| # Alpaca Instruction Following Dataset | |
| ## Motivation | |
| ### For what purpose was the dataset created? | |
| To enable more open-source research on instruction following large language models, we use generate 52K instruction-followng demonstrations using OpenAI's text-davinci-003 model. | |
| ### Who created the dataset | |
| - [Rohan Taori](https://www.rohantaori.com/) | |
| - [Ishaan Gulrajani](https://ishaan.io/) | |
| - [Tianyi Zhang](https://tiiiger.github.io/) | |
| - [Yann Dubois](https://yanndubs.github.io/) | |
| - [Xuechen Li](https://www.lxuechen.com/) | |
| - [Carlos Guestrin](https://guestrin.su.domains/) | |
| - [Percy Liang](https://cs.stanford.edu/~pliang/) | |
| - [Tatsunori B. Hashimoto](https://thashim.github.io/) | |
| ## Composition | |
| ### What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? | |
| The instruction following demonstrations are bootstrapped by following the [seed set](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl) released from the self-instruct project. | |
| Given that the dataset is generated, it is difficult to pinpoint who/what the instances represent. | |
| ### How many instances are there in total | |
| In total, there are 52,002 instances in the dataset. | |
| ### Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? | |
| not applicable. | |
| ### What data does each instance consist of? | |
| - `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique. | |
| - `input`: `str`, optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. | |
| - `output`: `str`, the answer to the instruction as generated by `text-davinci-003`. | |
| ### Is any information missing from individual instances? | |
| no. | |
| ### Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? | |
| not applicable. | |
| ### Is there a label or target associated with each instance? | |
| the finetuning target is the response generated by `text-davinci-003`. | |
| ### Are there recommended data splits (e.g., training, development/validation, testing)? | |
| The Alpaca models (both demo and the ones that will be released) are trained on all 52K data. | |
| There is no recommended data split for the dataset. | |
| ### Are there any errors, sources of noise, or redundancies in the dataset? | |
| All 52k instructions are unique. However, some generated instructions may not be sensible, i.e., there may not exist any good response to the instruction. | |
| ### Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? | |
| the dataset is self-contained. | |
| ### Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications)? | |
| no. | |
| ### Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? | |
| The generated may contain a few inappropriate responses. In our preliminary testing, we have not encountered any offensive responses. | |
| ## Collection process | |
| The [Github repository](https://github.com/tatsu-lab/stanford_alpaca) contains the code to generate the dataset. | |
| ## Uses | |
| ### Has the dataset been used for any tasks already? | |
| The dataset is used to train the Alpaca models that are both used for the demo and released. | |
| ### Is there a repository that links to any or all papers or systems that use the dataset? | |
| Please see https://github.com/tatsu-lab/stanford_alpaca | |
| ### Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? | |
| This dataset is generated by using the OpenAI's API. Therefore, this dataset cannot be used for commerical usage that compete with OpenAI. | |
| ### Are there tasks for which the dataset should not be used? | |
| The dataset should not be used for commerical usage that compete with OpenAI. | |
| ## Distribution | |
| ### Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? | |
| The dataset can be freely downloaded. | |
| ### How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? | |
| The dataset can be downloaded from the [Github repository](https://github.com/tatsu-lab/stanford_alpaca) as a json file. | |
| ### Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? | |
| This dataset is distributed under [the ODC-By license](https://opendatacommons.org/licenses/by/1-0/). | |
| ### Have any third parties imposed IP-based or other restrictions on the data associated with the instances? | |
| no | |
| ### Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? | |
| no | |
| ## Maintenance | |
| ### Who is supporting/hosting/maintaining the dataset? | |
| The dataset is hosted on github and the Github repository is maintained by Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li. | |
| ### How can the owner/curator/manager of the dataset be contacted (e.g., email address)? | |
| Please open an issue in the [Github repository](https://github.com/tatsu-lab/stanford_alpaca) | |
| ### Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? | |
| We do not have plan to update the dataset. |