New papers tackle LLM pruning, quantization efficiency

Four arXiv papers propose methods to reduce LLM inference cost: low-rank importance pruning, depth-pruning distribution shift correction (SHIFT-LLM), Fisher-information-based mixed-precision quantization (FAMPWQ), and zero-search quantized LoRA (AQLoRA). A fifth evaluates quantization effects on Bangla.