speed commited on
Commit
dcb683b
Β·
verified Β·
1 Parent(s): d2c712b

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +9 -8
README.md CHANGED
@@ -128,7 +128,7 @@ license: other
128
  ---
129
 
130
  # EDINET-Bench
131
- πŸ“š [Paper](https://pub.sakana.ai/edinet-bench) | πŸ“ [Blog](https://sakana.ai/edinet-bench/) | πŸ§‘β€πŸ’» [Code](https://github.com/SakanaAI/EDINET-Bench)
132
 
133
  EDINET-Bench is a Japanese financial benchmark designed to evaluate the performance of LLMs on challenging financial tasks including accounting fraud detection, earnings forecasting, and industry prediction.
134
  This dataset is built leveraging [EDINET](https://disclosure2.edinet-fsa.go.jp), a platform managed by the Financial Services Agency (FSA) of Japan that provides access to disclosure documents such as securities reports.
@@ -137,7 +137,7 @@ This dataset is built leveraging [EDINET](https://disclosure2.edinet-fsa.go.jp),
137
  - **June 9, 2025**: This dataset was originally released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Although section 1.7.3 of the Public Domain License (PDL) 1.0 states that it is compatible with CC BY 4.0, we have relicensed the dataset under PDL 1.0 to ensure strict consistency with the original licensing terms of the source data.
138
 
139
  ## Resources
140
- - πŸ“ƒ**Paper**: Read our paper for detailed dataset construction pipeline and evaluation results at https://pub.sakana.ai/edinet-bench
141
  - πŸ—οΈ**Counstruction Code**: Create a new benchmark dataset at https://github.com/SakanaAI/edinet2dataset
142
  - πŸ“Š**Evaluation Code**: Evaluate the performance of models on EDINET-Bench at https://github.com/SakanaAI/EDINET-Bench
143
 
@@ -239,11 +239,12 @@ EDINET-Bench is intended solely for advancing LLM applications in finance and mu
239
 
240
  ```
241
  @misc{sugiura2025edinet,
242
- author = {Issa Sugiura and Takashi Ishida and Taro Makino and Chieko Tazuke and Takanori Nakagawa and Kosuke Nakago and David Ha},
243
- title = {{EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements}},
244
- institution = {Sakana AI},
245
- year = {2025},
246
- month = {June},
247
- url = {https://pub.sakana.ai/edinet-bench}
 
248
  }
249
  ```
 
128
  ---
129
 
130
  # EDINET-Bench
131
+ πŸ“š [Paper](https://arxiv.org/abs/2506.08762) | πŸ“ [Blog](https://sakana.ai/edinet-bench/) | πŸ§‘β€πŸ’» [Code](https://github.com/SakanaAI/EDINET-Bench)
132
 
133
  EDINET-Bench is a Japanese financial benchmark designed to evaluate the performance of LLMs on challenging financial tasks including accounting fraud detection, earnings forecasting, and industry prediction.
134
  This dataset is built leveraging [EDINET](https://disclosure2.edinet-fsa.go.jp), a platform managed by the Financial Services Agency (FSA) of Japan that provides access to disclosure documents such as securities reports.
 
137
  - **June 9, 2025**: This dataset was originally released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Although section 1.7.3 of the Public Domain License (PDL) 1.0 states that it is compatible with CC BY 4.0, we have relicensed the dataset under PDL 1.0 to ensure strict consistency with the original licensing terms of the source data.
138
 
139
  ## Resources
140
+ - πŸ“ƒ**Paper**: Read our paper for detailed dataset construction pipeline and evaluation results at https://arxiv.org/abs/2506.08762
141
  - πŸ—οΈ**Counstruction Code**: Create a new benchmark dataset at https://github.com/SakanaAI/edinet2dataset
142
  - πŸ“Š**Evaluation Code**: Evaluate the performance of models on EDINET-Bench at https://github.com/SakanaAI/EDINET-Bench
143
 
 
239
 
240
  ```
241
  @misc{sugiura2025edinet,
242
+ author={Issa Sugiura and Takashi Ishida and Taro Makino and Chieko Tazuke and Takanori Nakagawa and Kosuke Nakago and David Ha},
243
+ title={{EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements}},
244
+ year={2025},
245
+ eprint={2506.08762},
246
+ archivePrefix={arXiv},
247
+ primaryClass={q-fin.ST},
248
+ url={https://arxiv.org/abs/2506.08762},
249
  }
250
  ```