|
--- |
|
license: apache-2.0 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: data/train-* |
|
- split: test |
|
path: data/test-* |
|
dataset_info: |
|
features: |
|
- name: question |
|
dtype: string |
|
- name: answer |
|
dtype: string |
|
- name: task |
|
dtype: string |
|
- name: context |
|
dtype: string |
|
- name: context_type |
|
dtype: string |
|
- name: options |
|
sequence: string |
|
- name: program |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 52823429 |
|
num_examples: 14377 |
|
- name: test |
|
num_bytes: 15720371 |
|
num_examples: 4673 |
|
download_size: 23760863 |
|
dataset_size: 68543800 |
|
--- |
|
|
|
|
|
<p align="left"> |
|
<img src="bizbench_pyramid.png"> |
|
</p> |
|
|
|
# BizBench: A Quantitative Reasoning Benchmark for Business and Finance |
|
|
|
Public dataset for [BizBench](https://arxiv.org/abs/2311.06602). |
|
|
|
Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. |
|
Together, these requirements make this domain difficult for large language models (LLMs). |
|
We introduce BizBench, a benchmark for evaluating models' ability to reason about realistic financial problems. |
|
BizBench comprises **eight quantitative reasoning tasks**, focusing on question-answering (QA) over financial data via program synthesis. |
|
We include three financially-themed code-generation tasks from newly collected and augmented QA data. |
|
Additionally, we isolate the reasoning capabilities required for financial QA: reading comprehension of financial text and tables for extracting intermediate values, and understanding financial concepts and formulas needed to calculate complex solutions. |
|
Collectively, these tasks evaluate a model's financial background knowledge, ability to parse financial documents, and capacity to solve problems with code. |
|
We conducted an in-depth evaluation of open-source and commercial LLMs, comparing and contrasting the behavior of code-focused and language-focused models. |
|
We demonstrate that the current bottleneck in performance is due to LLMs' limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain. |
|
|
|
We have also develop a heavily curated leaderboard with a held-out test set open to submission: [https://benchmarks.kensho.com/](https://benchmarks.kensho.com/). This set was manually curated by financial professionals and further cleaned by hand in order to ensure the highest quality. A sample pipeline for using this dataset can be found at [https://github.com/kensho-technologies/benchmarks-pipeline](https://github.com/kensho-technologies/benchmarks-pipeline). |
|
|
|
## Dataset Statistics |
|
|
|
| Dataset | Train/Few Shot Data | Test Data | |
|
| --- | --- | --- | |
|
| **Program Synthesis** | | | |
|
| FinCode | 7 | 47 | |
|
| CodeFinQA | 4668 | 795 | |
|
| CodeTATQA | 2856 | 2000 | |
|
| **Quantity Extraction** | | | |
|
| ConvFinQA (E) | | 629 | |
|
| TAT-QA (E) | | 120 | |
|
| SEC-Num | 6846 | 2000 | |
|
| **Domain Knowledge** | | | |
|
| FinKnow | | 744 | |
|
| ForumlaEval | | 50 | |
|
|
