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: We are subject to credit risk, which is the risk of default that results from a borrower’s inability or unwillingness to make contractually required mortgage payments. While our loans are sold into the secondary market without recourse, we do have repurchase and indemnification obligations to investors for breaches under our loan sale agreements. For loans that were repurchased or not sold in the secondary market, we are subject to credit risk to the extent a borrower defaults and the proceeds upon ultimate foreclosure and liquidation of the property are insufficient to cover the amount of the mortgage loan plus expenses incurred. We believe that this risk is mitigated through the implementation of stringent underwriting standards, strong fraud detection tools and technology designed to comply with applicable laws and our standards. In addition, we believe that this risk is mitigated through the quality of our loan portfolio. For the year ended December 31, 2022, our originated loans had a weighted average loan to value ratio of 79.67%, and a weighted average FICO score of 738. For the year ended December 31, 2021, our originated loans had a weighted average loan to value ratio of 71.68%, and a weighted average FICO score of 750. Management believes that the increase in the weighted average loan to value ratio year over year is primarily due to the increase in the percentage of purchase volume to total loan origination volume in 2022._
Assistant: