You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Despite the comprehensiveness of SuperNI in collecting existing NLP tasks, most of these NLP tasks were proposed for research purposes and skewed toward classification. To better access the practical value of instruction-following models, a subset of the authors curate a new set of instructions motivated by user-oriented applications.We first brainstorm different domains where large LMs may be useful (e.g., email writing, social media, productivity tools, entertainment, programming), then craft instructions related to each domain along with an input-output instance (again, input is optional). We aim to diversify the styles and formats of these tasks (e.g., instructions may be long or short; input/output may take the form of bullet points, tables, codes, equations, etc.). In total, we create 252 instructions with 1 instance per instruction. We believe it can serve as a testbed for evaluating how instruction-based models handle diverse and unfamiliar instructions. Table 4 presents a small portion of the 252 tasks. The whole test set will be available upon request. A series of works have found evidence that vanilla language models can be effective at following general language instructions if tuned with annotated “instructional” data – datasets containing language instructional commands and their desired outcome based on human judgement Weller et al. (2020); Mishra et al. (2022); Wang et al. (2022); Wei et al. (2022); Sanh et al. (2022); Ouyang et al. (2022); Parmar et al. (2022); Scialom et al. (2022); Chung et al. (2022); Luo et al. (2022); Puri et al. (2022); Yin et al. (2022); Chakrabarty et al. (2022); Lin et al. (2022); Gupta et al. (2022); Muennighoff et al. (2022).Additionally, they show a direct correlation between the size and diversity of the “instructional” data and the generalizability of resulting models to unseen tasks.Since these developments depend on human annotated “instructional” data, this poses a bottleneck for progress toward more generalizable models(for example see Fig. 5a in Wang et al., 2022).Our work aims to tackle this bottleneck by reducing the dependence on human annotators.

Question: What is the similarities and differences between the NaturalInstructions dataset and the SuperNaturalInstructions dataset?
This paper only mentions and discusses the SuperNaturalInstructions (or SuperNI) dataset, and does not explicitly discuss the NaturalInstructions dataset. However, based on the naming, it is possible that both the Supe- NaturalInstructiuons and NaturalInstructions datasets are datasets that contain annotated instructional data to help large language models (LLMs) perform a wider range of specialized tasks.