Performance on multi-document QA is highest when the answer is at the start or end of the context and drops sharply in the middle.

Capability: Losing information in long inputs

Sources

supportsLost in the Middle: How Language Models Use Long Contexts
Performance on multi-document QA is highest when the answer is at the start or end of the context and drops sharply in the middle.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

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Related claims

On multi-document question answering with GPT-3.5 Turbo's 16k-context variant, accuracy dropped by more than twenty points when the document containing the answer was moved from the start or end of the context to the middle, with nothing else about the task changed.Losing information in long inputs
In long-context tasks over 8K-128K-token documents, the middle-of-context penalty is granularity-dependent: word-level QA and paragraph-filling accuracy dip when the evidence sits mid-document, but sentence-cloze accuracy peaks in the middle, because mid-paragraph sentences carry concrete entity and date overlap that models match on instead of reasoning about discourse role.Losing information in long inputs · reviewed by AI
For multi-hop QA over 28k-896k token haystacks with frozen Qwen2.5 backbones (7B-72B), summarizing the document in a parallel hierarchical tree — overlapping windows scanned ~5x, raw segments scored 'critical' passed straight to the final answer, and one summarization channel per decomposed sub-question — scores higher than sequential memory-agent and BM25 retrieval baselines without training, and beats an RL-trained memory agent on the out-of-distribution dataset while losing to it in-distribution.Losing information in long inputs · reviewed by AI
For multi-agent pipelines on knowledge-intensive QA with an 8B backbone, deciding the collaboration topology after retrieval and conditioning it on the retrieved evidence yields higher accuracy than generating the topology from query semantics alone with the same retrieved evidence available at execution.Getting more from several models than from one · reviewed by AI