Update README.md

README.md

@@ -1,32 +1,74 @@
----
-license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: subject
-    dtype: string
-  - name: question
-    dtype: string
-  - name: A
-    dtype: string
-  - name: B
-    dtype: string
-  - name: C
-    dtype: string
-  - name: D
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: task
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 52881
-    num_examples: 131
-  download_size: 36977
-  dataset_size: 52881
----
+---
+license: apache-2.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: subject
+    dtype: string
+  - name: question
+    dtype: string
+  - name: A
+    dtype: string
+  - name: B
+    dtype: string
+  - name: C
+    dtype: string
+  - name: D
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: task
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 52881
+    num_examples: 131
+  download_size: 36977
+  dataset_size: 52881
+---
+
+# Finance Fundamentals: Domain Knowledge
+
+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)
+
+
+## Example
+
+```
+A fixed-rate system is characterized by:
+
+A. explicit legislative commitment to maintain a specified parity.
+B. monetary independence being subject to the maintenance of an exchange rate peg.
+C. target foreign exchange reserves bearing a direct relationship to domestic monetary aggregates.
+```
+
+## Citation
+
+If you find this data useful, please cite:
+```
+@inproceedings{krumdick-etal-2024-bizbench,
+    title = "{B}iz{B}ench: A Quantitative Reasoning Benchmark for Business and Finance",
+    author = "Krumdick, Michael  and
+      Koncel-Kedziorski, Rik  and
+      Lai, Viet Dac  and
+      Reddy, Varshini  and
+      Lovering, Charles  and
+      Tanner, Chris",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.452/",
+    doi = "10.18653/v1/2024.acl-long.452",
+    pages = "8309--8332",
+    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."
+}
+```