wedges, and chiL peppers on the side. & Answer not mentioned in the document \\ \cline{2-4}  & Tuned Bonito & Pho is a type of noodle scapp originating in ... & North Vietnam. & - \\ \hline \end{tabular}
\end{table}
Table 1: Generation results of the tuned Bonito vs. original Bonito on 3 NLP tasks. Blue highlights the information successfully extracted by the tuned Bonito.

\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Dataset** & **Domains covered** & **Data Source** & **\# of Samples** & **\# of Countries** \\ \hline NomBank(Ziems et al., 2023a) & Situational Social / Cultural Norms & Multiple databases to collect entities & 155K & 160 \\ \hline CANDLE(Nguyen et al., 2023) & Cultural Norms \& Commonsense Knowledge & C4 Corpus (Dodge et al., 2021) & 1.1M & 176 \\ \hline CultureBank(Shi et al., 2024) & Community-based Cultural Knowledge & Reddit, TikTok & 23K & 113 \\ \hline
**CultureInstruct (Ours)** & Cultural Norms, History, Cuisine, Art, Religion, Language, Music, Festivals, Literature, Social Life & Dolma Corpus (Soldaini et al., 2024) & 430K & 183 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of CultureInstruct vs. other cultural datasets, namely NormBank, CANDLE, and CultureBank.

Figure 2: Topic distribution of CultureInstruct.

 

topics, with most areas adequately represented.

Instruction QualityTo analyze the design choice of the instruction generation process in our pipeline, we compare the instruction quality of the original Bonito model and our tuned Bonito. We follow Xu et al. (2024) and prompt Llama-3.1-Instruct 70B to judge the quality of the generated instructions (see Appendix B.4 for the prompt template and inference configuration). The quality distributions of the tuned Bonito and the original Bonito are illustrated in Figure 3. The majority of instructions generated by the tuned Bonito model are rated as "Excellent" (87.07%), while the ones from the original Bonito are mostly rated as "Average" (55.57%). To further validate the judgment made by Llama-3.1-Instruct 70B, we conduct a manual check on a sub-sample of CultureInstruct. Llama-3.1-Instruct agrees with humans annotation at 70.93% accuracy rate; more details are described in Appendix C.1. We also analyze how instruction quality correlates with cultural knowledge relevance in Appendix C.2.

Dataset CoverageWe analyze the coverage of CultureInstruct vs other cultural training datasets, namely CANDLE, CultureBank, and World Value Survey. Specifically, we embed the samples in datasets with the embedding model in Section 2.3, then project the embedding to a two-dimensional space using t-SNE (van der Maaten and Hinton, 2008).

Figure 4 shows the t-SNE contour plot, where the Gaussian kernel is used for density estimation. For the raw scatter plot of t-SNE, see Appendix C.3. The representation of CultureInstruct encompasses areas covered by other datasets. However, most of the area of CultureBank is not covered by our dataset. This is because CultureBank covers the Social Life topic, while this is the least frequent topic in CultureInstruct. Nevertheless, CultureInstruct remains prominent as it includes instructions from diverse topics. We analyze this by computing the average Euclidean distance of each topic in CultureInstruct against the other datasets. Results are presented in Table 3. The Literature topic has the largest distance to CultureBank and CANDLE, indicating its under-representation in these datasets, while for World Value Survey it is the Festivals topic. Other underrepresented topics include Music, History, and Religion.

### Additional Public Datasets

To enhance the quality of CultureConstruct, we concatenate the constructed dataset with some other culture-related public datasets. Specifically, CultureBank is the most important dataset to include, as it covers the Social Life topic that CultureInstruct has very few samples of. Other culture-related datasets include: (i) a dataset of situational social norms - NormBankZiems et al. (2023), (ii) a dataset of multicultural proverbs and sayings - MAPS Liu et al. (2023), (iii) the training

\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Topic** & **CultureBank** & **CANDLE** & **World Value Survey** \\ \hline Religion & 127.48 & 97.15 & 119.42 \\ History & 121.50 & 94.83 & 115.99 \\ Cultural News & 112.92 & 99.80 & 132.00 \\ Custine & 104.99 & 91.83 & 116.19 \\ Art & 114.19 & 89.37 & 119.71 \\ Music & 136.71 & 105.15 & 124.56 \\ Festivals & 103.85 & 79.80 & **133.31** \\ Licentre & **141.12** & **109.73** & 125.98 \\ Social Life & 96.26 & 82.42 & 100.34 \\ Language & 118.58 & 90.54 & 125.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average Euclidean Distance per topic in CultureInstruct against CultureBank, CANDLE, and World Value Survey. **Bold** highlights the topics having the largest distances to each cultural dataset.

Figure 4: t-SNE contour plot of CultureInstruct and other culture-related dataset, namely CANDLE, CultureBank, World Value Survey.

Figure 3: Instruction quality distribution of the original Bonito vs. the tuned Bonito.

 

set of CANDLE Nguyen et al. (2023), representing culture-related commonsense knowledge.

## 4 Experiments

In our experiments, we investigate to what extent Cultureinstruct improves the performance of various LLMs on cultural benchmarks, covering three aspects: (i) general cultural knowledge and cultural norms, (ii) linguistic cultural bias, and (iii) human values.

### Models

Training Setups.The following models are used for training with Cultureinstruct: Llama-3.1-Instruct (8B & 70B) Meta (2024), Qwen2-Instruct (7B & 72B) Yang et al. (2024), Gemma2-Instruct 9B Team et al. (2024), Mistral-0.3-Instruct 7B Jiang et al. (2023), OLMo 7B Groeneveld et al. (2024). For all models, we use QLoRA Dettmers et al. (2023) during fine-tuning to save computational resources. Specifically, for models having less than 70B parameters, we use \(r=256\) and \(\alpha=256\), while \(r=128\) and \(\alpha=128\) are used for the larger models. All models are fine-tuned for 2 epochs on one A100-80B GPU.

Inference Setups.We evaluate all of the above-mentioned models and also include results from GPT-4o (version 2024-08-06) and GPT-4o Mini (version 2024-07-18). For these two models, we use the default decoding parameters for inference and in terms of open-sourced LLMs, we use greedy decoding for inference, except for the GlobalOpinionQA benchmark.

### Cultural Benchmarks

We study the effect of fine-tuning on the following cultural benchmarks:

General Cultural Knowledge.We experiment with CANDLE Nguyen et al. (2023) and CulturalBench Chiu et al. (2024) as multiple-choice question benchmarks. Both of these benchmarks cover multicultural knowledge, with CulturalBench being a much harder benchmark due to its red-teaming approach in the data creation process. We also consider the EtiCor benchmark Dwivedi et al. (2023) for evaluation. EtiCor covers region-specific etiquette for 5 regions: East Asia, India, Middle East & Africa, North America & Europe, and Latin America. With this data, the corresponding evaluation task is "Etiquette Sensitivity". Given a statement about etiquette, the task is to predict whether the statement is appropriate for a region.

Linguistic Cultural Bias.We evaluate LLMs on the CAMeL benchmark Naous et al. (2024). Following their work, using CAMeL, we examine the performance in Arabic of models on the text-infilling task to show the model preference of Western vs. Arab entities. Specifically, we compute the probabilities of \([MASK]\) tokens in CAMeL prompts being Western or Arab entities. Then, the Cultural Bias Score (CBS) is used as the evaluation metric, where a lower score indicates less bias towards Western entities.

Human Values.GlobalOpinionQA Durmus et al. (2024) is chosen as our benchmark for human values and opinions. This benchmark aggregates the Pew Global Attitudes Survey3 and the World Values Survey4, comprising around 2.5K questions. We follow their work to calculate the Jensen-Shannon distance between the human and model distributions, averaging over 5 prompts.

Footnote 3: [https://www.pewresearch.org/feature/global-abortion/](https://www.pewresearch.org/feature/global-abortion/)

Footnote 4: [https://www.worldvaluessurvey.org/wvs.jsp](https://www.worldvaluessurvey.org/wvs.jsp)

### Main Results

General Cultural Knowledge.Table 4 shows the results of models on general cultural knowledge and norms benchmarks (CANDLE and CultinAlBench). Regarding the models ranging from 7B to 9B parameter sizes, fine-tuning with Cultureinstruct led to consistent improvements in both benchmarks. The models also showed marked improvements in the higher parameter category (70B-72B). Notably, Qwen2-Instruct 72B, started with a relatively strong baseline performance (89.20%) but still exhibited improvement, reaching 90.80% accuracy. This model even outperforms GPT-4o and GPT-4o Mini on both benchmarks. This indicates that even models with high initial cultural competency can further benefit from fine-tuning with Cultureinstruct. Across both CANDLE and CulturalBench benchmarks, LLMs with smaller parameter sizes (7B to 9B) tend to exhibit larger relative improvements after fine-tuning than the ones with larger parameter sizes (70B to 72B). This is due to the limitation in computational resources, we have to use a lower LoRA rank for larger LLMs, resulting in less forceful fine-tuning.

 

Regarding the EtiCor benchmark, fine-tuning with CultureInstruct still shows consistent improvements across all of the regions Table 5, though with some exceptions in the Western region (America & Europe). For instance, Llama-3.1-Instruct 8B initially achieves an F1 score of 85.35 for America & Europe, but after fine-tuning with CultureInstruct, its performance slightly drops to 83.37. A similar pattern can be observed with Qwen2-Instruct 7B, where its score decreases after fine-tuning. The results suggest that while fine-tuning with CultureInstruct enhances cultural awareness of LLMs in non-Western regions, it may introduce slight trade-offs in regions where the model already performs well, like America & Europe. This could indicate that the training emphasizes broader multicultural knowledge, which benefits global regions but may occasionally conflict with region-specific nuances in Western contexts.

Linguistic Cultural Bias.The experimental results of the linguistic cultural bias benchmark - CAMeL are provided in Table 4. Qwen2-Instruct\(7B+\)CultureInstruct achieves the best score of 41.78, indicating the lowest cultural bias. Similarly, OLMo \(7B+\)CultureInstruct shows a substantial reduction from 49.01 to 42.02 after fine-tuning. This demonstrates that the multi-cultural knowledge embedded in CultureInstruct, despite being English-based, positively impacts the performance of LLMs in reducing bias on non-English tasks like CAMeL.

Human Values.The results from the GlobalOpinionQA benchmark in Table 6 show that fine-tuning with CultureInstruct consistently improves model performance by reducing the Jensen-Shannon divergence, indicating better alignment with human values and opinions. Across different regions, models fine-tuned with CultureInstruct exhibit lower divergence, which suggests that they are more aligned with the regional opinions assessed in the benchmark. These results reveal that CultureInstruct helps models better understand and align with a variety of regional opinions, making them more culturally aware and responsive to human values across different countries.

Standard LLM Benchmarks.We report the results of models on several standard LLM benchmarks that are not related to cultural knowledge (HellaSwagz [Zellers et al., 2019], CommonsenseQA [Talmor et al., 2019], and GPQA [Rein et al., 2024]) in Appendix D.1 to show the performance tradeoffs when fine-tuning with CultureInstruct.

\begin{table}
\begin{tabular}{l c c c} \hline \hline Model & \multicolumn{4}{c}{Cultural Benchmarks} \\ \cline{2-4}  & CANDLE (Accuracy \(\uparrow\)) & CulturalBench (Accuracy \(\uparrow\)) & CAMEL (Calutural Bias Score \(\downarrow\)) \\ \hline \multicolumn{4}{l}{**Parameter Size between 7B and 9B**} \\ \hline Llama-3.1–Instruct 8B & 72.20 & 48.81 & 51.42 \\ + CultureInstruct & 79.80 & 59.52 & 49.17 \\ \hline Qwen2-Instruct 7B & 77.80 & 47.62 & 47.51 \\ + CultureInstruct & 88.00 & 67.46 & **41.78** \\ \hline Gemma2-Instruct 9B & 83.20 & 52.38 & 53.06 \\ + CultureInstruct & 83.40 & 67.44 & 47.99 \\ \hline Mistrak-0.3–Isstruct 7B & 76.60 & 44.84 & 50.38 \\ + CultureInstruct & 78.40 & 61.11 & 49.54 \\ \hline OLMo 7B & 67.00 & 37.30 & 49.01 \\ + CultureInstruct & 70.20 & 46.83 & 42.02 \\ \hline \multicolumn{4}{l}{**Parameter Size between 7MB and 72B**} \\ \hline Llama-3.1-Instruct 70B & 83.20 & 61.90 & 50.75 \\ + CultureInstruct & 89.80 & 71.43 & 47.60 \\ \hline Owen2-Instruct 72B & 89.20 & 65.08 & 47.78 \\ + CultureInstruct & **90.80** & **73.98** & 42.20 \\ \hline \multicolumn{4}{l}{**Commercial LLM**} \\ \hline GPT-40 Mini (version 2024-07-18) & 87.58 & 64.29 & N/A \\ \hline GPT-40 (version 2024-08-06) & 89.38 & 73.00 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of LLMs on cultural benchmarks. CANDLE and CulturalBench belong to the **General Cultural Knowledge** benchmark. CAMel represents the **Linguistic Cultural Bias** type. **Blue** highlights the best results in each column. Results of GPT-40 and GPT-40 Mini on CAMeL are unavailable due to classified token probabilities.

 



### Effects of Fine-tuning on Model Outputs

In this section, we compare some base LLMs with their corresponding fine-tuned version with CultureConstruct to evaluate the hallucination and toxicity level after fine-tuning. To analyze the hallucination level of models, we evaluated the original and finetuned Llama-3.1-Instruct 8B and Qwen2-Instruct 7B on the TruthfulQA benchmark Lin et al. (2022) - a dataset designed to specifically address the challenge of truthfulness and factual accuracy in AI-generated responses. Results on the subsets of the TruthfulQA benchmark are described in Table 7. After fine-tuning the models with CultureConstruct, the results demonstrate no significant performance decreases across any of the TruthfulQA subsets. Specifically, the accuracy on both MC1 and MC2 remains stable, and there is no noticeable decline in the ROUGE-L scores for generative outputs. These findings confirm that CultureConstruct does not negatively impact the factual accuracy or trustworthiness of the models, proving its compatibility with high-quality, reliable language generation.

Regarding the toxicity levels of models after fine-tuning, Table 8 shows the results of models on the ToxiGen benchmark Hartvigsen et al. (2022). Similar to the TruthfulQA benchmark, after fine-tuning the models with CultureInitruct, the results reveal that there are no significant changes in performance, with accuracy remaining nearly identical. Specifically, Llama-3.1-Instruct 8B shows a minor increase, while Qwen2-Instruct 7B exhibits virtually no difference in accuracy. These results indicate that fine-tuning with CultureConstruct does not increase the toxicity level of the models, ensuring their outputs remain aligned with ethical and nontoxic standards.

