Update README.md

README.md

@@ -56,13 +56,13 @@ The detailed description of dataset and reference will be added after the compet
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 1. **Passage Retrieval**:
-   - **FinDER**: This task involves retrieving relevant sections from **10-K Reports** and financial disclosures based on **Search Queries** that simulate real-world questions asked by financial professionals, using domain-specific jargon and abbreviations.
+   - **FinDER**: Involves retrieving relevant sections from **10-K Reports** and financial disclosures based on **Search Queries** that simulate real-world questions asked by financial professionals, using domain-specific jargon and abbreviations.
    - **FinQABench**: Focuses on testing AI models' ability to answer **Search Queries** over **10-K Reports** with accuracy, evaluating the system's ability to detect hallucinations and ensure factual correctness in generated answers.
-   - **FinanceBench**: Participants use **Natural Queries** to retrieve relevant information from public filings like **10-K** and **Annual Reports**. The aim is to evaluate how well systems handle straightforward, real-world financial questions.
+   - **FinanceBench**: Uses **Natural Queries** to retrieve relevant information from public filings like **10-K** and **Annual Reports**. The aim is to evaluate how well systems handle straightforward, real-world financial questions.
 
 2. **Tabular and Text Retrieval**:
    - **TATQA**: Requires participants to answer **Natural Queries** that involve numerical reasoning over hybrid data, which combines tables and text from **Financial Reports**. Tasks include basic arithmetic, comparisons, and logical reasoning.
-   - **FinQA**: This task demands answering complex **Natural Queries** over **Earnings Reports** using multi-step numerical reasoning. Participants must accurately extract and calculate data from both textual and tabular sources.
+   - **FinQA**: Demands answering complex **Natural Queries** over **Earnings Reports** using multi-step numerical reasoning. Participants must accurately extract and calculate data from both textual and tabular sources.
    - **ConvFinQA**: Involves handling **Conversational Queries** where participants answer multi-turn questions based on **Earnings Reports**, maintaining context and accuracy across multiple interactions.
    - **MultiHiertt**: Focuses on **Multi-Hop Queries**, requiring participants to retrieve and reason over hierarchical tables and unstructured text from **Annual Reports**, making this one of the more complex reasoning tasks involving multiple steps across various document sections.