Task-Specific Pre-training is an intermediate step between general pre-training and fine-tuning, where the model is further pre-trained on valid data using objectives closely related to the final target task — bridging the gap between the generic MLM objective and the specific downstream application.

Mechanism

• Phase 1: General Pre-training (Wiki + Books, MLM).
• Phase 2 (Task-Specific): Continue training on domain data using designated objectives (e.g., Gap Sentence Generation for Summarization).
• Phase 3: Fine-tuning on labeled data.

Why It Matters

• Alignment: Standard MLM is not aligned with generation or retrieval. Task-specific pre-training aligns the internal representations.
• Performance: Consistently improves performance, especially when labeled data is scarce.
• Domain: Often combined with Domain-Adaptive Pre-training (DAPT).

Task-Specific Pre-training is specialized drills — practicing the specific mechanics of the final game (reordering, summarizing) before the actual match.