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+---
+license: mit
+---
+# Summary
+`databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousnads of Databricks empoyees in several
+of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information 
+extraction, open QA, and summarization.
+
+[LEGAL]
+This dataset is licensed for commercial use ...
+
+# Dataset Overview
+Databricks employees were invited to create prompt / response pairs using Google Forms in each of eight different instruction categories, including the seven 
+outlined above as well as an open-ended free-form category.  Employees were instructed to avoid using information from any source on the web with 
+the exception of Wikipedia, and explicitly instructed to avoid using generative AI in formulating instructions or responses.  Examples of each behavior were 
+provided to motivate the types of questions and instructions appropriate to each category.
+
+Halfway through the data generation process, participants were given the option of answering questions posed by other employees. They were 
+asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly.  
+
+For questions in the XYZ categories employees were asked to provide a reference passage copied from Wikipedia.  This text may contain bracketed 
+citations which we recommend users remove for downstream applications.
+
+# Intended Uses
+While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, 
+this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the [Self-Instruct](https://arxiv.org/abs/2212.10560) paper. 
+For example, employee-generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of 
+instructions in each of the respective InstructGPT categories.  
+
+Likewise, both the instructions and responses present fertile ground for data augmentation.  A paraphrasing model might be used to restate each prompt or short responses, with the 
+resulting text associated to the respective ground-truth sample.  Such an approach might provide a form of regularization on the dataset that could allow for 
+more robust instruction-following behavior in models derived from these synthetic datasets.
+
+# Dataset Limitations
+[LEGAL]