Tootfinder

Training data generation for context-dependent rubric-based short answer grading
Pavel \v{S}indel\'a\v{r}, D\'avid Slivka, Christopher Bouma, Filip Pr\'a\v{s}il, Ond\v{r}ej Bojar
https://arxiv.org/abs/2603.28537 https://arxiv.org/pdf/2603.28537 https://arxiv.org/html/2603.28537
arXiv:2603.28537v1 Announce Type: new
Abstract: Every 4 years, the PISA test is administered by the OECD to test the knowledge of teenage students worldwide and allow for comparisons of educational systems. However, having to avoid language differences and annotator bias makes the grading of student answers challenging. For these reasons, it would be interesting to compare methods of automatic student answer grading. To train some of these methods, which require machine learning, or to compute parameters or select hyperparameters for those that do not, a large amount of domain-specific data is needed. In this work, we explore a small number of methods for creating a large-scale training dataset using only a relatively small confidential dataset as a reference, leveraging a set of very simple derived text formats to preserve confidentiality. Using these methods, we successfully created three surrogate datasets that are, at the very least, superficially more similar to the reference dataset than purely the result of prompt-based generation. Early experiments suggest one of these approaches might also lead to improved model training.
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Meine Motorik ist so im Eimer, oder auch: Hört mich, wie ich meinen Computer anschreie "Höre ich mal auf, mich zu verradieren?!"

Noch ein paar der zuletzt hier besonders häufig geteilten #News:
IT-Angriff betrifft IT der Beweisstückstelle der Polizei

An LLM is a machine for giving confident plausible answers to whatever question is thrown at it.
The methods it uses to do this mean that (for questions that are not too challenging) there is a good chance that the answer is correct as well as confident.
Confident is guaranteed, correct is not. Is that what you need?

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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/5]:
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https://arxiv.org/abs/2603.22015 https://mastoxiv.page/@arXiv_csCL_bot/116283633674519408
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Dasen Dai, Biao Wu, Meng Fang, Wenhao Wang
https://arxiv.org/abs/2603.22999 https://mastoxiv.page/@arXiv_csCL_bot/116289015432093128
- Continual Robot Skill and Task Learning via Dialogue
Weiwei Gu, Suresh Kondepudi, Anmol Gupta, Lixiao Huang, Nakul Gopalan
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Zara Siddique, Irtaza Khalid, Liam D. Turner, Luis Espinosa-Anke
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Fangzhou Li, Pagkratios Tagkopoulos, Ilias Tagkopoulos
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- Large Language Models for Computer-Aided Design: A Survey
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Liu, Kong, Dong, Yang, Li, Tang, Yuan, Niu, Zhang, Zhao, Lin, Huang, Wang
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IT-Angriff betrifft IT der Beweisstückstelle der Polizei
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