Omkar2118/datasets huggingface dataset:
ALTA 2023 Shared Task
Full name: Discriminate between human-authored and synthetic text generated by Large Language Models (LLMs)

This dataset is described in the [ALTA 2023 Shared Task](https://www.alta.asn.au/events/sharedtask2023/index.html) and associated [CodaLab competition](https://codalab.lisn.upsaclay.fr/competitions/14327).

The goal of this task is to build automatic detection systems that can discriminate between human-authored and synthetic text generated by Large Language Models (LLMs). The generated synthetic text will come from a variety of sources, including different domain sources (e.g., law, medical) and different LLMs (e.g., T5, GPT-X). The performance of the models will be evaluated based on their accuracy, robustness in detecting synthetic text.
ALTA 2022 Shared Task
Full name: PIBOSO Sentence classification

This dataset is described in the [ALTA 2022 Shared Task](https://www.alta.asn.au/events/sharedtask2022/index.html) and associated [CodaLab competition](https://codalab.lisn.upsaclay.fr/competitions/6935).

The goal of this task is to build automatic sentence classifiers that can map the content of biomedical abstracts into a set of pre-defined categories, which are used for Evidence-Based Medicine (EBM). EBM practitioners rely on specific criteria when judging whether a scientific article is relevant to a given question. They generally follow the PICO criterion: Population (P) (i.e., participants in a study); Intervention (I); Comparison (C) (if appropriate); and Outcome (O) (of an Intervention). Variations and extensions of this classification have been proposed, and for this task we will extend PICO by adding the classes Background (B) and Study Design (S); and including sentences that have no relevant content: Other (O). Therefore, the goal will be to classify the provided sentences according to the PIBOSO schema. Such information could be leveraged in various ways: e.g., to improve search performance; to enable structured querying with specific categories; and to aid users in more quickly making judgements against specified PICOSO criteria.