Built on

Cardoso, A., Veale, T., Wiggins, G.A.: Converging on the divergent: The history (and future) of the international joint workshops in computational creativity. AI magazine 30
2009
Earlier work this paper cites.
Kang, H.: The fruit of my woman. trans. D. Smith (London: Granta, 2000), 2–24 (2016)
2016
Earlier work this paper cites.
Fosse, J.: Scenes from a Childhood. Fitzcarraldo Editions, London (2018)
2018
Earlier work this paper cites.
Shanahan, M., Clarke, C.: Evaluating large language model creativity from a literary perspective. arXiv preprint arXiv:2312.03746 (2023)
Original
2023
Earlier work this paper cites.
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A.M., Hauth, A., et al.: Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)
Original
2023
Earlier work this paper cites.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)
Original
2023
Earlier work this paper cites.
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., McHardy, R.: Challenges and applications of large language models. ArXiv abs/2307.10169
Original
2023
Earlier work this paper cites.
Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D., Shi, S., Tu, Z.: Document-level machine translation with large language models. ArXiv abs/2304.02210
Original
2023
Earlier work this paper cites.
Chakrabarty, T., Laban, P., Agarwal, D., Muresan, S., Wu, C.-S.: Art or artifice? large language models and the false promise of creativity. ArXiv abs/2309.14556
Original
2023
Earlier work this paper cites.
Dai, S., Xiong, A., Ku, L.: Llm-in-the-loop: Leveraging large language model for thematic analysis. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pp. 9993–10001 (2023). https://doi.org/10.18653/V1/2023.FINDINGS-EMNLP.669
2023
Earlier work this paper cites.
Drápal, J., Westermann, H., Savelka, J.: Using large language models to support thematic analysis in empirical legal studies. In: Sileno, G., Spanakis, J., Dijck, G. (eds.) Legal Knowledge and Information Systems - JURIX 2023: The Thirty-sixth Annual Conference, Maastricht, The Netherlands, 18-20 December 2023. Frontiers in Artificial Intelligence and Applications, vol. 379, pp. 197–206 (2023). https://doi.org/10.3233/FAIA230965
2023
Earlier work this paper cites.
Choi, K.: Computational thematic analysis of poetry via bimodal large language models. Proceedings of the Association for Information Science and Technology 60
2023
Earlier work this paper cites.
Paoli, S.D.: Can large language models emulate an inductive thematic analysis of semi-structured interviews? an exploration and provocation on the limits of the approach and the model. CoRR abs/2305.13014
Original
2023
Earlier work this paper cites.
Karpinska, M., Iyyer, M.: Large language models effectively leverage document-level context for literary translation, but critical errors persist. ArXiv abs/2304.03245
Original
2023
Earlier work this paper cites.

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Jiang, M., Joshi, M.: Cpopqa: Ranking cultural concept popularity by llms. ArXiv abs/2311.07897
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Cited alongside, same era.

Then

Michel, G., Epure, E.V., Hennequin, R., Cerisara, C.: A realistic evaluation of llms for quotation attribution in literary texts: A case study of llama3. arXiv preprint arXiv:2406.11380 (2024)
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2024
Closest in time.
Yu, L., Liu, Q., Xiong, D.: Lfed: A literary fiction evaluation dataset for large language models. arXiv preprint arXiv:2405.10166 (2024)
Original
2024
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Zhang, Y., Garg, A., Cao, Y., Lew, L., Ghorbani, B., Zhang, Z., Firat, O.: Binarized neural machine translation. Advances in Neural Information Processing Systems 36
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2024

Analyzing Nobel Prize Literature with Large Language Models

Yang, Zhenyuan, Liu, Zhengliang, Zhang, Jing et al.

Understand

This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis.

The outputs of these models are compared directly to those produced by graduate-level human participants.
By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' by Han Kang, the 2024 laureate, and 'Friendship' by Jon Fosse, the 2023 laureate, the research explores the extent to which AI can engage with complex literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development.
Given the Nobel Prize's prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation.

Built on

Cardoso, A., Veale, T., Wiggins, G.A.: Converging on the divergent: The history (and future) of the international joint workshops in computational creativity. AI magazine 30
2009
Earlier work this paper cites.
Kang, H.: The fruit of my woman. trans. D. Smith (London: Granta, 2000), 2–24 (2016)
2016
Earlier work this paper cites.
Fosse, J.: Scenes from a Childhood. Fitzcarraldo Editions, London (2018)
2018
Earlier work this paper cites.
Shanahan, M., Clarke, C.: Evaluating large language model creativity from a literary perspective. arXiv preprint arXiv:2312.03746 (2023)
Original
2023
Earlier work this paper cites.
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A.M., Hauth, A., et al.: Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)
Original
2023
Earlier work this paper cites.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)
Original
2023
Earlier work this paper cites.
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., McHardy, R.: Challenges and applications of large language models. ArXiv abs/2307.10169
Original
2023
Earlier work this paper cites.
Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D., Shi, S., Tu, Z.: Document-level machine translation with large language models. ArXiv abs/2304.02210
Original
2023
Earlier work this paper cites.
Chakrabarty, T., Laban, P., Agarwal, D., Muresan, S., Wu, C.-S.: Art or artifice? large language models and the false promise of creativity. ArXiv abs/2309.14556
Original
2023
Earlier work this paper cites.
Dai, S., Xiong, A., Ku, L.: Llm-in-the-loop: Leveraging large language model for thematic analysis. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pp. 9993–10001 (2023). https://doi.org/10.18653/V1/2023.FINDINGS-EMNLP.669
2023
Earlier work this paper cites.
Drápal, J., Westermann, H., Savelka, J.: Using large language models to support thematic analysis in empirical legal studies. In: Sileno, G., Spanakis, J., Dijck, G. (eds.) Legal Knowledge and Information Systems - JURIX 2023: The Thirty-sixth Annual Conference, Maastricht, The Netherlands, 18-20 December 2023. Frontiers in Artificial Intelligence and Applications, vol. 379, pp. 197–206 (2023). https://doi.org/10.3233/FAIA230965
2023
Earlier work this paper cites.
Choi, K.: Computational thematic analysis of poetry via bimodal large language models. Proceedings of the Association for Information Science and Technology 60
2023
Earlier work this paper cites.
Paoli, S.D.: Can large language models emulate an inductive thematic analysis of semi-structured interviews? an exploration and provocation on the limits of the approach and the model. CoRR abs/2305.13014
Original
2023
Earlier work this paper cites.
Karpinska, M., Iyyer, M.: Large language models effectively leverage document-level context for literary translation, but critical errors persist. ArXiv abs/2304.03245
Original
2023
Earlier work this paper cites.

Similar

Jiang, M., Joshi, M.: Cpopqa: Ranking cultural concept popularity by llms. ArXiv abs/2311.07897
Original
2023
Cited alongside, same era.
Masoud, R.I., Liu, Z., Ferianc, M., Treleaven, P.C., Rodrigues, M.: Cultural alignment in large language models: An explanatory analysis based on hofstede’s cultural dimensions. ArXiv abs/2309.12342
Original
2023
Cited alongside, same era.
Lin, Y.-T., Chen, Y.-N.: Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model (2023). https://arxiv.org/abs/2311.17487
Original
2023
Cited alongside, same era.
Kovac, G., Sawayama, M., Portelas, R., Colas, C., Dominey, P.F., Oudeyer, P.: Large language models as superpositions of cultural perspectives. CoRR abs/2307.07870
Original
2023
Cited alongside, same era.

Then

Michel, G., Epure, E.V., Hennequin, R., Cerisara, C.: A realistic evaluation of llms for quotation attribution in literary texts: A case study of llama3. arXiv preprint arXiv:2406.11380 (2024)
Original
2024
Closest in time.
Yu, L., Liu, Q., Xiong, D.: Lfed: A literary fiction evaluation dataset for large language models. arXiv preprint arXiv:2405.10166 (2024)
Original
2024
Closest in time.
Zhang, Y., Garg, A., Cao, Y., Lew, L., Ghorbani, B., Zhang, Z., Firat, O.: Binarized neural machine translation. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Li, J., Tang, T., Zhao, W.X., Nie, J.-Y., Wen, J.-R.: Pre-trained language models for text generation: A survey. ACM Computing Surveys 56
2024
Closest in time.
Paoli, S.D.: Thematic analysis with large language models: does it work with languages other than english? A targeted test in italian. CoRR abs/2404.08488

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Bill, D., Eriksson, T.: Fine-tuning a LLM using Reinforcement Learning from Human Feedback for a Therapy Chatbot Application. Dissertation (2023)

2023

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Liu, Z., Li, Y., Cao, Q., Chen, J., Yang, T., Wu, Z., Hale, J., Gibbs, J., Rasheed, K., Liu, N., et al.: Transformation vs tradition: Artificial general intelligence (agi) for arts and humanities. arXiv preprint arXiv:2310.19626 (2023)

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2024

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Yao, B., Jiang, M., Yang, D., Hu, J.: Benchmarking LLM-based Machine Translation on Cultural Awareness (2024). https://arxiv.org/abs/2305.14328

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