Sentence Order Prediction (SOP) is a pre-training objective introduced in ALBERT to replace Next Sentence Prediction (NSP) — instead of predicting if two sentences are random or consecutive (topic matching), SOP makes the model predict which of two consecutive sentences came first, forcing it to learn coherence rather than just topic similarity.

SOP vs. NSP

• NSP Weakness: Negative examples are random sentences — easy to distinguish by topic (e.g., "Hockey" vs. "Cooking").
• SOP Hardness: Negative examples are the SAME two consecutive sentences but SWAPPED order (B then A).
• Task: Given two segments, predict if order is (A, B) or (B, A).
• Focus: Modeling coherence, logical flow, and discourse markers — mere topic matching is insufficient.

Why It Matters

• Better Representations: ALBERT showed SOP leads to significantly better performance on multi-sentence reasoning tasks (SQuAD, RACE, MNLI).
• Efficiency: Harder task signal allows more efficient learning of inter-sentence relationships.
• Structure: Forces the model to understand narrative flow and causal links.

SOP is fixing NSP — a harder ordering task that forces the model to learn logical coherence instead of just topic matching.