Analiza ilościowa zestawów danych i schematów ich wykorzystania w modelach językowych
Adam Jachimczyk
Abstrakt
Cel/Teza
Rosnąca popularność modeli językowych implikuje wzrost zainteresowania zestawami danych wykorzystywanymi do ich szkolenia. Analiza wpisuje się w nurt badań poświęconych tym zbiorom, koncentrując się na częstotliwości ich wykorzystania oraz schematach użycia przez różne typy modeli językowych.
Koncepcja/Metody badań
Przeanalizowano 56 762 modele językowe z repozytorium Hugging Face. W ramach analizy zidentyfikowano zestawy danych oraz określono częstość ich wykorzystania przez modele językowe. Zbadano także stopień zróżnicowania wykorzystania danych przez autorów modeli oraz stopień wykorzystania zestawów w różnych kategoriach modeli językowych.
Wyniki i wnioski
Większość modeli była szkolona na podstawie tylko jednego zestawu danych. Zaobserwowano także, że w kategoriach z mniejszą liczbą modeli występuje większe zróżnicowanie wykorzystywanych do ich szkolenia zestawów.
Oryginalność/Wartość poznawcza
Analiza ujawnia pewne schematy dotyczące stopnia zróżnicowania wykorzystania zestawów danych w zależności od autora i kategorii modelu językowego.
Słowa kluczowe
model językowy, sztuczna inteligencja, zestaw danych, Hugging Face
Bibliografia
Arif, A. (2023, czerwiec 27). Introduction to Large Language Models (LLMs): An Overview of BERT, GPT, and Other Popular Models. John Snow Labs. https://www.johnsnowlabs.com/introduction-to-large-language-models-llms-an-overview-of-bert-gpt-and-other-popular-models/
arXiv.org e-Print archive. (b.d.). Pobrano 28 marzec 2025, z https://arxiv.org/
Askari, A., Aliannejadi, M., Kanoulas, E., & Verberne, S. (2023). Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts (No. arXiv:2305.02320). arXiv. https://doi.org/10.48550/arXiv.2305.02320
Auffarth, B. (2024). Generatywna sztuczna inteligencja z LangChain: Budowanie aplikacji AI opartych na LLM z użyciem Pythona, ChatGPT i innych modeli językowych. Helion.
Baradaran, R., Ghiasi, R., & Amirkhani, H. (2022). A Survey on Machine Reading Comprehension Systems. Natural Language Engineering, 28(6), 683–732. https://doi.org/10.1017/S1351324921000395
Common Crawl—Open Repository of Web Crawl Data. (b.d.). Pobrano 28 marzec 2025, z https://commoncrawl.org/
Data Provenance Initiative. (b.d.). Pobrano 3 kwiecień 2025, z https://www.dataprovenance.org/about
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021). Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (No. arXiv:2104.08758). arXiv. https://doi.org/10.48550/arXiv.2104.08758
DuoRC. (b.d.). Pobrano 20 marzec 2025, z https://duorc.github.io/
Dzendzik, D., Foster, J., & Vogel, C. (2021). English Machine Reading Comprehension Datasets: A Survey. W M.-F. Moens, X. Huang, L. Specia, & S. W. Yih (Red.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (s. 8784–8804). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.693
Eigenbrode, S., Barth, A., Fregly, C., & Watrak, A. (Red.). (2024). Generatywna sztuczna inteligencja na platformie AWS: Tworzenie multimodalnych aplikacji wnioskujących kontekstowo. Helion.
FacebookAI/roberta-base · Hugging Face. (2024, marzec 11). https://huggingface.co/FacebookAI/roberta-base
Feature Extraction with LLMs, Hugging Face and MinIO. (b.d.). Pobrano 7 kwiecień 2025, z https://www.e2enetworks.com/blog/feature-extraction-with-large-language-models-hugging-face-and-minio
Fka/awesome-chatgpt-prompts · Datasets at Hugging Face. (2024, kwiecień 22). https://huggingface.co/datasets/fka/awesome-chatgpt-prompts
GitHub · Build and ship software on a single, collaborative platform · GitHub. (b.d.). Pobrano 28 marzec 2025, z https://github.com/
Goetze, T. S., & Abramson, D. (2021). Bigger Isn’t Better: The Ethical and Scientific Vices of Extra-Large Datasets in Language Models. Companion Publication of the 13th ACM Web Science Conference 2021, 69–75. https://doi.org/10.1145/3462741.3466809
Google-research-datasets/natural-questions. (2025). [Dataset]. Google Research Datasets. https://github.com/google-research-datasets/natural-questions (Original work published 2019)
Guo, X., & Chen, Y. (2024). Generative AI for Synthetic Data Generation: Methods, Challenges and the Future (No. arXiv:2403.04190). arXiv. https://doi.org/10.48550/arXiv.2403.04190
Hämäläinen, P., Tavast, M., & Kunnari, A. (2023). Evaluating Large Language Models in Generating Synthetic HCI Research Data: A Case Study. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–19. https://doi.org/10.1145/3544548.3580688
HuggingFaceH4/stack-exchange-preferences · Datasets at Hugging Face. (b.d.). Pobrano 24 marzec 2025, z https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences
Jordon, J., Szpruch, L., Houssiau, F., Bottarelli, M., Cherubin, G., Maple, C., Cohen, S. N., & Weller, A. (2022). Synthetic Data—What, why and how? (No. arXiv:2205.03257). arXiv. https://doi.org/10.48550/arXiv.2205.03257
Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S., Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei, S., Suarez, P. O., … Adeyemi, M. (2022). Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10, 50–72. https://doi.org/10.1162/tacl_a_00447
Li, Z., Zhu, H., Lu, Z., & Yin, M. (2023). Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations (No. arXiv:2310.07849). arXiv. https://doi.org/10.48550/arXiv.2310.07849
Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2024). Datasets for Large Language Models: A Comprehensive Survey (No. arXiv:2402.18041). arXiv. https://doi.org/10.48550/arXiv.2402.18041
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach (No. arXiv:1907.11692). arXiv. https://doi.org/10.48550/arXiv.1907.11692
Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., Wu, X. (Alexis), Shippole, E., Bollacker, K., Wu, T., Villa, L., Pentland, S., & Hooker, S. (2024). A large-scale audit of dataset licensing and attribution in AI. Nature Machine Intelligence, 6(8), 975–987. https://doi.org/10.1038/s42256-024-00878-8
Longpre, S., Singh, N., Cherep, M., Tiwary, K., Materzynska, J., Brannon, W., Mahari, R., Obeng-Marnu, N., Dey, M., Hamdy, M., Saxena, N., Anis, A. M., Alghamdi, E. A., Chien, V. M., Yin, D., Qian, K., Li, Y., Liang, M., Dinh, A., … Kabbara, J. (2025). Bridging the Data Provenance Gap Across Text, Speech and Video (No. arXiv:2412.17847). arXiv. https://doi.org/10.48550/arXiv.2412.17847
Lurunchik/WikiHowNFQA · Datasets at Hugging Face. (b.d.). Pobrano 20 marzec 2025, z https://huggingface.co/datasets/Lurunchik/WikiHowNFQA
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023). FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. W H. Bouamor, J. Pino, & K. Bali (Red.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (s. 12076–12100). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.741
Models—Hugging Face. (2025, kwiecień 16). https://huggingface.co/models
Newest Questions. (b.d.). Stack Overflow. Pobrano 28 marzec 2025, z https://stackoverflow.com/questions
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., … Zoph, B. (2024). GPT-4 Technical Report (No. arXiv:2303.08774). arXiv. https://doi.org/10.48550/arXiv.2303.08774
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021). Data and its (dis)contents: A survey of dataset development and use in machine learning research. Patterns, 2(11). https://doi.org/10.1016/j.patter.2021.100336
