GerMedIQ: A Resource for Simulated and Synthesized Anamnesis Interview Responses in German (2025.acl-srw)
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Justin Hofenbitzer, Sebastian Schöning, Belle Sebastian, Jacqueline Lammert, Luise Modersohn, Martin Boeker, Diego Frassinelli
| Challenge: | Text corpora in non-English clinical contexts is scarce due to privacy restrictions and restricted access to secure environments. |
| Approach: | They propose to use Large Language Models to generate synthetic data using a German medical interview questions corpus. |
| Outcome: | The proposed dataset generates comparable responses to human-generated questions. |
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| Challenge: | Large language models (LLMs) have great potential for synthetic data generation. |
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A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
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