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cs.LG2026★ 1 cited
Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation
Andrew S. Cassidy, Guillaume Garreau, Jay Sivagnaname +4
We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain (for example, Hardware Description Lan…
cs.LG2025
SiLQ: Simple Large Language Model Quantization-Aware Training
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani +2
Large language models can be quantized to reduce inference time latency, model size, and energy consumption, thereby delivering a better user experience at lower cost. A challenge…