## 5 Related Works

**Cultural Knowledge Acquisition.** In recent years, various cultural datasets have been constructed, and most of the works involve utilizing public sources and performing data synthe

\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{Model} & \multicolumn{4}{c}{GlobalOptionQA Benchmark (Jensen–Shannon divergence \(\downarrow\))} \\ \cline{2-6}  & America & Japan & Germany & China \\ \hline \hline
**Parameter Size between 7B and 9B** & & & & \\ \hline \hline
**Llama-3.1-Instruct 8B** & 65.50 & 66.08 & 65.62 & 65.12 \\ + CultureInstruct & **55.71** & 59.51 & 56.23 & 59.05 \\ \hline Qwen2-Instruct 7B & 64.75 & 63.71 & 64.96 & 64.47 \\ + CultureInstruct & 63.15 & 63.77 & 61.11 & 63.05 \\ \hline \hline
**Parameter Size between 7B and 72B** & & & & \\ \hline \hline
**Llama-3.1-Instruct 70B** & 62.87 & 63.81 & 63.05 & 63.05 \\ + CultureInstruct & 55.77 & **56.29** & 56.13 & 57.01 \\ \hline \hline
**Qwen2-Instruct 72B** & 61.31 & 66.30 & 65.70 & 65.77 \\ + CultureInstruct & 55.67 & 56.62 & **56.01** & **56.96** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of LLMs on GlobalOpinionQA benchmark (Jensen–Shannon divergence). **Blue** highlights the best results from each column. Results of GPT-4o and GPT-4o Mini are unavailable due to classified token probabilities.

\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{Model} & \multicolumn{4}{c}{EtCor Benchmark (F1 \(\uparrow\))} \\ \cline{2-6}  & Latin America & India & Middle East & America \& Europe & East Asia \\ \hline \hline
**Parameter Size between 7B and 9B** & & & & & \\ \hline \hline Llama-3.1-Instruct 8B & 60.90 & 67.73 & 59.53 & 85.35 & 63.70 \\ + CultureInstrustruct & 71.35 & 69.98 & 72.98 & 83.37 & 72.73 \\ \hline Qwen2-Instruct 7B & 68.35 & 76.75 & 72.54 & 84.84 & 72.16 \\ + CultureInstruct & 77.43 & **79.04** & 78.70 & **87.77** & 77.79 \\ \hline \hline
**Parameter Size between 7B and 72B** & & & & & \\ \hline \hline Llama-3.1-Instruct 70B & 62.97 & 73.70 & 61.67 & 85.93 & 65.52 \\ + CultureInstruct & 73.20 & 77.98 & 76.03 & 85.52 & 76.81 \\ \hline \hline
**Owen2-Instruct 72B** & 69.18 & 77.85 & 76.05 & 86.57 & 76.05 \\ + CultureInstruct & **78.08** & 78.77 & **78.97** & 85.37 & **78.59** \\ \hline
**Commercial LLM** & & & & & \\ \hline GPT-4o Mini (version 2024-07-18) & 71.83 & 58.45 & 75.44 & 76.75 & 51.99 \\ \hline GPT-4o (version 2024-08-06) & 74.81 & 59.80 & 78.16 & 79.96 & 54.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of LLMs on the EtiCor benchmark (F1 scores). **Blue** highlights the best results in each column.



sis for collecting cultural data. AlKhamissi et al. (2024); Masoud et al. (2023); Li et al. (2024a) utilize global opinion surveys, such as the World Value Survey and Hofstede survey to construct their cultural benchmarks. Similarly, CulturePark (Li et al., 2024b) proposed a multi-agent framework to synthesize data from the World Value Survey. Several studies have collected social norms from different cultures by prompting LLMs, including NormBank Ziems et al. (2023), NormSage Fung et al. (2023), SocialDial Zhan et al. (2023), ChineseNormBase Qu et al. (2019), and MulticulturalNormalBase Pham et al. (2024). CultureAtlas Fung et al. (2024), CultureBank Shi et al. (2024), and CANDLE Nguyen et al. (2023) also sourced their data from public web pages with the help of robust NLP models for data filtering and synthesis. Our work closely follows the above-mentioned datasets, in which we also utilize public data sources and a specialized LLM for instruction generation. However, our work addresses the research gap of the previous works by including multiple facets of cultural knowledge, making CultureInstruct more diverse.

Mitigating Cultural BiasSeveral works have proposed approaches to mitigate the notorious cultural bias of LLMs, which can be categorized into three types: (i) prompt engineering Wang et al. (2024); AlKhamissi et al. (2024); Li et al. (2024c); Zhan et al. (2024), (ii) continued pre-training Lin and Chen (2023); Pipatanakul et al. (2023), and (iii) instruction fine-tuning Shi et al. (2024); Fung et al. (2023); Li et al. (2024a, b). In this work, we attempted to improve LLMs with the instruction fine-tuning approach, which is more consistent than prompt engineering and more cost-efficient than continued pre-training.

## 6 Conclusions

In this paper, we introduce CultureInstruct, an instruction-tuning dataset designed specifically for improving cultural knowledge and reducing cultural bias of LLMs. CultureInstruct consists of 430K meticulously-crafted instructions, covering 11 most relevant topics to cultural knowledge. Our experiments show that by fine-tuning with CultureInstruct, various LLMs show consistent improvements on three types of cultural benchmarks, namely general cultural knowledge, linguistic cultural bias, and human values. In the future, we will extend the dataset to cover more instruction tasks that are specific to cultural knowledge, as well as extending CultureInstruct to multilingual settings.

### Acknowledgement

This work is partly supported by the ARC Future Fellowship FT190100039. This material is based on research sponsored by DARPA under agreement number HR001122C0029 (CCU Program). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

### Limitations

The samples in CultureInstruct are provided in English. Therefore, our dataset may not capture the cultural nuances appearing in language-specific characteristics. This is mainly due to the limitation of the Bonito model, as it can only generate English instructions, and extending it to multilingual may take a large amount of resources. In future work, we will attempt to apply translation models or extend the instruction generation model with multilingual capabilities.

Another limitation lies in the variety of models chosen for fine-tuning. Although these models are commonly used in other works, they still cannot

\begin{table}
\begin{tabular}{l c c c} \hline \hline Model & TruthfulQA Benchmark & TruthfulQA MC1 (Acc \(\uparrow\)) & TruthfulQA MC2 (Acc \(\uparrow\)) & TruthfulQA Gen (ROUGEL \(\downarrow\) T) \\ \hline \hline  11mm-3.1-Instruct SB & 36.84 & 54.44 & 65.24 \\   + CultureInstruct & 36.79 & 54.31 & 65.18 \\ \hline   Qven2-Instruct TB & 38.80 & 56.30 & 53.12 \\   + CultureInstruct & 38.77 & 56.14 & 52.98 \\ \hline \hline  & & & \\ \end{tabular}
\end{table}
Table 7: Performance of LLMs on the subsets of the TruthfulQA benchmark.

\begin{table}
\begin{tabular}{l c} \hline \hline Model & ToxGen Benchmark (Acc \(\uparrow\)) \\ \hline \hline  11mm-3.1-Instruct BB & 45.85 \\   + CultureInstruct & 45.87 \\ \hline   Qven2-Insstruct 7B & 47.77 \\   + CultureInstruct & 47.73 \\ \hline \hline  & & \\ \end{tabular}
\end{table}
Table 8: Performance of models on the ToxIGen Benchmark 

fully represent the performance of other LLMs, such as those with less than 7B parameters or more than 72B parameters. Hence, we will expand the list of models to further test the usefulness of CultureInstruct.

