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Sebastian Raschka Thank you for sharing this fascinating work! Our study (arxiv.org/abs/2405.18218), released a month prior to this work, already reveals the redundancy of attention layers. In our research, we applied iterative pruning to both attention and feed-forward layers, and our experiments

The redundancy of attention layers has been shown in our earlier LLM structural pruning work. As shown in the figure, you can prune nearly half of the attention layers with minimal performance drop.

Our paper has been accepted to ICLR 2026! See you in Singapore!

thanks for sharing!

🎉 Our paper “Algorithmic Fidelity of LLMs in Generating Synthetic German Public Opinions” is accepted at #ACL2025 main conference as an oral presentation! 🇩🇪🤖 We study how well LLMs simulate real survey responses using open-ended German data, showing the left-leaning bias.