Long Context & Efficient Sequences · 2023

Lost in the Middle / Long-Context Reliability (RULER)

Nelson F. Liu, Kevin Lin, Percy Liang, Cheng-Ping Hsieh, Boris Ginsburg

An empirical study showing that long-context models retrieve information reliably only when it sits near the start or end of the input and degrade sharply for content in the middle, exposing that a large context window does not guarantee uniform use of it (with RULER later giving a systematic length-vs-reliability probe).

Editorial record

Plain-language summary

The work tested models on multi-document QA and key-value retrieval while varying where the relevant information was placed in a long input. Accuracy followed a U-shaped curve: high when the needed fact was at the beginning or end, substantially lower when buried in the middle, even for models nominally supporting the full length. This demonstrated that advertised context length overstates usable context, motivating position-robustness work and synthetic stress benchmarks like RULER that measure at what effective length a model still retrieves and reasons reliably.

Source record

Provenance

Record ID
P-427
Record created
2026-07-13
Last reviewed
2026-07-14
Record version
2

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