### Ethical Statement

To regulate the use of CultureInstruct and the models fine-tuned using it, we outline several ethical considerations and emphasize potential risks.

Misuse of Data.The primary goal of CultureInstruct is to reduce cultural bias in LLMs by integrating diverse cultural knowledge. Despite our efforts to mitigate bias, this resource may contain some content that could be perceived as sensitive or controversial, particularly in the context of cultural differences. CultureInstruct is constructed using publicly available web data and synthesized instructions generated by a specialized LLM. It is released for academic and research purposes and does not reflect the personal opinions or values of the authors. Any form of misuse, including employing this dataset to promote cultural discrimination or division, is strictly prohibited. Users are expected to adhere to the highest ethical standards, ensuring responsible use of this resource in alignment with research ethics. The authors and creators of CultureInstruct hold no liability for misuse, misinterpretation, or unintended consequences of the dataset or models fine-tuned on it.

Risks in Data Generation.Since CultureInstruct involves automatically generated instructions from a specialized LLM, there is a risk of inaccuracies or unintended bias within the dataset. We have tried to tune this LLM for better generation, but acknowledge that some instances of bias may remain. The creation of CultureInstruct followed ethical guidelines to ensure that the dataset is inclusive and culturally diverse.

Potential Bias.While CultureInstruct aims to minimize cultural bias in LLMs, it is impossible to eliminate bias entirely. The dataset and the models fine-tuned with it should be viewed as tools for research and improvement rather than final solutions to the issue of cultural bias in AI. We encourage further research, feedback, and iteration to continuously address and refine cultural fairness in AI systems.

By releasing CultureInstruct, we aim to contribute to the responsible development of AI technologies that are more culturally aware. All users of this resource are expected to use it under ethical research practices, ensuring transparency and fairness.

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Document Filtering

### Keyword Filtering

Table 11 shows the list of keywords for each domain that we used in the Keyword Filtering step. Below is the heuristic for filtering documents:

* If the document contains less than \(n\) keywords from the "General Cultural Knowledge" or other domain lists, we label the document as "irrelevant".
* If the document contains less than \(n\) domain keywords and more than \(n\) general keywords, we label the document as "General culture knowledge".
* If the document contains more than \(n\) domain keywords, we label the document with the domain label with the highest number of keyword occurrences.
* Finally, we keep the document with the highest number of keyword occurrences.

In our approach, we empirically set \(n=3\) and apply the keyword filtering with the heuristic above.

### FastText Retrieval

**Training Configurations** We utilize the fastText library to fine-tune a model with a vector dimension of 300, a learning rate of 0.1, a maximum n-gram length of 3, and a maximum number of word occurrences of 3. The checkpoint we used for fine-tuning is crawl-300d-2M-subword (Mikolov et al., 2018), which was pretrained on 600B tokens of the Common Crawl corpus5.

Footnote 5: [https://commoncrawl.org/](https://commoncrawl.org/)

**Training Datasets** To train the fastText model for retrieving relevant documents, we use the following 10 cultural-related datasets: SocialChem101 (Forbes et al., 2020), NormBank Ziems et al. (2023a) CultureBank Shi et al. (2024), CultureAtlas Fung et al. (2024), MAPS Liu et al. (2023), CALI Culturally Aware Natural Language Inference Huang and Yang (2023), CANDLE Nguyen et al. (2023), NormLens Han et al. (2023) , Social IQA Sap et al. (2019), ETHICS Hendrycks et al. (2021).

## Appendix B Instruction Generation

### GPT-4o Prompt for Instruction Generation

Figure 5 shows the prompt used for generating instruction data using GPT-4o. The generated instructions are used to continue fine-tuning the Bonito model.

### Generated Examples

We illustrate several examples of CultureIntstruct in Table 12. There are examples provided for 6 NLP tasks, namely Summarization, Extractive Question Answering, Multiple-choice Question Answering, Textual Entailment, Topic Classification, and Sentence Completion. The examples for the additional reasoning task are provided in Appendix B.3.

### Chain-of-Thought Reasoning Task Generation

Figure 6 describes the prompt for generating Chain-of-Thought Reasoning instructions using GPT-4o. Table 13 shows some example instructions generated by our tuned Bonito model.

### Instruction Quality Assessment

To assess the quality of the instructions generated by the tuned Bonito, we prompt Llama-3.1

Figure 5: Prompt template for generation instructions using GPT-4o.

 

Instruct 70B with the prompt template shown in Figure 9. For each instruction generated by Bonito, we prompt Llama-3.1-Instruct 70B 5 times and take the majority voting as the quality label.

## Appendix C Additional Data Analysis

### Assessing LLM Judgement on Instruction Quality

To assess the reliability of using Llama-3.1-Instruct 70B for judging instruction quality (Section 2.2 and Section 3.2), we perform a manual evaluation of the judgments of the model. Firstly, we hired 5 graduate students for this task, so the annotation quality is better than standard crowdsourcing workers. The pay rate for each annotator is 20 USD per hour. We then perform stratified sampling with the generated instructions based on the task type, and 234 samples are selected for labeling. We then prompt Llama-3.1-Instruct 70B for its quality labels (instruction prompt template shown in Figure 9) and have each human annotator label the samples, following the identical instruction given to the model.

After completing the annotation process, we first collect the majority human selection for each sample. As the number of human annotators is odd, it is unnecessary to check for cases where there may be an equal number of selections - no majority selection. Then the model's reliability can be assessed by computing the accuracy of the model predictions w.r.t. human majority selections. **Within 234 samples, Llama-3.1-Instruct agrees with humans on 166 samples, reaching 70.93% accuracy**. We further perform qualitative analysis on cases where there is disagreement between the model and human annotators.

Qualitative AnalysisTable 14 shows some examples for analyzing human judgment and LLM judgment. In the first example, the answer contains information that is not provided in the context (highlighted in Red), hence the quality score should be "Poor", as labeled by human annotators. Example 3 follows the same pattern, where the LLM judge cannot detect the hallucinated content in the instructions. In the second example, where the task is to summarize the main subject of the provided text, the generated answer is missing several sections in the text (Geography, Transportation, Economy, Education, Sport), as well as containing some hallucinated sections (History, Culture). Human annotators rated this example as "Poor", while the LLM Judge could not detect the problem. However, in the last two examples, the judgments of LLM are better than human evaluators. Based on the explanation made by the model, it is clear that in this case, the LLM Judge successfully detected the problems in the generated instructions that human annotators cannot. This analysis highlights the strengths and weaknesses of LLM judgment, demonstrating that while it can outperform human evaluators in some cases, it still struggles to consistently identify hallucinations and missing content.

### Assessing Instruction Quality & Culture Relevance

In this section, we analyze to what extend the instruction quality correlates with cultural knowledge relevance. Figure 7 presents a heatmap illustrating the joint distribution of Instruction Quality and

Figure 6: Prompt template for generation Chain-of-Thought Reasoning instructions using GPT-4o.

Figure 7: Instruction Quality and Cultural-Relevance distributions.

 

Cultural-Relevance ratings, expressed as percentages. To judge the instruction quality and cultural relevance, we prompt Llama-3.1-Instruct 70B to obtain the quality labels ( Figure 9) and the culture relevance labels (Figure 10. From the figure, a substantial portion of CultureInstruct is concentrated in the higher rating categories ("good" and "excellent"). This distribution suggests a strong alignment between high-quality instruction and high cultural relevance within the dataset. Overall, most of the content of CultureInstruct is rated highly in both Instruction Quality and Culture Relevance, emphasizing our data construction pipeline on ensuring both pedagogical effectiveness and cultural sensitivity.

