Update README.md

README.md

@@ -122,6 +122,7 @@ language:
 - ja
 tags:
 - finance
+- accounting
 size_categories:
 - 1K<n<10K
 ---
@@ -148,7 +149,7 @@ For detailed information, please read our paper and code.
 **Acounting fraud detection**
 
 This task is a binary classification problem aimed at predicting whether a given annual report is fraudulent.
-The label is either fraud (1) or non-fraud (0).
+The label is either fraud (1) or non-fraud (0). The explanation includes the reasons why the LLM determined that the contents of the amended report are related to accounting fraud.
 
 ```python
 >>> from datasets import load_dataset
@@ -203,10 +204,24 @@ DatasetDict({
 })
 ```
 
+## Limitation
+
+- **Mislabeling**: When constructing the benchmark dataset for the accounting fraud detection task, we assume that only cases explicitly reported as fraudulent are labeled as such, while all others are considered non-fraudulent. However, there may be undiscovered fraud cases that remain unreported, introducing potential label noise into the dataset. 
+Additionally, our fraud examples are constructed by having the LLM read the contents of the amended reports and determine whether they are related to fraudulent activities. Due to the hallucination problem inherent in LLMs, there is a risk that some cases may be incorrectly identified as fraudulent.
+
+- **Intrinsic difficulty**: Among the tasks in our benchmark, the fraud detection and earnings forecasting tasks may be intrinsically challenging with a performance upper bound, as the LLM relies solely on information from a single annual report for its predictions. 
+Future research directions could explore the development of benchmark task designs that enable the model to utilize information beyond the annual report with novel agentic pipelines.
+
+
 ## LICENSE
 
 EDINET-Bench is licensed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en) in accordance with [EDINET's Terms of Use](https://disclosure2dl.edinet-fsa.go.jp/guide/static/disclosure/WZEK0030.html).
 
+
+## ⚠️ Warnings
+EDINET-Bench is intended solely for advancing LLM applications in finance and must not be used to target or harm any real companies included in the dataset.
+
+
 ## Citation
 
 ```