HEAL restores FP32-level output reproducibility in 16-bit LLM inference using targeted INT16 quantization and algebraic compensation, cutting overhead by up to 7.1x versus full FP32 on the new MCR-Bench.
Automatic evaluation of healthcare llms beyond question-answering
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Demystifying Numerical Instability in LLM Inference: Achieving Reproducible Inference for Mission-Critical Tasks with HEAL
HEAL restores FP32-level output reproducibility in 16-bit LLM inference using targeted INT16 quantization and algebraic compensation, cutting overhead by up to 7.1x versus full FP32 on the new MCR-Bench.