license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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dtype: string
- name: answer
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- name: task
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- name: context
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BizBench: A Quantitative Reasoning Benchmark for Business and Finance
Public dataset for BizBench.
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/. 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.
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 |