task_categories:question-answering language:en license:cdla-permissive-2.0 size_categories:1K<n<10K format:csv modality:text library:datasets library:pandas library:mlcroissant library:polars arxiv:2406.05506 region:us Business Process Management Causal NLP Reasoning
---
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