Papers by Justin Martin
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. |
NLP Web Services for Resource-Scarce Languages (P18-4)
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| Challenge: | Existing text-based core technologies were ported to Java-based web services for 10 resource-scarce languages spoken in South Africa. |
| Approach: | They propose to port existing text-based core technologies to Java-based web services from various architectures for 10 resource-scarce languages spoken in South Africa. |
| Outcome: | The proposed technologies were developed over a period of eight years for 10 resource-scarce languages spoken in South Africa. |
Remember what you did so you know what to do next (2023.findings-emnlp)
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| Challenge: | Existing studies have shown large language models (LLMs) to be poor fit for a simulated robot to achieve 30 classes of goals. |
| Approach: | They use the 6B parameter GPT-J language model to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld. |
| Outcome: | The proposed model outperforms the state-of-the-art by a factor of 1.4 when training on as many prior steps as will fit, and the results are 2.3x better than the state of the-art. |