System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Multi-family real estate loans generally involve a greater risk than residential real estate loans because of legislation and government regulations involving rent control and rent stabilization, which are outside the control of the borrower or the Bank, and could impair the value of the security for the loan or the future cash flow of such properties. For example, on June 14, 2019, the State of New York enacted legislation increasing the restrictions on rent increases in a rent-regulated apartment building, including, among other provisions, (i) repealing the vacancy bonus and longevity bonus, which allowed a property owner to raise rents as much as 20% each time a rental unit became vacant, (ii) eliminating high rent vacancy deregulation and high-income deregulation, which allowed a rental unit to be removed from rent stabilization once it crossed a statutory high-rent threshold and became vacant, or the tenant’s income exceeded the statutory amount in the preceding two years, and (iii) eliminating an exception that allowed a property owner who offered preferential rents to tenants to raise the rent to the full legal rent upon renewal. The legislation still permits a property owner to charge up to the full legal rent once the tenant vacates. As a result of this legislation as well as previously existing laws and regulations, it is possible that rental income might not rise sufficiently over time to satisfy increases in the loan rate at repricing or increases in overhead expenses (e.g., utilities, taxes, maintenance, etc.). For example, the New York City Rent Guidelines Board established the maximum rent increase on certain apartments at 3.0% for a one-year lease beginning on or after October 1, 2023 and on or after September 30, 2024, while the overall inflation rate increased at a greater rate. In addition, overhead (including maintenance) expenses often increase significantly during inflationary periods. Finally, if the cash flow from a collateral property is reduced (e.g., if leases are not obtained or renewed), the borrower’s ability to repay the loan and the value of the security for the loan may be impaired.
Assistant: