Lost in the Middle
Positional attention effects.
The Lost-in-the-Middle Effect
Across long-context models, retrieval accuracy follows a U-shaped curve by position: facts at the beginning and end of the context are recalled well, while facts in the middle are recalled worst. This is the 'lost in the middle' effect.
- It is a property of attention and training, not random.
- It persists even when the answer is unambiguously present.
Position is not neutral; it is a reliability factor you must design around.
Why the Middle Suffers
Several pressures compound in the middle: positional biases favor recent and earliest tokens, training data over-represents 'answer near the start or end' patterns, and the sheer number of competing distractors in the interior dilutes attention to any single fact.
You cannot remove the bias from the model, but you can stop fighting it.