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What does hard parameter sharing mean?

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  1. What does hard parameter sharing mean?
    • Each task has a separate full network with a similarity penalty
    • Tasks share labels but no model parameters
    • One output head predicts every task without task identity
    • Tasks share a common hidden trunk and use task-specific output heads
  2. How does hard sharing differ from soft parameter sharing?
    • Soft sharing keeps separate task parameters and encourages them to remain similar
    • Soft sharing always uses one shared trunk
    • Hard sharing stores no parameters
    • The two terms mean the same architecture
  3. Why can hard sharing reduce overfitting for related tasks?
    • Each task receives more independent parameters
    • Shared layers learn features that must serve multiple tasks
    • The model avoids training data
    • All tasks are forced to use identical output labels
  4. How are task losses commonly combined for joint training?
    • By choosing one task and ignoring the others
    • By averaging labels across tasks
    • As a weighted sum in a shared objective
    • By using only the smallest loss
  5. What can happen if one task’s loss or gradients dominate?
    • All task heads disappear
    • The tasks become independent
    • The model automatically balances every gradient
    • The shared trunk may prioritize it while other tasks are under-served
  6. When is hard parameter sharing most suitable?
    • When tasks can benefit from useful shared representations
    • When every task is unrelated and incompatible
    • When a task must never influence another
    • When there is only one task
  7. What does a task-specific head usually do?
    • Stores all training examples
    • Maps shared features to that task’s output
    • Balances the gradients of every other task
    • Selects which hidden trunk to use per token
  8. What should be monitored when training a multi-task model?
    • Only the total parameter count
    • Only the largest task’s accuracy
    • Per-task performance and whether shared updates help or harm each task
    • The number of output heads alone