--- license: cc-by-nc-sa-4.0 language: - lt size_categories: - n<1K --- # Dataset Card for TiLt-Pro TiLt-Pro (**T**ests **i**n **L**i**t**huanian, **Pro**fessional) is a dataset of multiple-choice question tests that are used to assess the work-related knowledge of workers of multiple professions. ## Table of Content - [Dataset Details](#dataset-details) - [Dataset description](#dataset-description) - [Uses](#uses) - [Direct Use](#direct-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Data Collection and Processing](#data-collection-and-processing) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Bias, Risks, and Limitations](#bias-risks-and-limitations) - [Recommendations](#recommendations) - [Dataset Card Author](#dataset-card-author) - [Dataset Card Contact](#dataset-card-contact) ## Dataset Details The dataset was collected in August, 2024. ### Dataset Description - **Point of contact:** [Jekaterina Novikova](https://jeknov.github.io/) - **Language:** Lithuanian (lt) - **License:** CC-BY-NC-SA-4.0 ## Uses ### Direct Use The dataset is intended to be used as a subset of training data for the development of multilingual language models. It can be used together with the [TiLt-HS](https://huggingface.co/datasets/Jekaterina/tilt-hs) dataset, as both follow the exact same structure. ## Dataset Structure ### Data Instances A typical data point comprises some meta data, including language of the test, the country of origin, file source and name, number of the test's specific question, and a license of the test. The main content contains the question, suggested options for the answer and the correct answer. In addition, some information about the nature of the test is provided, including test level and category in both original language and in English. An example from the TiLt-Pro dataset looks as follows: ``` { "language": "lt", "country": "Lithuania", "file_name": "Padavejo_ir_barmeno_IV_uzduociu_sasiuvinis.docx", "source": "https://www.kpmpc.lt/kpmpc/wp-content/uploads/Uzduociu_sasiuviniai/", "license": "no license", "level": "professional", "category_en": "waiter and bartender", "category_original_lang": "padavėjas ir barmenas", "original_question_num": 1, "question": "Kokioje taurėje patiekiamas viskis?", "options": [ "vyno;", "žemoje storadugnėje stiklinėje;", "tulpės formos žema kojele." ], "answer": "2" } ``` ### Data Fields - `language`: The language of the sample. - `country`: The country of the sample. - `source`: The ID or URL of the source. - `file_name`: The name of the source file. - `license`: License of the source. - `level`: The academic level of the tested knowledge. Can be one of the following: e.g. Middle School, High School, University Entrance, University, Professional etc. - `category_en`: The low level category according to the source IN ENGLISH, i.e. the exam name. - `category_original_lang`: Similarly to the `category_en` attribute, the low level category according to the source IN THE ORIGINAL LANGUAGE. - `original_question_num`: Id of the sample. - `question`: The text of the multi-choice question or the statement of the True-False question. - `options`: A list of texts of the multiple choice options. In the case of True-False task, it is left blank. - `answer`: The string representing the correct choice(s) (1, 2, 3, ...). It corresponds to the numbering of the respective choice columns. In the case of True-False task, this is "1" for true and "0" for false. ### Data Splits The dataset is not split and has only a *train* subset. The dataset contains tests for eight professions, with the following individual number of datapoints/test questions in each: | | Train | |----------------------------------------------------|-------| | employee safety and health in the fishing industry | 28 | | hairdresser | 67 | | hotel housekeeper | 50 | | room attendant | 50 | | sales assistant | 143 | | security guard | 115 | | shop assistant and cashier | 84 | | waiter and bartender | 150 | ## Dataset Creation ### Curation Rationale This dataset was collected as a part of the ***AYA Expedition*** initiative, for the ***Global Exams*** project. ### Source Data The original tests for security guards, used in this dataset, were downloaded from the website of [MB "Gynyba"](https://gynyba.eu/). The original employee safety and health in the fishing industry tests were downloaded from the website of the [Silutes Vocational Training Center](https://www.silutespmc.lt/). The original tests for hairdressers, hotel houskeepers, room attendants, sales assistants, shop assistants and waiters were downloaded from the website of [the Qualifications, Vocational Education and Training Development Centre](https://www.kpmpc.lt/kpmpc/teoriniu-ir-praktiniu-uzduociu-sasiuviniai/) in Lithuania. Exact link to each test question is provided in the `source` and `file_name` fields of the dataset. #### Data Collection and Processing Tests were downloaded during the period of August 25-27, 2024. Only the questions with multiple-choice answers were selected from the tests. Questions depending on images/figures to be answered, were filtered out. Questions that required reading a paragraph & answering were not included either. Only a subset of available professional tests was included in the current version of the dataset. ### Annotations The dataset does not contain any additional annotations. #### Personal and Sensitive Information No personal, sensitive or private information included in the dataset. ## Bias, Risks, and Limitations This dataset only includes the tests for a certain limited number of professions. There is a risk that some of the knowledge presented in the tests changes with time, so it is important to pay attention to the date of the data collection. This dataset only contains of Lithuanian professional tests and does not necessarily generalize to other languages. ### Recommendations Users should be aware of the risks and limitations of the dataset. ## Dataset Card Author [Jekaterina Novikova](https://jeknov.github.io/) ## Dataset Card Contact [Jekaterina Novikova](https://jeknov.github.io/)