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: •Changes in the cost of medical treatments (including prescription drugs) and underlying fee schedules (“inflation”)_•Availability of medical providers and medical wage impacts_•Frequency of visits to health providers_•Number of medical procedures given during visits to health providers_•Types of health providers used_•Type of medical treatments received_•Use of preferred provider networks and other medical cost containment practices_•Availability of new medical processes and equipment_•Changes in the use of pharmaceutical drugs, including drugs for pain management_•Degree of patient responsiveness to treatment_General workers’ compensation risk factors_•Frequency of reopening claims previously closed_•Mortality trends of injured workers with lifetime benefits and medical treatment_•Changes in statutory benefits, including due to presumption laws_•The impact, if any, of potential future changes to government health insurance legislation_Workers’ compensation book of business risk factors_•Product mix_•Injury type mix_•Changes in underwriting standards_Unanticipated changes in risk factors can affect reserves. As an indicator of the causal effect that a change in one or more risk factors could have on reserves for workers’ compensation, a 1% increase (decrease) in incremental paid loss development for each future calendar year could result in a 1.2% increase (decrease) in claims and claim adjustment expense reserves._
Assistant: