Symbol P
P is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
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Why this formula appears here
An end-to-end accuracy score measures the left-hand side only. The two terms on the right are exactly what generation metrics and retrieval metrics separately illuminate, and the second term is the one an aggregate score cannot see at all. P( R) is the probability the model answers correctly despite the retriever having failed — because the underlying language model already carried the answer parametrically, because the question was easy enough to guess, or, in the worst case, because the benchmark’s own answer had leaked into the model’s training data. Whenever that term is large, an end-to-end score can look excellent while the retrieval component the system was…
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Symbol R
the event that the retriever’s top- k results contain sufficient evidence to answer the query, and let “correct” mean the generated answer is judged faithful and relevant.
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Published contexts (2)
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Equation 20 · AI Agents & Systems
Measuring What a RAG System Retrieves, Not Just What It Answers
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
An end-to-end accuracy score measures the left-hand side only. The two terms on the right are exactly what generation metrics and retrieval metrics separately illuminate, and the second term is the one an aggregate score cannot see at all. P( R) is the probability the model answers correctly despite the retriever having failed — because the underlying language model already carried the answer parametrically, because the question was easy enough to guess, or, in the worst case, because the benchmark’s own answer had leaked into the model’s training data. Whenever that term is large, an end-to-end score can look excellent while the retrieval component the system was…
Meanings in this article
- : the event that the retriever’s top- k results contain sufficient evidence to answer the query, and let “correct” mean the generated answer is judged faithful and relevant.
Equation 23 · AI Agents & Systems
Measuring What a RAG System Retrieves, Not Just What It Answers
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
The mechanism is not exotic. A benchmark released as a public dataset, a paper, or a web page is exactly the kind of content large-scale pretraining crawls are built to ingest. A survey of benchmark data contamination in large language models lays out the resulting problem directly: leading models trained on web-scale corpora can inadvertently incorporate benchmark data into their training sets, which inflates measured performance in ways that do not reflect genuine capability, and the survey catalogues detection methods and alternative assessment strategies developed specifically to counter it [ 12 ] . For RAG evaluation the consequence is sharper than for a plain language-model benchmark,…
Meanings in this article
- : inflated by memorisation rather than by any generalisable capability, and every end-to-end score computed against that reference set overstates what the retrieval component is doing.