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: Our role as advisor to our clients involves complex analysis and the exercise of professional judgment, including rendering fairness opinions in connection with mergers and other transactions. Our activities, and particularly those of our FVA group, may subject us to the risk of significant legal liabilities to our clients and affected third parties, including shareholders of our clients who could bring securities class actions against us. In recent years, the volume of claims and amount of damages claimed in litigation and regulatory proceedings against financial services companies have been increasing. Litigation alleging that we performed below our agreed standard of care or breached any other obligations to a client or other parties could expose us to significant legal liabilities, particularly with respect to our FVA group, and, regardless of outcome, is often very costly, could distract our management and could damage our reputation. These risks often may be difficult to assess or quantify and their existence and magnitude often remain unknown for substantial periods of time. Our engagements typically include broad indemnities from our clients and provisions to limit our exposure to legal claims relating to our services, but these provisions may not protect us in all cases, including when we perform below our agreed standard of care or a client does not have the financial capacity to pay under the indemnity. As a result, we may incur significant legal expenses in defending against or settling litigation. In addition, we may have to spend a significant amount to adequately insure against these potential claims, or insurance coverage may not be available on commercial terms or at all. Substantial legal liability or significant regulatory action against us could have material adverse financial effects or cause significant reputational harm to us, which could seriously harm our business prospects, financial condition and results of operations.
Assistant: