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: Historical results may not be indicative of future performance due to, among other things, changes in our mix of business, repricing of certain lines of business, or any number of economic cyclical effects on our business. Reserves, whether calculated under GAAP or statutory accounting principles, do not represent an exact calculation of future benefit liabilities but are instead estimates made by us using actuarial and statistical procedures. Actual experience may differ from our reserve assumptions. There can be no assurance that our reserves will be sufficient to fund our future liabilities in all circumstances. Future loss development may require reserves to be increased, which would adversely affect earnings in current and future periods. Life expectancies may increase, which could lengthen the time a claimant receives disability or long-term care benefits and could result in a change in mortality assumptions and an increase in reserves for these and other long-tailed products. Adjustments to reserve amounts may also be required in the event of changes from the assumptions regarding future morbidity (which represents the incidence of claims and the rate of recovery, including the effects thereon of inflation and other societal and economic factors); premium rate increases; persistency; policy benefit offsets, including those for social security and other government-based welfare benefits; and interest rates used in calculating the reserve amounts, which could have a material adverse effect on our results of operations or financial condition.
Assistant: