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cs.LG2025
Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling
Tim Z. Xiao, Johannes Zenn, Zhen Liu +3
Large language models (LLMs) can often accurately describe probability distributions using natural language, yet they still struggle to generate faithful samples from them. This mi…
cs.LG2025
Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy Coding
Alexander Conzelmann, Robert Bamler
The ever-growing size of neural networks poses serious challenges on resource-constrained devices, such as embedded sensors. Compression algorithms that reduce their size can mitig…