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 majority of our assets are monetary in nature and, as a result, we are subject to significant risk from changes in interest rates. Between August 2019 and March 2020, the Federal Open Market Committee of the Federal Reserve Board decreased its target range for short-term interest rates by 200 basis points, and between March 2022 and December 2022, it raised interest rates by 425 basis points and indicated that additional increases may occur in 2023. Changes in interest rates may impact our net interest income in our banking segment as well as the valuation of our assets and liabilities in each of our segments. Earnings in our banking segment are significantly dependent on our net interest income, which is the difference between interest income on interest-earning assets, such as loans and securities, and interest expense on interest-bearing liabilities, such as deposits and borrowings. We expect to periodically experience “gaps” in the interest rate sensitivities of our banking segment’s assets and liabilities, meaning that either our interest-bearing liabilities will be more sensitive to changes in market interest rates than our interest-earning assets, or vice versa. In either event, if market interest rates should move contrary to our position, this “gap” may work against us, and our results of operations and financial condition may be adversely affected. Given the potential for an adverse impact on our net interest income associated with interest rate cycle transitions, we periodically evaluate our current “gap” position and determine whether a repositioning of the banking segment’s balance sheet is appropriate. Asymmetrical changes in interest rates, such as if short-term rates increase or decrease at a faster rate than long-term rates, can affect the slope of the yield curve. A
Assistant: