Papers by Nikolas Gritsch
Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization (2024.naacl-long)
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Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting
| Challenge: | Large Language Models (LLMs) have reshaped natural language processing with impressive capabilities, but their ever-increasing size has raised concerns about their effective deployment and the need for LLM compression. |
| Approach: | This study introduces the Divergent Token Metrics (DTMs) that measure token divergences that allow deeper insights into the subtleties of model compression. |
| Outcome: | The proposed measures can identify outliers and improve performance in the sparseness of the LLMs. |
Nexus: Adaptive Upcycling to Efficiently Pretrain Mixture of Experts (2025.findings-emnlp)
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| Challenge: | Nevertheless, training from scratch on trillions of tokens remains expensive that most users can only finetune these models. |
| Approach: | They propose to reuse parameters of dense models for the MoE layers with a router that can integrate new experts into existing trained models without hurting performance on previous domains. |
| Outcome: | The proposed router can integrate new experts into existing trained models without hurting the performance on previous domains. |