Papers with multi-task paradigm

1 papers
Impartial Multi-task Representation Learning via Variance-invariant Probabilistic Decoding (2025.acl-long)

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Challenge: Existing methods focus on balancing loss or gradients but fail to address this issue due to the representation discrepancy in latent space.
Approach: They propose a framework that harmonizes representation spaces across tasks to ensure impartial learning by harmonizing representation spaces.
Outcome: The proposed framework outperforms 12 representative methods under the same multi-task settings, especially in heterogeneous task combinations and data-constrained scenarios.

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