Below are 15 task properties which were used for evaluating if a task is suitable for Machine Learning. Since LLMs are different from Machine Learning, revise the list of properties to be appropriate for LLMs. Explain your reasoning for each change.

Q1. Information needed to complete the task (inputs) and outputs can be explicitly specified in machine-readable format
Q2. Task information is recorded or recordable by computer
Q3. Task does not require a wide range of complex outputs (mental and/or physical)
Q4. Task feedback (on the success of outputs) is immediate or available through plentiful historical data.
Q5. The task output is error tolerant
Q6. It is not important that outputs are perceived to come from a human
Q7. Task does not require complex, abstract reasoning
Q8. Task is principally concerned with matching information to concepts, labels, predictions, or actions
Q9. Task does not require detailed, wide-ranging conversational interaction with a customer or other person
Q10. Task is highly routine and repeated frequently
Q11. Task is describable with rules
Q12. There is no need to explain decisions during task execution
Q13. Task can be converted to answering multiple choice questions, ranking alternative options, predicting a number, or grouping similar objects
Q14. Long term planning is not required to successfully complete the task
Q15. The task requires working with text data or might require working with text in the future
Q16. The task requires working with image/video data or might require wo Please answer in JSON format.
user