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Context: Annotating large-scale instruction data can be challenging for humans because it requires 1) creativity to come up with novel tasks and 2) expertise for writing the labeled instances for each task.In this section, we detail our process for Self-Instruct, which refers to the pipeline of generating tasks with a vanilla pretrained language model itself and then conducting instruction tuning with this generated data in order to align the language model to follow instructions better. This pipeline is depicted in Figure 1. Self-Instruct is based on a finding that large pretrained language models can be prompted to generate new and novel instructions when presented with some existing instructions in the context. This provides us with a way to grow the instruction data from a small set of seed human-written instructions.We propose to generate a diverse set of instructions in a bootstrapping fashion. We initiate the task pool with 175 tasks (1 instruction and 1 instance for each task) written by our authors. For every step, we sample 8 task instructions from this pool as in-context examples.Of the 8 instructions, 6 are from the human-written tasks,and 2 are from the model-generated tasks in previous steps to promote diversity.The prompting template is shown in Table 6. To encourage diversity, a new instruction is added to the task pool only when its ROUGE-L overlap with any existing instruction is less than 0.7.We also exclude instructions that contain some specific keywords (e.g., images, pictures, graphs) that usually can not be processed by language models. When generating new instances for each instruction, we filter out instances that are exactly the same or those with the same input but different outputs. 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. Self-Instruct depends on LMs, and it will inherit all the limitations that carry over with LMs.As recent studies have shown Razeghi et al. (2022); Kandpal et al. (2022), tail phenomena pose a serious challenge to the success of LMs. In other words, LMs’ largest gains correspond to the frequent uses of languages (head of the language use distribution), and there are minimal gains in the low-frequency contexts.Similarly, in the context of this work, it would not be surprising if the majority of the gains by Self-Instruct are skewed towardtasks or instructions that present more frequently in the pre-training corpus.As a consequence, the approach might show brittleness with respect to uncommon and creative instructions.

Question: If LMs have been found to be biased and limited in creativity, how would LMs be able to create instructions that cover a greater diversity of tasks compared to humans who might be able to imagine possible tasks?
LMs might be able to create instructions covering a greater diversity of tasks since they are trained on a large corpus of material that encompasses the work of many humans. Additionally, the authors' proposed approach, Self-Instruct, uses a bootstrapping phase where humans provide the first set of instructions, and a LM uses those as examples to generate more instructions. Approaches such as these, combining the efforts of a human and a language model, might be one way to ensure LMs create a wider array of tasks. However, the authors do acknowledge that LMs are prone to be biased towards commonly occurring sequences, at the cost of rarer sequences, meaning that this is an open research question.