The paper 'Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model' introduces Sorbet, a transformer-based spiking language model tailored for neuromorphic hardware compatibility. It leverages PTsoftmax and bit-shifting power normalization (BSPN) for energy-efficient operations, distillation, and quantization to maintain competitive performance.