| --- |
| license: cdla-permissive-2.0 |
| task_categories: |
| - question-answering |
| language: |
| - en |
| tags: |
| - Business Process Management |
| - Causal |
| - NLP |
| - Reasoning |
| pretty_name: BP^C |
| size_categories: |
| - 1K<n<10K |
| --- |
| # BP<sup>C</sup>: A Benchmark Dataset for Causal Business Process Reasoning |
|
|
| # Dataset Card for BP<sup>C</sup> |
|
|
| ## Table of Contents |
| - [Table of Contents](#table-of-contents) |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks](#supported-tasks) |
| - [Languages](#languages) |
| <!--- [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) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
| - [Annotation Guidelines](#annotationguidelines) |
| - [Update](#updates) |
| - [Loading data](#loadingdata)--> |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** https://huggingface.co/datasets/ibm/BPC |
| - **Paper:** https://arxiv.org/abs/2406.05506 |
| - **Point of Contact:** [Inna Skarbovsky](inna@il.ibm.com) |
| - **Version:** 1.0 |
|
|
| ### Dataset Summary |
|
|
| Abstract. Large Language Models (LLMs) are increasingly used for boosting organizational efficiency and automating tasks. |
| While not originally designed for complex cognitive processes, recent efforts have further extended to employ LLMs in activities such as reasoning, planning, |
| 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 |
| 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 |
| the ability of LLMs to reason about causal and process perspectives of business operations. |
| 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, |
| a set of questions about these situations, and a set of ground truth answers to these questions. |
| Reasoning on BP^C is of crucial importance for process interventions and process improvement. |
| 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. |
|
|
| ### Supported Tasks |
|
|
| - Question Answering |
| - Causal and Process Reasoning |
| - LLM tunning and testing |
|
|
| ### Languages |
|
|
| - English |
