[155] Wang, H., Li, R., Jiang, H., Wang, Z., Tang, X., Bi, B., Cheng, M., Yin, B.,
Wang, Y., Zhao, T., et al.: Lighttoken: A task and model-agnostic lightweight
token embedding framework for pre-trained language models. In: Proceedings of
the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,
pp. 2302–2313 (2023)

[156] Zhang, M., Shen, C., Yang, Z., Ou, L., Yu, X., Zhuang, B., et al.: Pruning
meets low-rank parameter-eﬃcient ﬁne-tuning. arXiv preprint arXiv:2305.18403
(2023)

[157] Wu, X., Yao, Z., He, Y.: Zeroquant-fp: A leap forward in llms post-training
w4a8 quantization using ﬂoating-point formats. arXiv preprint arXiv:2307.09782
(2023)

[158] Chen, P., Yu, H.-F., Dhillon, I., Hsieh, C.-J.: Drone: Data-aware low-rank com-
pression for large nlp models. Advances in neural information processing systems
34, 29321–29334 (2021)
Beyond Efficiency