PKU-Alignment/PKU-SafeRLHF · Datasets at Hugging Face. (2024, kwiecień 20). https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF
Rejeleene, R., Xu, X., & Talburt, J. (2024). Towards Trustable Language Models: Investigating Information Quality of Large Language Models (No. arXiv:2401.13086). arXiv. https://doi.org/10.48550/arXiv.2401.13086
Rogers, A., Gardner, M., & Augenstein, I. (2023). QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension. ACM Comput. Surv., 55(10), 197:1-197:45. https://doi.org/10.1145/3560260
Rossi, L., Harrison, K., & Shklovski, I. (2024). The Problems of LLM-generated Data in Social Science Research. Sociologica, 18(2), Article 2. https://doi.org/10.6092/issn.1971-8853/19576
Samsung/samsum · Datasets at Hugging Face. (2022, grudzień 23). https://huggingface.co/datasets/Samsung/samsum
Sun, T., Zhang, X., He, Z., Li, P., Cheng, Q., Liu, X., Yan, H., Shao, Y., Tang, Q., Zhang, S., Zhao, X., Chen, K., Zheng, Y., Zhou, Z., Li, R., Zhan, J., Zhou, Y., Li, L., Yang, X., … Qiu, X. (2024). MOSS: An Open Conversational Large Language Model. Machine Intelligence Research, 21(5), 888–905. https://doi.org/10.1007/s11633-024-1502-8
Tang, R., Han, X., Jiang, X., & Hu, X. (2023). Does Synthetic Data Generation of LLMs Help Clinical Text Mining? (No. arXiv:2303.04360). arXiv. https://doi.org/10.48550/arXiv.2303.04360
Tasksource/deberta-small-long-nli · Hugging Face. (2023, grudzień 21). https://huggingface.co/tasksource/deberta-small-long-nli
Tatsu-lab/alpaca · Datasets at Hugging Face. (2024). [Dataset]. https://huggingface.co/datasets/tatsu-lab/alpaca
The Stanford Question Answering Dataset. (b.d.). Pobrano 20 marzec 2025, z https://rajpurkar.github.io/SQuAD-explorer/
TriviaQA. (b.d.). Pobrano 20 marzec 2025, z https://nlp.cs.washington.edu/triviaqa/
Wei, J., Huang, D., Lu, Y., Zhou, D., & Le, Q. V. (2024). Simple synthetic data reduces sycophancy in large language models (No. arXiv:2308.03958). arXiv. https://doi.org/10.48550/arXiv.2308.03958
What is Feature Extraction? - Hugging Face. (2024, listopad 15). https://huggingface.co/tasks/feature-extraction
What is Fill-Mask? - Hugging Face. (2025, styczeń 15). https://huggingface.co/tasks/fill-mask
What is Question Answering? | IBM. (2025, styczeń 30). https://www.ibm.com/think/topics/question-answering
What is Sentence Similarity? - Hugging Face. (2025, marzec 6). https://huggingface.co/tasks/sentence-similarity
What is Table Question Answering? - Hugging Face. (2023, maj 3). https://huggingface.co/tasks/table-question-answering
What is Text Classification? - Hugging Face. (2023, grudzień 19). https://huggingface.co/tasks/text-classification
What is Text Generation? - Hugging Face. (2024, sierpień 27). https://huggingface.co/tasks/text-generation
What is Token Classification? - Hugging Face. (2024, październik 8). https://huggingface.co/tasks/token-classification
What is Zero-Shot Classification? - Hugging Face. (2023, wrzesień 5). https://huggingface.co/tasks/zero-shot-classification
Wikimedia Downloads. (b.d.). Pobrano 27 marzec 2025, z https://dumps.wikimedia.org/
Yu, X. V., Asai, A., Chatterjee, T., Hu, J., & Choi, E. (2022). Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources (No. arXiv:2211.15649). arXiv. https://doi.org/10.48550/arXiv.2211.15649
Zeng, C., Li, S., Li, Q., Hu, J., & Hu, J. (2020). A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets (No. arXiv:2006.11880). arXiv. https://doi.org/10.48550/arXiv.2006.11880
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., … Wen, J.-R. (2025). A Survey of Large Language Models (No. arXiv:2303.18223). arXiv. https://doi.org/10.48550/arXiv.2303.18223
arXiv.org e-Print archive. (b.d.). Pobrano 28 marzec 2025, z https://arxiv.org/
Askari, A., Aliannejadi, M., Kanoulas, E., & Verberne, S. (2023). Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts (No. arXiv:2305.02320). arXiv. https://doi.org/10.48550/arXiv.2305.02320
Auffarth, B. (2024). Generatywna sztuczna inteligencja z LangChain: Budowanie aplikacji AI opartych na LLM z użyciem Pythona, ChatGPT i innych modeli językowych. Helion.
Baradaran, R., Ghiasi, R., & Amirkhani, H. (2022). A Survey on Machine Reading Comprehension Systems. Natural Language Engineering, 28(6), 683–732. https://doi.org/10.1017/S1351324921000395
Common Crawl—Open Repository of Web Crawl Data. (b.d.). Pobrano 28 marzec 2025, z https://commoncrawl.org/
Data Provenance Initiative. (b.d.). Pobrano 3 kwiecień 2025, z https://www.dataprovenance.org/about
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021). Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (No. arXiv:2104.08758). arXiv. https://doi.org/10.48550/arXiv.2104.08758
DuoRC. (b.d.). Pobrano 20 marzec 2025, z https://duorc.github.io/
Dzendzik, D., Foster, J., & Vogel, C. (2021). English Machine Reading Comprehension Datasets: A Survey. W M.-F. Moens, X. Huang, L. Specia, & S. W. Yih (Red.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (s. 8784–8804). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.693
Eigenbrode, S., Barth, A., Fregly, C., & Watrak, A. (Red.). (2024). Generatywna sztuczna inteligencja na platformie AWS: Tworzenie multimodalnych aplikacji wnioskujących kontekstowo. Helion.
FacebookAI/roberta-base · Hugging Face. (2024, marzec 11). https://huggingface.co/FacebookAI/roberta-base
Feature Extraction with LLMs, Hugging Face and MinIO. (b.d.). Pobrano 7 kwiecień 2025, z https://www.e2enetworks.com/blog/feature-extraction-with-large-language-models-hugging-face-and-minio
Fka/awesome-chatgpt-prompts · Datasets at Hugging Face. (2024, kwiecień 22). https://huggingface.co/datasets/fka/awesome-chatgpt-prompts
GitHub · Build and ship software on a single, collaborative platform · GitHub. (b.d.). Pobrano 28 marzec 2025, z https://github.com/
Goetze, T. S., & Abramson, D. (2021). Bigger Isn’t Better: The Ethical and Scientific Vices of Extra-Large Datasets in Language Models. Companion Publication of the 13th ACM Web Science Conference 2021, 69–75. https://doi.org/10.1145/3462741.3466809
Google-research-datasets/natural-questions. (2025). [Dataset]. Google Research Datasets. https://github.com/google-research-datasets/natural-questions (Original work published 2019)
Guo, X., & Chen, Y. (2024). Generative AI for Synthetic Data Generation: Methods, Challenges and the Future (No. arXiv:2403.04190). arXiv. https://doi.org/10.48550/arXiv.2403.04190
Hämäläinen, P., Tavast, M., & Kunnari, A. (2023). Evaluating Large Language Models in Generating Synthetic HCI Research Data: A Case Study. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–19. https://doi.org/10.1145/3544548.3580688
HuggingFaceH4/stack-exchange-preferences · Datasets at Hugging Face. (b.d.). Pobrano 24 marzec 2025, z https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences
Jordon, J., Szpruch, L., Houssiau, F., Bottarelli, M., Cherubin, G., Maple, C., Cohen, S. N., & Weller, A. (2022). Synthetic Data—What, why and how? (No. arXiv:2205.03257). arXiv. https://doi.org/10.48550/arXiv.2205.03257
Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S., Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei, S., Suarez, P. O., … Adeyemi, M. (2022). Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10, 50–72. https://doi.org/10.1162/tacl_a_00447
Li, Z., Zhu, H., Lu, Z., & Yin, M. (2023). Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations (No. arXiv:2310.07849). arXiv. https://doi.org/10.48550/arXiv.2310.07849
Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2024). Datasets for Large Language Models: A Comprehensive Survey (No. arXiv:2402.18041). arXiv. https://doi.org/10.48550/arXiv.2402.18041
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach (No. arXiv:1907.11692). arXiv. https://doi.org/10.48550/arXiv.1907.11692
Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., Wu, X. (Alexis), Shippole, E., Bollacker, K., Wu, T., Villa, L., Pentland, S., & Hooker, S. (2024). A large-scale audit of dataset licensing and attribution in AI. Nature Machine Intelligence, 6(8), 975–987. https://doi.org/10.1038/s42256-024-00878-8
Longpre, S., Singh, N., Cherep, M., Tiwary, K., Materzynska, J., Brannon, W., Mahari, R., Obeng-Marnu, N., Dey, M., Hamdy, M., Saxena, N., Anis, A. M., Alghamdi, E. A., Chien, V. M., Yin, D., Qian, K., Li, Y., Liang, M., Dinh, A., … Kabbara, J. (2025). Bridging the Data Provenance Gap Across Text, Speech and Video (No. arXiv:2412.17847). arXiv. https://doi.org/10.48550/arXiv.2412.17847
Lurunchik/WikiHowNFQA · Datasets at Hugging Face. (b.d.). Pobrano 20 marzec 2025, z https://huggingface.co/datasets/Lurunchik/WikiHowNFQA
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023). FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. W H. Bouamor, J. Pino, & K. Bali (Red.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (s. 12076–12100). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.741
Models—Hugging Face. (2025, kwiecień 16). https://huggingface.co/models
Newest Questions. (b.d.). Stack Overflow. Pobrano 28 marzec 2025, z https://stackoverflow.com/questions
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., … Zoph, B. (2024). GPT-4 Technical Report (No. arXiv:2303.08774). arXiv. https://doi.org/10.48550/arXiv.2303.08774
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021). Data and its (dis)contents: A survey of dataset development and use in machine learning research. Patterns, 2(11). https://doi.org/10.1016/j.patter.2021.100336
PKU-Alignment/PKU-SafeRLHF · Datasets at Hugging Face. (2024, kwiecień 20). https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF
Rejeleene, R., Xu, X., & Talburt, J. (2024). Towards Trustable Language Models: Investigating Information Quality of Large Language Models (No. arXiv:2401.13086). arXiv. https://doi.org/10.48550/arXiv.2401.13086
Rogers, A., Gardner, M., & Augenstein, I. (2023). QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension. ACM Comput. Surv., 55(10), 197:1-197:45. https://doi.org/10.1145/3560260
Rossi, L., Harrison, K., & Shklovski, I. (2024). The Problems of LLM-generated Data in Social Science Research. Sociologica, 18(2), Article 2. https://doi.org/10.6092/issn.1971-8853/19576
Samsung/samsum · Datasets at Hugging Face. (2022, grudzień 23). https://huggingface.co/datasets/Samsung/samsum
Sun, T., Zhang, X., He, Z., Li, P., Cheng, Q., Liu, X., Yan, H., Shao, Y., Tang, Q., Zhang, S., Zhao, X., Chen, K., Zheng, Y., Zhou, Z., Li, R., Zhan, J., Zhou, Y., Li, L., Yang, X., … Qiu, X. (2024). MOSS: An Open Conversational Large Language Model. Machine Intelligence Research, 21(5), 888–905. https://doi.org/10.1007/s11633-024-1502-8
Tang, R., Han, X., Jiang, X., & Hu, X. (2023). Does Synthetic Data Generation of LLMs Help Clinical Text Mining? (No. arXiv:2303.04360). arXiv. https://doi.org/10.48550/arXiv.2303.04360
Tasksource/deberta-small-long-nli · Hugging Face. (2023, grudzień 21). https://huggingface.co/tasksource/deberta-small-long-nli
Tatsu-lab/alpaca · Datasets at Hugging Face. (2024). [Dataset]. https://huggingface.co/datasets/tatsu-lab/alpaca
The Stanford Question Answering Dataset. (b.d.). Pobrano 20 marzec 2025, z https://rajpurkar.github.io/SQuAD-explorer/
TriviaQA. (b.d.). Pobrano 20 marzec 2025, z https://nlp.cs.washington.edu/triviaqa/
Wei, J., Huang, D., Lu, Y., Zhou, D., & Le, Q. V. (2024). Simple synthetic data reduces sycophancy in large language models (No. arXiv:2308.03958). arXiv. https://doi.org/10.48550/arXiv.2308.03958
What is Feature Extraction? - Hugging Face. (2024, listopad 15). https://huggingface.co/tasks/feature-extraction
What is Fill-Mask? - Hugging Face. (2025, styczeń 15). https://huggingface.co/tasks/fill-mask
What is Question Answering? | IBM. (2025, styczeń 30). https://www.ibm.com/think/topics/question-answering
What is Sentence Similarity? - Hugging Face. (2025, marzec 6). https://huggingface.co/tasks/sentence-similarity
What is Table Question Answering? - Hugging Face. (2023, maj 3). https://huggingface.co/tasks/table-question-answering
What is Text Classification? - Hugging Face. (2023, grudzień 19). https://huggingface.co/tasks/text-classification
What is Text Generation? - Hugging Face. (2024, sierpień 27). https://huggingface.co/tasks/text-generation
What is Token Classification? - Hugging Face. (2024, październik 8). https://huggingface.co/tasks/token-classification
What is Zero-Shot Classification? - Hugging Face. (2023, wrzesień 5). https://huggingface.co/tasks/zero-shot-classification
Wikimedia Downloads. (b.d.). Pobrano 27 marzec 2025, z https://dumps.wikimedia.org/
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Adam Jachimczyk
a.jachimczyk@uw.edu.pl
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne – Bez utworów zależnych 4.0 Międzynarodowe.
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