BPC

# BP^C: A Benchmark Dataset for Causal Business Process Reasoning

# Dataset Card for BP^C

## 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]([e-mail])
- **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^C 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^C 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

# BP^C：面向因果业务流程推理的基准数据集

# BP^C 数据集卡片

## 目录
- [目录](#table-of-contents)
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务](#supported-tasks)
- [语言](#languages)
<!--- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [数据集遴选依据](#curation-rationale)
- [源数据](#source-data)
- [标注信息](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据使用注意事项](#considerations-for-using-the-data)
- [数据集的社会影响](#social-impact-of-dataset)
- [偏见讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集编撰者](#dataset-curators)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献内容](#contributions)
- [标注指南](#annotationguidelines)
- [更新记录](#updates)
- [数据加载](#loadingdata)-->

## 数据集描述
- **主页**：https://huggingface.co/datasets/ibm/BPC
- **相关论文**：https://arxiv.org/abs/2406.05506
- **联系人**：[Inna Skarbovsky]([e-mail])
- **版本**：1.0

### 数据集概述
**摘要**：大语言模型（Large Language Model，LLM）正日益被用于提升组织效率与自动化任务。尽管大语言模型最初并非为复杂认知流程而设计，但近期研究已进一步将其应用于推理、规划与决策制定等活动。在业务流程领域，此类能力可借助大语言模型训练所用的海量语料库，实现对相关业务流程的深度理解。为此，我们特此发布BP^C（即因果增强业务流程，Causally-augmented Business Processes）数据集——一套全新开发的流程感知型问答集合，可用于评估大语言模型对业务运营的因果与流程视角进行推理的能力。该基准数据集包含一系列特定领域的BP^C相关场景、针对这些场景的若干问题，以及对应问题的标准答案。对BP^C进行推理对于流程干预与流程优化至关重要。该基准数据集可通过两种可能的方式使用：一是测试任意目标大语言模型的性能，二是用于训练大语言模型以提升其针对BP^C的推理能力。

### 支持任务
- 问答
- 因果与流程推理
- 大语言模型微调与测试

### 语言
- 英语