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Foundation models for topic modeling: a case study

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In summary, GPT-4 outperforms the other two LLMs throughout the entire process, primarily due to the limitations of the models’ scale. Meanwhile, Qwen-72B performs considerably better than Qwen-14B and achieves comparable results, albeit slightly inferior, to those of GPT-4. We summarized our experiments in Table 2.

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References

  1. Pham C M, Hoyle A, Sun S, Iyyer M. Topicgpt: a prompt-based topic modeling framework. 2023, arXiv preprint arXiv: 2311.01449

  2. Bai J, Bai S, Chu Y, et al. Qwen technical report. 2023, arXiv preprint arXiv: 2309.16609

  3. Li Z, Zhang X, Zhang Y, Long D, Xie P, Zhang M. Towards general text embeddings with multi-stage contrastive learning. 2023, arXiv preprint arXiv: 2308.03281

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Correspondence to Tong Wei.

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Zeng, H., Sun, JM., Li, CS. et al. Foundation models for topic modeling: a case study. Front. Comput. Sci. 19, 192325 (2025). https://doi.org/10.1007/s11704-024-40069-7

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  • DOI: https://doi.org/10.1007/s11704-024-40069-7