Update README.md

README.md

@@ -216,7 +216,7 @@ 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.
+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 and lack of instruction following abilities, 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.