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: In estimating the component of the ACL for loans that share similar risk characteristics, loans are segregated into loan segments with shared risk characteristics. The Company's primary portfolio segments align with the methodology applied in estimating the ACL under CECL. Loans are designated and pooled into loan segments based on product types, business lines, and other risk characteristics._In determining the ACL, the Company derives an estimate of expected credit losses primarily using an expected loss methodology that incorporates risk parameters (PD, LGD, and EAD), which are derived from various vendor models,_internally-developed statistical models, or non-statistical estimation approaches. Probability of default is projected in these models or estimation approaches using a single economic scenario and were developed to incorporate relevant information about past events, current conditions, and reasonable and supportable forecasts. With the exception of the Company's residential loan segment, the Company's PD models define default as loans that are 90 days past due, on nonaccrual status, have a charge-off, or obligor bankruptcy. Input reversion is used for all loan segment models, except for the commercial and industrial and CRE, owner-occupied loan segments. Output reversion is used for the commercial and industrial and CRE, owner-occupied loan segments by incorporating, after the forecast period, a one-year linear reversion to the long-term reversion rate in year three through the remaining life of the loans within the respective segments. LGDs are typically derived from the Company's historical loss experience. However, for the warehouse lending and municipal and nonprofit loan segments, where the Company has either zero (or near zero) losses, or has a limited loss history through the last economic downturn, certain non-modeled methodologies are employed to estimate LGD. Factors utilized in calculating average LGD vary for each loan segment and are further described below. EAD refers to the Company's exposure to loss at the time of borrower default. For revolving lines of credit, the Company incorporates an expectation of increased line utilization for a higher EAD on defaulted loans based on historical experience. For term loans, EAD is calculated using an amortization schedule based on contractual loan terms, adjusted for a prepayment rate assumption. Prepayment trends are sensitive to interest rates and the macroeconomic environment. Fixed rate loans are more influenced by interest rates, whereas variable rate loans are more influenced by the macroeconomic environment. After the quantitative expected loss estimates are calculated, management then adjusts these estimates to incorporate consideration of different probability weighted economic scenarios, current trends and conditions that are not captured in the quantitative loss estimates, through the use of qualitative and/or environmental factors._
Assistant: