The lost-in-the-middle effect in LLMs appears mainly when inputs fill up to half the model's context window; beyond that, accuracy favors information closest to the end.
Marzena Karpinska, Katherine Thai, Kyle Lo, Tanya Goyal, and Mohit Iyyer
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Positional Biases Shift as Inputs Approach Context Window Limits
The lost-in-the-middle effect in LLMs appears mainly when inputs fill up to half the model's context window; beyond that, accuracy favors information closest to the end.