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English isn't always the best choice for generating high-quality code鈥攍anguage bias significantly impacts LLM performance across programming tasks.
AQLM outperforms full-precision baselines in code generation, while QuIP# falters under complex prompts, revealing critical trade-offs in quantization strategies.
Sampling-based GNN training suffers from surprising CPU overhead, but ZEROGNN eliminates this bottleneck by enabling fully GPU-resident execution, leading to significant speedups.
