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--- |
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license: cdla-permissive-2.0 |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- Business Process Management |
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- Causal |
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- NLP |
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- Reasoning |
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pretty_name: BP^C |
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size_categories: |
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- 1K<n<10K |
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--- |
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# BP<sup>C</sup>: A Benchmark Dataset for Causal Business Process Reasoning |
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# Dataset Card for BP<sup>C</sup> |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks](#supported-tasks) |
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- [Languages](#languages) |
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<!--- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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- [Annotation Guidelines](#annotationguidelines) |
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- [Update](#updates) |
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- [Loading data](#loadingdata)--> |
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## Dataset Description |
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- **Homepage:** https://huggingface.co./datasets/ibm/BPC |
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- **Paper:** https://arxiv.org/abs/2406.05506 |
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- **Point of Contact:** [Inna Skarbovsky]([email protected]) |
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- **Version:** 1.0 |
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### Dataset Summary |
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Abstract. Large Language Models (LLMs) are increasingly used for boosting organizational efficiency and automating tasks. |
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While not originally designed for complex cognitive processes, recent efforts have further extended to employ LLMs in activities such as reasoning, planning, |
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and decision-making. In business processes, such abilities could be invaluable for leveraging on the massive corpora LLMs have been trained on for gaining a deep understanding |
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of such processes. In adherence to this goal, we attach here the BP<sup>C</sup> dataset, a newly developed set of process-aware Q&A that can be used to assess |
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the ability of LLMs to reason about causal and process perspectives of business operations. |
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We refer to this view as Causally-augmented Business Processes (BP^C). The benchmark comprises a set of domain-specific BP<sup>C</sup> related situations, |
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a set of questions about these situations, and a set of ground truth answers to these questions. |
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Reasoning on BP^C is of crucial importance for process interventions and process improvement. |
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The benchmark could be used in one of two possible modalities: testing the performance of any target LLM and training an LLM to advance its capability to reason about BP^C. |
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### Supported Tasks |
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- Question Answering |
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- Causal and Process Reasoning |
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- LLM tunning and testing |
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### Languages |
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- English |
