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: National and international economic downturns have, and in the future could, reduce demand for our services in each of our reportable segments, resulting in the loss of business or increased pressure to contract for business on less favorable terms than our generally preferred terms. Economic downturns that impact our financial condition may be caused by inflation, supply chain disruptions, geopolitics, global energy shortages, major central bank policy actions including interest rate increases, public health crises, or other factors. Economic hardship in our client base has also impacted and may continue to impact our business. For example, in early stages of the COVID-19 pandemic, or in the past, such as in the period of economic distress following the financial crisis of 2008, certain of our businesses were negatively affected by reduced employment levels at our clients’ locations and declining levels of business and customer spending. In addition, financial distress and insolvency experienced by clients, especially larger clients, has in the past made it difficult and in the future could make it difficult for us to collect amounts we are owed and could result in the voiding or modification of existing contracts. For example, in response to the changed circumstances caused by shutdowns earlier in the COVID-19 pandemic, we worked with clients to renegotiate contracts and financial structures in order to mitigate lost revenues caused by partial or full closure of client premises. Similarly, financial distress or insolvency, if experienced by our key vendors and service providers such as insurance carriers, could significantly increase our costs.
Assistant: