---
title: "LLMs as Cultural Archives — paper by Tonga et al. (2026)"
description: "The authors instead build a Cultural Commonsense Knowledge Graph (CCKG) in the ATOMIC style, using iterative prompting… 45 references."
authors: ["Junior Cedric Tonga", "Chen Cecilia Liu", "Iryna Gurevych", "Fajri Koto"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.295/"
doi: "10.18653/v1/2026.eacl-long.295"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tonga-2026-llms-as-cultural-archives-cultural/"
---
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# LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction

**Authors:** Junior Cedric Tonga, [Chen Cecilia Liu](https://md.research.elamur.ai/free/en/authors/chen-cecilia-liu/), [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) and [Fajri Koto](https://md.research.elamur.ai/free/en/authors/fajri-koto/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.295/) · [PDF](https://aclanthology.org/2026.eacl-long.295.pdf) · [DOI 10.18653/v1/2026.eacl-long.295](https://doi.org/10.18653/v1/2026.eacl-long.295) · [OpenAlex](https://openalex.org/W7140135491)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks whether the cultural knowledge that large language models absorb during pretraining can be turned into an explicit, structured resource. Existing cultural knowledge bases mostly store isolated facts and are usually in English. The authors instead build a Cultural Commonsense Knowledge Graph (CCKG) in the ATOMIC style, using iterative prompting of GPT-4o and Llama-3.3-70B-Instruct.

The method works in two stages:
- **Initial generation:** for each subtopic, country and language, the model produces if–then assertions linking actions.
- **Iterative expansion:** sequential links are re-prompted to add intermediate and follow-up steps, yielding multi-step chains of cultural practice.

Human annotators rated the graphs for five countries on cultural relevance, correctness and path coherence. Native-language extractions carried richer cultural detail, but English versions were more coherent and generally preferred, even for cultures that are not English-speaking. This points to uneven cultural encoding in current LLMs.

Supplying CCKG paths to smaller models improved cultural commonsense reasoning and story generation, with English chains giving the largest benefit. The work suggests that chain-structured knowledge is a usable basis for culturally grounded NLP.

## Key points
- Graph edges use five relation types: xNext, xEffect, xNeed, oNext and oEffect. Assertions are composed into paths of actions, such as an Indonesian breakfast sequence involving lontong, soto ayam and a warung.
- Downstream reasoning was evaluated on the IndoCulture and ArabCulture benchmarks. Story generation used Llama3.1-8B-IT, Qwen2.5-7B-IT and Gemma2-9B-IT across three countries.
- In English story generation, adding CCKG paths improved quality for all nine model–country pairs. Llama models gained about +1.4 in Cultural Relevance on average, while Fluency and Coherence improved less.
- GPT-4o used as a judge agreed with human raters much better on native-language stories than on English ones:

  | Metric | English stories | Native-language stories |
  |---|---|---|
  | Cultural relevance | ~0.4 | ~0.8 |
  | Fluency | −0.1 | 0.9 |
  | Coherence | 0.0 | 0.8 |

- Code is released on GitHub (JuniorTonga/Cultural_Commonsense_Knowledge_Graph). The authors present the extracted content as a research prototype rather than a formal dataset and caution about possible stereotypes.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Technische Universität Darmstadt](https://md.research.elamur.ai/free/en/universities/technische-universitat-darmstadt/) — University, Darmstadt, Germany · 109 works · 35,920 citations
- [Hessian Center for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/hessian-center-for-artificial-intelligence/) — Research institute, Darmstadt, Germany · 12 works · 529 citations

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## Cite

Cite as: Junior Cedric Tonga, Chen Cecilia Liu, Iryna Gurevych and Fajri Koto. 2026. LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tonga2026llms,
  title = {{LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction}},
  author = {Junior Cedric Tonga, Chen Cecilia Liu, Iryna Gurevych and Fajri Koto},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.295},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.295}
}
```

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37. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2A%2C+Varsha+Kishore%2A%2C+Felix+Wu%2A%2C+Kilian+Q%2E+Weinberger%2C+and+Yoav+Artzi%2E+2020%2E+Bertscore%3A+Evaluating+text+generation+with+bert%2E+In+8th+International+Conference+on+Learning+Representations%2C+ICL)
38. Kai Sun, Yifan Xu, Hanwen Zha, Yue Liu, and Xin Luna Dong. 2024. Head-to-tail: How knowledgeable are large language models (LLMs)? A.K.A. will LLMs replace knowledge graphs? In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human La… [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Yifan+Xu%2C+Hanwen+Zha%2C+Yue+Liu%2C+and+Xin+Luna+Dong%2E+2024%2E+Head%2Dto%2Dtail%3A+How+knowledgeable+are+large+language+models+%28LLMs%29%3F+A%2EK%2EA%2E+will+LLMs+replace+knowledge+graphs%3F+In+Proceedings+of+the+2024)
39. A We recruited two annotators per country (ten in total) and applied strict eligibility requirements to ensure cultural authenticity and linguistic proficiency. Annotators were required to meet the following criteria: • Native speaker of the specified local language and proficient in English (speaki… [find](https://scholar.google.com/scholar?q=A+We+recruited+two+annotators+per+country+%28ten+in+total%29+and+applied+strict+eligibility+requirements+to+ensure+cultural+authenticity+and+linguistic+proficiency%2E+Annotators+were+required+to+meet+the+fo)
40. Qingyu Tan, Hwee Tou Ng, and Lidong Bing. 2023. Towards benchmarking and improving the temporal reasoning capability of large language models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14820–14835, Toronto, Canada. Assoc… [find](https://scholar.google.com/scholar?q=Qingyu+Tan%2C+Hwee+Tou+Ng%2C+and+Lidong+Bing%2E+2023%2E+Towards+benchmarking+and+improving+the+temporal+reasoning+capability+of+large+language+models%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Associat)
41. Resided in the country for at least ten years. • Demonstrated deep familiarity with the country’s culture. [find](https://scholar.google.com/scholar?q=Resided+in+the+country+for+at+least+ten+years%2E+%E2%80%A2+Demonstrated+deep+familiarity+with+the+country%E2%80%99s+culture%2E)
42. Niket Tandon, Gerard de Melo, Fabian M. Suchanek, and Gerhard Weikum. 2014. Webchild: harvesting and organizing commonsense knowledge from the web. Proceedings of the 7th ACM international conference on Web search and data mining. [find](https://scholar.google.com/scholar?q=Niket+Tandon%2C+Gerard+de+Melo%2C+Fabian+M%2E+Suchanek%2C+and+Gerhard+Weikum%2E+2014%2E+Webchild%3A+harvesting+and+organizing+commonsense+knowledge+from+the+web%2E+Proceedings+of+the+7th+ACM+international+conference+)
43. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, and Thomas Mesnard et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint, abs/2408.00118. [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Morgane+Riviere%2C+Shreya+Pathak%2C+Pier+Giuseppe+Sessa%2C+Cassidy+Hardin%2C+Surya+Bhupatiraju%2C+L%C3%A9onard+Hussenot%2C+and+Thomas+Mesnard+et+al%2E+2024%2E+Gemma+2%3A+Improving+open+language+models+at+a+pract)
44. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, R…
45. Annotator Criteria • Both parents are also natives residing in the same country. • Minimum qualification of senior high school graduation (higher degrees preferred). [find](https://scholar.google.com/scholar?q=Annotator+Criteria+%E2%80%A2+Both+parents+are+also+natives+residing+in+the+same+country%2E+%E2%80%A2+Minimum+qualification+of+senior+high+school+graduation+%28higher+degrees+preferred%29%2E)

*Updated 1 November 2026*