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The best external writing on AI testing, limitations and quality — curated, summarized, and rated. We link out to the source; the value-add is our summary and the findings each piece connects to.

1 reference

PaperHigh credibilityarXiv
Lost in the Middle: How Language Models Use Long Contexts
An empirical study showing that LLM accuracy depends strongly on where relevant information sits in the input: performance is highest when key facts are at the very start or end of the context and degrades markedly when they fall in the middle, producing a characteristic U-shaped curve.
🐛 1 linked findingEvalsRAGContext window

Lost in the Middle: How Language Models Use Long Contexts