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ieg-dhr · Dec 22, 2024 · 2539fa2 · 2539fa2
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@@ -209,15 +209,26 @@ <h2>Literature</h2>
                         Dobson, J.E. (2023). On reading and interpreting black box deep neural networks. <i>International Journal of Digital Humanities</i>, <strong>5</strong>, 431–449. 
                         <a href="https://doi.org/10.1007/s42803-023-00075-w" target="_blank">https://doi.org/10.1007/s42803-023-00075-w</a>
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                         Khurana, D., Koli, A., Khatter, K. <i>et al.</i> (2023). Natural language processing: state of the art, current trends and challenges. <i>Multimedia Tools and Applications</i>, <strong>82</strong>, 3713–3744. 
                         <a href="https://doi.org/10.1007/s11042-022-13428-4" target="_blank">https://doi.org/10.1007/s11042-022-13428-4</a>
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+                        König, M. (19. August 2024). ChatGPT und Co. in den Geschichtswissenschaften – Grundlagen, Prompts und Praxisbeispiele. <i>Digital Humanities am DHIP</i>. Abgerufen am 2. Dezember 2024 von 
+                        <a href="https://doi.org/10.58079/126eo" target="_blank">https://doi.org/10.58079/126eo</a>
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                         Navigli, R., Conia, S., & Ross, B. (2023). Biases in Large Language Models: Origins, Inventory, and Discussion. <i>Journal of Data and Information Quality</i>, <strong>15</strong>(2), Article 10, 21 pages. 
                         <a href="https://doi.org/10.1145/3597307" target="_blank">https://doi.org/10.1145/3597307</a>
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+                        Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024). A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. <i>arXiv:2402.07927</i>. 
+                        <a href="https://doi.org/10.48550/arXiv.2402.07927" target="_blank">https://doi.org/10.48550/arXiv.2402.07927</a>
+                    </li>
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