Papers by Fabian Küch
Stratified Selective Sampling for Instruction Tuning with Dedicated Scoring Strategy (2025.findings-emnlp)
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| Challenge: | Recent work shows that post-training datasets can be substantially downsampled without noticeably deteriorating performance. |
| Approach: | They propose a method that efficiently bins data into groups and scores difficulty using specialized models. |
| Outcome: | The proposed method can be efficient and universally applied to post-training datasets. |
From Understanding to Generation: An Efficient Shortcut for Evaluating Language Models (2025.emnlp-main)
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| Challenge: | Iterative evaluation of large language models during training can be time- and compute-intensive. |
| Approach: | They reformulate generative tasks into computationally cheaper NLU alternatives and test their performance correlation between them. |
| Outcome: | The proposed alternatives reduce evaluation time by 35x compared to NLU benchmarks. |