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: Our outstanding debt balances are predominantly fixed rate debt. While changes in interest rates will have no impact on the interest we pay on our fixed rate debt, interest on our commercial paper and revolving credit facility will be exposed to interest rate fluctuations. Additionally, as of December 31, 2022, because we held certain interest rate swap agreements that have the economic effect of modifying the fixed-interest obligations associated with our 3.900% Senior Notes due 2028 and our 2.00% Senior Notes due 2030, a portion of the fixed-rate interest payable on these senior notes effectively became variable based on LIBOR or SOFR. At December 31, 2022, there were no commercial paper borrowings outstanding and no outstanding principal balance under our revolving credit facility._By entering into the aforementioned swap arrangements, we have assumed risks associated with variable interest rates based upon LIBOR and SOFR. Changes in the overall level of interest rates affect the interest expense that we recognize in our Consolidated Statements of Income. An interest rate risk sensitivity analysis is used to measure interest rate risk by computing estimated changes in cash flows as a result of assumed changes in market interest rates. As of December 31, 2022, if LIBOR or SOFR-based interest rates would have been higher by 100 basis points, the change would have increased our interest expense annually by approximately $3 million, as it relates to our fixed to floating interest rate swap agreements. See Notes to Consolidated Financial Statements—Note 9. Financial Instruments._
Assistant: