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--- |
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license: apache-2.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: subject |
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dtype: string |
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- name: question |
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dtype: string |
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- name: A |
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dtype: string |
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- name: B |
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dtype: string |
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- name: C |
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dtype: string |
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- name: D |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: task |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 52881 |
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num_examples: 131 |
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download_size: 36977 |
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dataset_size: 52881 |
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--- |
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# Finance Fundamentals: Domain Knowledge |
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This dataset contains 131 multiple choice questions designed to test a models domain knowledge in business and finance. For more information, see the [BizBench paper.](https://aclanthology.org/2024.acl-long.452.pdf) |
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## Example |
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``` |
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A fixed-rate system is characterized by: |
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A. explicit legislative commitment to maintain a specified parity. |
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B. monetary independence being subject to the maintenance of an exchange rate peg. |
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C. target foreign exchange reserves bearing a direct relationship to domestic monetary aggregates. |
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``` |
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## Citation |
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If you find this data useful, please cite: |
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``` |
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@inproceedings{krumdick-etal-2024-bizbench, |
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title = "{B}iz{B}ench: A Quantitative Reasoning Benchmark for Business and Finance", |
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author = "Krumdick, Michael and |
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Koncel-Kedziorski, Rik and |
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Lai, Viet Dac and |
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Reddy, Varshini and |
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Lovering, Charles and |
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Tanner, Chris", |
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editor = "Ku, Lun-Wei and |
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Martins, Andre and |
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Srikumar, Vivek", |
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booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = aug, |
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year = "2024", |
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address = "Bangkok, Thailand", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.acl-long.452/", |
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doi = "10.18653/v1/2024.acl-long.452", |
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pages = "8309--8332", |
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abstract = "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 conduct 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." |
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} |
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``` |
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