Title: DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models. Abstract: Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, two pain points persist for this paradigm: (a) as the pre-trained models grow bigger (e.g., $175$B parameters for GPT-3), even the fine-tuning process can be time-consuming and computationally expensive; (b) the fine-tuned model has the same size as its starting point by default, which is neither sensible due to its more specialized functionality, nor practical since many fine-tuned models will be deployed in resource-constrained environments. To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. Our proposed framework, dubbed $\textbf{D}$ually $\textbf{S}$parsity-$\textbf{E}$mbedded $\textbf{E}$fficient Tuning (DSEE), aims to achieve two key objectives:  (i) $\textit{parameter efficient fine-tuning}$ -  by enforcing  sparsity-aware weight updates on top of the pre-trained weights; and (ii) $\textit{resource-efficient inference}$ - by encouraging a sparse weight structure towards the final fine-tuned model. We leverage sparsity in these two directions by exploiting both unstructured and structural sparse patterns in pre-trained language models via magnitude-based pruning and $\ell_1$ sparse regularization. Extensive experiments and in-depth investigations, with diverse network backbones (i.e., BERT, GPT-2, and DeBERTa) on dozens of datasets, consistently demonstrate highly impressive parameter-/training-/inference-efficiency, while maintaining competitive downstream transfer performance. For instance, our DSEE-BERT obtains about $35\%$ inference FLOPs savings with $<0.1\%$ trainable parameters and comparable performance to conventional fine-tuning.