Papers by Song-ha Jo
Task-aware Block Pruning with Output Distribution Signals for Large Language Models (2026.findings-eacl)
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| Challenge: | Existing methods to estimate block importance rely on representation similarity or computationally expensive sensitivity analyses to estimate task-aware model behavior. |
| Approach: | They propose a novel approach that quantifies block-level uncertainty from the statistics of each block’s early-exited output distribution on a calibration dataset. |
| Outcome: | Experiments show that the proposed approach preserves downstream task performance while reducing inference latency and computational cost. |