### Dataset Coverage

Figure 8 illustrates the raw scatter plot of t-SNE projection results. This plot aligns closely with Figure 4 in Section 3.2, where CultureInstruct still shows large coverage over other cultural training datasets.

## Appendix D Additional Experiments

### Evaluation on Standard LLMs Benchmarks

The following benchmarks are chosen for evaluation:

* **HellaSwag**Zellers et al. (2019) HellaSwag is a benchmark designed to evaluate a model's ability to complete scenarios in a commonsense, plausible manner. It presents a context followed by multiple-choice options, and the model must choose the most likely continuation. HellaSwag is particularly difficult because the incorrect choices are often highly plausible, making it a strong test of a model's commonsense reasoning capabilities.
* **CommonsenseQA**Talmor et al. (2019) CommonsenseQA is a multiple-choice benchmark designed to test a model's understanding of everyday commonsense knowledge. It presents questions that require reasoning based on basic facts about the world, which are often intuitive to humans but challenging for AI. The questions are crafted from commonsense knowledge sources and are accompanied by five answer choices, only one of which is correct. The benchmark tests the model's ability to reason beyond factual recall, requiring an understanding of how objects, events, and concepts relate in everyday scenarios.
* **GPQA**Rein et al. (2024) GPQA is a challenging dataset of multiple-choice questions written by domain experts in biology, physics, and chemistry. The questions are high-quality and extremely difficult, and the dataset is split into three sets: **Diamond**, **Extended**, and **Main set**. In our experiments, we use the **Main set** of GPQA for evaluation.

We show the evaluation results of Llama-3.1-Instruct and Qwen2-Instruct on standard LLMs benchmark in Table 9. Overall, the performance of LLMs decreases consistently after fine-tuning with CultureInstruct. We acknowledge that there exists the catastrophic forgetting problem of these models, as well as there is a domain mismatch between CultureInstruct and these benchmarks, hence the performance is lower.

We further tried to fine-tune Llama-3.1-Instruct 8B and Qwen2-Instruct 7B with a combined dataset in an attempt to retain some performance on standard benchmarks. Specifically, we combine CultureInstruct with a synthesized dataset - MagpieXu et al. (2024) and fine-tune the models. For Llama-3.1-Instruct 8B, we use the Magpie-Pro-300K-filtered subset, while it is Magpie-Qwen2-Pro-300K-Filtered subset for Qwen2-Instruct 7B. These sets are chosen so that each model retains some memory of previous fine-tuning. Results of this experiment are shown in Table 10. Overall, fine-tuning with the combined dataset (CultureInstruct + Magpie) helps to retain some of the performance

Figure 8: t-SNE scatter plot of CultureInstruct and other culture-related dataset, namely CANDLE, CultureBank, World Value Survey.

 

on standard LLM benchmarks. However, the performance of models on the cultural benchmarks (CANDLE and CulturalBench) is not as good as fine-tuning with only CultureInstruct. This is an expected open research problem of LLMs - catastrophic forgetting - and we leave this problem for future works.

\begin{table}
\begin{tabular}{l c c|c c c} \hline \multirow{2}{*}{Model} & \multicolumn{3}{c|}{Cultural Benchmarks (Accuracy \(\uparrow\))} & \multicolumn{3}{c}{Standard Benchmarks (Accuracy \(\uparrow\))} \\ \cline{2-6}  & CANDLE & CuluralBench & HellaSway (0-shot) & CommonsenseQA & GPQA \\ \hline Llama-3.1-Instruct 8B & 72.20 & 48.81 & 75.31 & 76.00 & 29.91 \\ + CultureInstruct & 79.80 & 59.52 & 74.06 & 74.61 & 27.90 \\ + CultureInstruct + Mapie & 74.50 & 52.79 & 74.75 & 74.96 & 29.24 \\ \hline Qwenz-Instruct 7B & 77.80 & 47.62 & 79.25 & 80.02 & 34.15 \\ + CultureInstruct & 88.00 & 67.46 & 76.73 & 73.59 & 32.28 \\ + CultureInstruct + Magipe & 81.40 & 62.05 & 77.21 & 78.63 & 32.31 \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of smaller LLMs on Cultural and Standard Benchmarks. The Mague subset for Llama-3.1-Instruct is Mague-Pro-300K–Filtered, while it is Mague-Qwen2-Pro-300K–Filtered for QWen2-Instruct.

\begin{table}
\begin{tabular}{l l l l} \hline \hline Model & \multicolumn{3}{c}{Standard LLMs Benchmarks} \\ \cline{2-5}  & HellaSwag (0-shot) (Accuracy \(\uparrow\)) & CommonsenseQA (Accuracy \(\uparrow\)) & GPQA (main set) (Accuracy \(\uparrow\)) \\ \hline \multicolumn{5}{l}{**Parameter Size between 7B and 9B**} \\ \hline \multicolumn{5}{l}{Llama-3.1-Instruct 8B} & 75.31 & 76.00 & 29.91 \\ + CultureInstruct & 74.06 & 74.61 & 27.90 \\ \hline Qwenz2-Instruct 7B & 79.25 & 80.02 & 34.15 \\ + CultureInstruct & 76.73 & 73.59 & 32.28 \\ \hline \multicolumn{5}{l}{**Parameter Size between 70B and 72B**} \\ \hline \multicolumn{5}{l}{Llama-3.1-Instruct 70B} & 84.27 & 80.10 & 41.29 \\ + CultureInstruct & 83.97 & 73.05 & 39.96 \\ \hline Qwenz2-Instruct 72B & 86.16 & 88.04 & 39.06 \\ + CultureInstruct & 85.41 & 87.71 & 36.38 \\ \hline \end{tabular}
\end{table}
Table 9: Performance of LLMs on standard LLMs benchmarks that are not related to cultural knowledge.

 



#Instruction You need to rate the quality of the question and answer pair based on its clarity, specificity, and coherence.

The rating scale is as follows:

- very poor: The question and answer pair is unclear, vague, incoherent. It lacks essential information and context.
- poor: The question and answer pair is somewhat unclear or lacks important details. It requires significant clarification.
- average: The question and answer pair is moderately clear and specific. It may require some additional information for a complete understanding.
- good: The question and answer pair is clear, specific, and mostly well-formed. It provides sufficient context for understanding the user's intent.
- excellent: The question and answer pair is very clear, specific, and well-articulated. It contains all the necessary information and context for providing a comprehensive response.

## Question (input)

## Answer (output)

Response Format Given the question and answer pair, you first need to give a short assessement, highlighting the strengths and/or weaknesses of the question and answer pair. Then, you need to respose with a rating from very poor to excellent by filling in the placeholders in [...]: {[  "explanation": "[...]",  "quality": "[very poor/poor/average/good/excellent]" }]

Figure 10: Prompt template for assessing Cultural-Relevance.

Figure 9: Prompt template for assessing Instruction Quality.





\begin{table}
\begin{tabular}{l l} \hline
**Domain** & **Set of Keywords** \\ \hline General Cultural Knowledge & Culture, Cultural, Cultural heritage, Tradition, Custom, Folklore, Cultural practice, Ritual, Cultural belief \\ \hline Art & Arts, Theatre, Cinema, Drama, Painting, Sculpture, Photography, Visual arts, Performing arts, Fine arts, Applied arts \\ \hline Cuisine & Cuisine, Traditional food, Culinary art, Culinary, Recipe, Gastronomy, Food culture, Food, Ethnic food, Specialty \\ \hline Cultural Norms & Cultural norm, Social norm, Social practice, Accepted behavior, Traditional practice, Cultural expectation, Social expectation, Social custom, Community standard, Behavioral norm, Cultural standard, Normative behavior, Social rule, Cultural value, Traditional value, Social conduct, Etiquette, Behavioral expectation, Cultural tradition, Tradition, Societal norm, Norms and value, Cultural moral, Social protocol, Normative practice, Social convention, Cultural belief, Ritual practice, Customary behaviour, Cultural prescription, Social behavior pattern, Normative social behavior, Cultural code, Social tradition, Traditional social role, Community custom, Cultural conformity, Societal expectation, Cultural practices, Cultural traditions, Social norms, Cultural practices, Customs and rituals, Cultural values, Social behavior, Etiquette and manners, Cultural identity, Cultural diversity, Cultural heritage, Cultural beliefs, Cultural taboos, Social conventions \\ \hline Festivals & Festival, Celebration, National holiday, Public holiday, Annual event, Ceremony, Ceremonies, National event, Cultural festival, Religious festival, Traditional festival, National holidays, International festival, Harvest festival, Music festival, Film festival, Arts and crafts festival, Food festival, Seasonal festival, Historical festival, Folk festival, Community festival, Festival vitual, Festival tradition, Festival celebration, Festival activities, Festival heritage \\ \hline History & History, Historical site, Monument, Museum, Archaeology, Ancestry, Genealogy, Historical figure, Cultural landmark, Historical, Ancient history, Medieval history, Modern history, Contemporary history, Historical event, Historical period, Historical figure, Historical movements, Cultural history, Political history, Social history, Economic history, Military history, Diplomatic history, Oral history, Public history, Historiography \\ \hline Language & Language, Dialect, Linguistic, Idiom, Proverb, Storytelling, Oral tradition, Mythology, Legend, Folktale, Linguistic, Language acquisition, Phonetic, Phonology, Syntax, Semantic, Pragmatic, Morphology, Sociolinguistic, Psycholinguistic, Bilingualism, Multilingualism, Language family, Endangered language, Language preservation, Language evolution, Dialects, Language and culture, Language policy, Translation and interpretation \\ \hline Literature & Classic literature, Modern literature, Contemporary literature, Literary analysis, Literary criticism, Literary theory, Literary devices, Narrative structure, Fiction, Non-fiction, Poetry, Pose, Literacy \\ \hline Music & Pop culture, Media, Music history, Music genres, Classical music, Music and culture \\ \hline Religion & Religion, Spirituality, Belief, Philosophies, Philosophy, Worldview, Religious practice, Sacred text, Deities, Ritual, Religious beliefs, Religious practice, World religions, Monotheism, Polytheism, Spirituality, Sacred text, Religious ritual, Religious symbol, Worship, Prayer, Pilgrimage, Religious ethic, Theology, Comparative religion, Interfaith dialogue, Religious tradition, Religious institution, Clergy, Faith community \\ \hline Social Life & Social norms, Family structures, Social custons, Daily life, Community life, Social etiquette, Festive events, Traditional clothing, Fashion \\ \hline \end{tabular}
\end{table}
Table 11: List of keywords for each domain in the Keyword Filtering step



\begin{table}
\begin{tabular}{l l c} \hline Culture & Question & Answer \\ \hline \multicolumn{3}{c}{**Multiple-choice Question Answering**} \\ \hline China & Which of the following philosophical traditions is the I & The answer is D. \\  & Ching foundational to in East Asia? & \\  & A. Buddhism & \\  & B. Confucianism & \\  & C. Daosim & \\  & D. All of the above & \\ \hline Malaysia & What is a common greeting used in Malaysia during & C. \\  & Hari Raya Aidilfiti? & \\  & A. Selamat Hari Raya & \\  & B. Maaf Zahir Dan Battin & \\  & C. Salam Aidilitri & \\  & D. Eid Mubarak & \\ \hline \multicolumn{2}{c}{**Sentence Completion**} \\ \hline Egypt & In Egypt, children typically receive an Eidi, which is a small sum of money to be spent on activities throughout & Ed \\  & the & \\ \hline Saint & Saint Vincent and the Grenadines has a significant disas- & the United Kingdom. \\  & or the & \\  & Grendings & \\ \hline \multicolumn{4}{c}{**Extractive Question Answering**} \\ \hline Canada & Which flag was used as the national flag of Canada until & The national flag of Canada until 1965 was the Red Ensign, specifically the Canadian Red Ensign. It featured the British Union Jack in the upper left corner and the Canadian coat of arms on a red field. \\ \hline Fiji & What are the three confederacies into which all Fijian chiefs belong? & Kubuna, Burebasaga, and Tovata. \\ \hline \multicolumn{3}{c}{**Textual Entailment**} \\ \hline Indonesia & “The tudong is a style of headscar, worn as interpretation of the Islamic hijab, prevalent amongst many Muslim women in the Malay-speaking world...” From the context, does it imply that the tudong is a traditional headscar worn by women in Malaysia and Indonesia? True or False? & True \\ \hline Ireland & “The president holds office for seven years, and can be ..." - The president of Ireland has significant ceremonial duties but limited executive power. & True \\ \hline \multicolumn{3}{c}{**Topic Classification**} \\ \hline New & Given the following document: “The monarch of New Zealand, who is New Zealand’s head of state, is also the Supreme Governor of the Church of England. But the country.... ” What is the main topic of the document? & Christian politics in New Zealand. \\ \hline Japan & Given the following document: “Japanese poetry is poetry typical of Japan, or written, spoken...” What is the main subject of the document? & Japanese poetry \\ \hline \multicolumn{3}{c}{**Summarization**} \\ \hline America & Given the following document: “This list indexes notable Native American artists from Oklahoma...” What is the primary focus of the document? & The document is a list of notable Native American artists from Oklahoma, Oklahoma Territory, and Indian Territory, detailing their birth or residency in those areas, along with their various artistic disciplines. \\ \hline Vietnam & Given the following documents: “Chams, or Champa people, Urang Campa, Nguoi Chan, are an Austronesian ethnic group in Southeast Asia and...” What is the historical and cultural significance of the Cham people in Vietnam? & The Cham people, the largest indigenous Muslim group in Vietnam, have a rich history dating back to the 2nd century AD. They are known for their unique language and culture, which have significantly influenced the modern Vietnamese culture. \\ \hline \end{tabular}
\end{table}
Table 12: Some examples of each instruction task type in CultureInstruct



**Document**

It is necessary to remember that each and every culture has its own cultural and social practices. It is understood that in 2 ways, social practice takes place and that is investigation and activity. Social strategies in education include the dynamic use of contact between adults and children to promote growth in children. It is here that literacy activities are seen not only as significant foundations for the regeneration of a society, but also for lifelong learning. Literacy practices are a key are through which instructions are passed on from one generation to another through social identity and social language. This particular paper explores the practices, tenacities, as well as, the discourse of literacy practices that are family based and their connections with the African-American families.

A majority of the African-American families are low-income families, thus, their children are usually faced with a higher risk of poor academic achievement when compared to the children that come from white families which are usually either middle class or upper class and this is mainly in the literacy domain. It is acknowledged that there are lifelong consequences of failing to become literate, for example, intergenerational illiteracy, undercompployment, and unemployment. It is true to state that the family literacy practices are some of the factors that influence a child's interest in literate behavior. However, these literacy practices vary depending on the contextual, as well as, the cultural variables of each and every family. Some of the literacy practices that have been evidenced to have a positive impact on early literacy among children include parents acting as literacy role models and parent-child book reading and these practices are common among the middle and the upper-class families. There is a clear indication that the literacy skills among the African-American children who mainly come from low-income families have been compromised by the literacy practices of African American families.

Family literacy is a social-cultural practice that occurs between the members of a family and acts to promote literacy development, as well as, its use. It can be established that simple literacy practices such as having a large number of books in the house are a literacy practice that influences the literacy development of a child. It is important to note that this is not a common practice among the African American families as a majority of them even lack home libraries. A majority of African American families do not have such and this can be attributed to the fact that a majority of them are single parent families with the single parent having to work long hours so as to meet the financial needs of the family, thus, may lack the time to be close to the children. The other major factor is interengental illiteracy. In the early years, only a few African Americans achieved academic success due to a combination of factors, for example, racial discrimination, arrogance, and drugs and substance abuse. Therefore, the African American families lacked literacy role models that the children could look up to and this over a long period of time affected the literacy development of children in African American families.

The family literacy practices play a major role in influencing the literacy development of children. However, these family literacy practices are influenced by various contextual and cultural factors. Above are discussed some of the literacy practices that lack in a majority of the African American families and how they have over the time affected the literacy development of the children in those families.

\begin{table}
\begin{tabular}{|p{143.8pt}|p{170.7pt}|} \hline  & **Generation Results** \\ \hline
**Generated Question** & **Generated Answer** \\ \hline What are the consequences of failing to become literate? & Failing to become literate can lead to several lifelong consequences. **Firstly**, it contributes to intergenerational illiteracy, where children from non-literate families may also struggle with literacy. **Secondly**, it can result in undermipolment and unemployment, as individuals without literacy skills may face challenges in securing jobs. This can perpetuate the cycle of poverty within families. **Ultimately**, these consequences highlight the importance of literacy not just for individual success but also for societal development. \\ \hline How do family literacy practices influence children’s literacy development? & Family literacy practices significantly impact children’s literacy development through various mechanisms. **First**, family literacy practices include activities such as reading with children and providing access to books, which help foster early literacy skills. Research shows that in middle and upper-class families, these practices are common and positively correlate with children’s literacy development. **In contrast**, African-American families, particularly those from low-income backgrounds, often lack such practices due to contextual factors like financial constraints and single-parent households. This lack of literary practices, such as limited access to books and reduced parent-child reading time, adversely affects the literacy development of children. The absence of literacy role models and intergenerational illiteracy further exacerbates the issue. **Therefore**, family literacy practices are crucial in shaping a child’s literacy skills, and disparities in these practices can lead to differences in literacy outcomes among different socioeconomic groups. \\ \hline What are some of the challenges faced by African-American families in supporting their children in supporting their children’s literacy development? & African-American families, particularly those from low-income backgrounds, face several challenges in supporting their children’s literacy development. **One major challenge** is the lack of resources, such as a sufficient number of books and home libraries, which are crucial for fostering literacy. Many of these families also struggle with limited time for activities like parent-child book reading due to long working hours and single-parenting responsibilities. Additionally, historical factors such as interpereational illiteracy and lack of literacy role models further hinder literacy development. **These factors combined** result in a significant disparity in literacy outcomes between African-American children from low-income families and their peers from middle or upper-class families. \\ \hline \end{tabular}
\end{table}
Table 13: Chain-of-Thought Reasoning instructions generated by our tuned Bonito model. **Blue** highlights the beginning of each reasoning step generated by the model.





**Question & Answer**

Given the following context:

"The following outline is provided as an overview of and topical guide to Saint Kits and Nevis:

The Federation of Saint Kitts and Nevis, also known as Saint Christopher and Nevis, is a sovereign federal two-island nation located in the Leeward Islands in the Caribbean Sea. It is the smallest nation in the Americas, both are and population.

The capital city and headquarters of government for the federated state is on the larger island of Saint Kitts. The smaller state of Nevis lies about 2 miles (3 km) southeast of Saint Kitts, across a shallow channel called 'The Narrows".

Histocally, the British dependency of Anguitla was also a part of this union, which was then known collectively as Saint Stricheyn-Kasyin-Avgiliu.

Saint Katts and Nevis are geographically part of the Leeward Islands. To the north-northwest lie the islands of Sanit Eastatistics, Saint Barthelemoy, and Saint-Martin Starture. To the east and northeast are Antigua and Barthola, and the southeast is the small ununhabated Island of Redonda, and the island of Monsterrant, which currently has an active volcano (see Souffier Hills.)

Saint Kirts and Nevis were among the first latitudes in the Caribbean to be settled by Europeans. Saint Kitts was home to the first British and French colonies in the Caribbean.

Saint Kitts and Nevis in the smallest nation on Earth to ever host a World Cup event; it was one of the host venues of the 2007 Cricket World Cup.

[1] Q. "What is Saint Kitts and Nevis know from?"

A. "Saint Kitts and Nevis is a sovereign federal two-island nation in the Caribbean, known for being the smallest nation in the America, hosting a World Cup event, and its significant contributions to the music industry, particularly reggae."

\begin{tabular}{l l c} \hline \multicolumn{4}{l}{Given the following context:} \\ \cline{2-3}  & Geography. Marakis is situated at the northwestern foot of Mount Aragats within the Shirak Plain, at a height of 1.72 meters above sea level, at a 24 km road distance south of the provincial centre Gyumri... \\ Demographics. Maralk is home to many historical monuments. The archaeological site at the northeastern vicinity of the town is home to a cyclopuan fortress and a settlement, both dating back to the 2nd millennium BC.. \\ Transportation. Maranki is located on the M-1 Motorway on the road between Gymart and Yervan. The town is also connected with the nearby towns and villages through a network of regional roads. \\ Economy. Marinki was an active industrial town during the Soviet period. The currently-abandoned cotton-spinning factory of Maralka used have more than 200 employees at its peak during the Soviet days.. \\ Education. Maralki is home to map myr-school kindregartens, 2 public education schools, as well a vocational school which is a set-owned intermediate technical college, serving the town and its surrounding villages.. \\ Sport. Maralki has a sport school run by the municipality. The town is served by a municipal football stadium with a limited seating capacity of 1.000. Football is the most popular sport in Maralka.." \\ Q. "What is then an subject of the concert?" \\ A. "The history, culture, and demographics of Maralki, Armenia.* \\ \end{tabular}

Given the following context:

"Thomas Sarkara at the Marsists Internet Archive."

"But what was the significance of Thomas Smarkar during his presidency?"

"Thomas Szankara, known as the "African Che Gateway", was a visionary leader who fought corruption, promoted free education and healthcare, and worked towards economic independence for Burkita Faso. He was assassinated in 1987."

Govern the following context:

"In the beginning of the 20th century, Uruguay became the most politically and socially advanced state on the continent. The liberal Jose Balley O'Dorica (in power between 1903) and 1907, then between 1911 and 1915) was the main architect of this transformation; freedom of expression and the press was affirmed, as was that of suffering. A system of proportional representation was adopted to allow for the representation of minorities. This period also saw the abolition of the death penalty, a right against administrative corruption, and the introduction of socialtivism and women's suffrage. On the economic level, Baltic stated that...."

Q. "What are the main features of Uruguay political and socialtival transformation in the early 20th century?"

A. "Urugaw theme now ten polistically and socially advanced state on thecontinent under the leadership of José Bailey Ordoline, who implemented significant reforms including freedom of expression and sufflarge, undistortion of the draft penalty, and nationalization of various sectors." Good Average The answer is clear and provides a summary of the jurisdictions that have enlighted their own children, and thus exhibit higher utility. In those notes not provide context on the constitutional and economic aspects of the transformation. The answer could be improved by...

\begin{table}
\begin{tabular}{p{168.0pt} p{168.0pt} p{168.0pt} p{168.0pt}} \hline \hline
**Question \& Answer** & **Human** & **LLM** & **LLM Explanation** \\ \hline Given the following context:

“The fighting outline is provided as an overview of and topical guide to Saint Kitts and Nevis:

The Federation of Saint Kitts and Nevis, also known as Saint Christopher and Nevis, is a sovereign federal two-island nation located in the Leeward Islands in the Caribbean Sea. It is the smallest nation in the Americans, in both area and population.

The capital city and headquarters of government for the federated state is on the larger island of Saint Kitts. The smaller state of Nevis lies about 2 miles (3 km) southeast of Saint Kitts, across a shallow channel called 'The Navrows’.

Historically, the British dependency of Anguitla was also a part of this union, which was then known collectively as Saint Stroehro-Neyingault.

Sait Kitts and Nevis are geographically part of the Leeward Islands. To the north-northwest lie the islands of Sanit Eastatistics, Sabia Samt Barthelemoy, and Saint Martin Starture. To the east and northeast are Antigua and Barthola, and the southeast is the small unhumbated Island of Redonda, and the island of Montersert, which currently has an active volcano (see Souffier Hills.)

Siant Kitts and Nevis were amongst the first latitudes in the Caribbean to be settled by Europeans. Saint Kitts was home to the first British and French colonies in the Caribbean.

Sait Kitts and Nevis in the smallest nation on Earth to ever host a World Cup event; it was one of the host venues of the 2007 cricket World Cup. “

Q. ”What is Saint Kitts and Nevis knows how far?”

A. "Saint Kitts and Nevis is a sovereign federal two-island nation in the Caribbean, known for being the smallest nation in the America, hosting a World Cup event, and its significant contributions to the music industry, particularly reggae."

Poor Excellent the main subject of the proviased context, and the answer succinctly identities it. Both question and answer are clear, concise, and specific to the task at hand.

\begin{tabular}{c c c} \hline \hline
**Question \& Answer** & **Human** & **LLM** & **LLM Explanation** \\ \hline Given the following context:

“The fighting outline is provided as an overview of and topical guide to Saint Kitts and Nevis:

The Federation of Saint Kitts and Nevis, also known as Saint Christopher and Nevis, is a sovereign federal two-island nation located in the Leeward Islands in the Caribbean Sea. It is the smallest nation in the America, in both area and population.

The capital city and headquarters of government for the federated state is on the larger island of Saint Kitts. The smaller state of Nevis lies about 2 miles (3 km) southeast of Saint Kitts, across a shallow channel called 'The Narrows’.

Historically, the British dependency of Anguitla was also a part of this union, which was then known collectively as Saint Stroehro-Neyingault.

Sait Kitts and Nevis are geographically part of the Leeward Islands. To the north-northwest lie the islands of Sanit Eastatistics, Sabia Samt Barthelemoy, and Saint Martin Starture. To the east and northeast are Antigua and Barthola, and the southeast is the small unhumbated Island of Redonda, and the island of Monsterrant, which currently has an active volcano (see Souffier Hills.)

Stanic Kitts and Nevis were among the first latitudes in the Caribbean to be settled by Europeans. Saint Kitts was home to the first British and French colonies in the Caribbean.

Sait Kitts and Nevis in the smallest nation on Earth to ever host a World Cup event; it was one of the host venues of 2007 cricket World Cup. “

Q. ”What is Saint Kitts and Nevis known for?”

A. "Saint Kitts and Nevis is a sovereign federal two-island nation in the Caribbean, known for being the smallest nation in the America, hosting a World Cup event, and its significant contributions to the music industry, particularly reggae."

\begin{tabular}{c c c} \hline \hline
**Question \& Answer** & **Human** & **LLM** & **LLM Explanation** \\ \hline Given the following context:

“The fighting outline is provided as an overview of and topical guide to Saint Kitts and Nevis:

The Federation of Saint Kitts and Nevis, also known as Saint Christopher and Nevis, is a sovereign federal two-island nation located in the Leeward Islands in the Caribbean Sea. It is the smallest nation in the America, in both area and population.

The capital city and headquarters of government for the federated state is on the larger island of Saint Kitts. The smaller state of Nevis lies about 2 miles (3 km) southeast of Saint Kitts, across a shallow channel called 'The Narrows’.

Historically, the British dependency of Anguitla was also a part of this union, which was then known collectively as Saint Stroehro-Neyingault.

Sait Kitts and Nevis are geographically part of the Leeward Islands. To the north-northwest lie the islands of Sanit Eastatistics, Sabia Samt Barthelemoy, and Saint Martin Starture. To the east and northeast are Antigua and Barthola, and the southeast is the small unhumbated Island of Redonda, and the island of Monsterrant, which currently has an active volcano (see Souffier Hills.)

Sanit Kitts and Nevis were among the first latitudes in the Caribbean to be settled by Europeans. Saint Kitts was home to the first British and French colonies in the Caribbean.

Sait Kitts and Nevis in the smallest nation on Earth to ever host a World Cup event; it was one of the host venues of 2007 cricket World Cup. “

Q. ”What is Saint Kitts and Nevis known for?”

A. "Saint Kitts and Nevis is a sovereign federal two-island nation in the Caribbean, known for being the smallest nation in the America, hosting a World Cup event, and its significant contributions to the music industry, particularly reggae."

\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Proceedings** & **Commission** & **Locality** \\ \hline Poor & Good & The question asks for the main subject of the project on the market, and the answer succinctly identities it. Both question and answer are clear, concise, and specific to the task at hand. \\ \hline Poor & Excellent & The question asks for the main subject of the project on the market, and the answer succinctly identities it. Both question and answer are clear, concise, and specific to the task at hand. \\ \hline Poor & Excellent & The question asks for the main subject of the project on the market, and the answer succinctly identities it. Both question and answer are clear, concise, and specific to the task at hand. \\ \hline Poor & Excellent & The question asks for the main subject of the project on the market to be covered, and the answer succinctly identities it. Both questions and answer are clear, concise, and specific to the task at hand. \\ \hline Poor & Excellent & The question asks for the main subject of the project on the market to be covered, and the answer succinctly identities it. Both question and answer are clear, concise, and specific to the task at hand. \\ \hline Poor & Excellent & The question asks for the main subject of the project on the market to be covered, and the answers sectionbles it. Both question and answer are clear, concise, and specific to the task at hand. \\ \hline Poor & Excellent & The question asks for the main subject of the project on the market to be covered, which is clear and specific. \\ \hline Poor & Excellent & The answer provides a good summary of his achievements and... \\ \hline Poor & Very Poor & Good & Average \\ Poor & Excellent & The answer is clear and specific, but it omits \\ \hline Poor & Excellent & Another \\ Poor & Excellent & The answer is clear and specific, but it omits \\ \hline Poor & Excellent & Another \\ Poor & Excellent