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: The residential real estate mortgage lending business is sensitive to changes in interest rates, especially long-term interest rates. Lower interest rates generally increase the volume of mortgage originations, while higher interest rates generally cause that volume to decrease. Therefore, our mortgage performance is typically correlated to fluctuations in interest rates, primarily the 10-year U.S. Treasury rate. Historically, mortgage origination volume and sales for the Bank and for other financial institutions have risen and fallen in response to these and other factors. An increase in interest rates and/or a decrease in our mortgage production volume could have a materially adverse effect on our operating results. The 10-year U.S. Treasury rate was 3.97 percent at December 31, 2023, and averaged 2.96 percent during 2023, 101 basis points higher than average rates experienced during 2022. The sustained higher rates experienced throughout 2023 negatively impacted the mortgage market including our loan origination volume and refinancing activity. In addition to being affected by interest rates, the secondary mortgage markets are also subject to investor demand for residential mortgage loans and investor yield requirements for these loans. These conditions may fluctuate or worsen in the future. Adverse market conditions, including increased volatility, changes in interest rates and mortgage spreads and reduced market demand, could result in greater risk in retaining mortgage loans pending their sale to investors. A prolonged period of secondary market illiquidity may result in a reduction of our loan mortgage production volume and could have a materially adverse effect on our financial condition and results of operations.
Assistant: