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Published equation contexts

score(D,Q)=∑qi∈QIDF(qi)⋅f(qi,D)⋅(k1+1)f(qi,D)+k1⋅(1−b+b⋅∣D∣avgdl)\mathrm{score}(D, Q) = \sum_{q_i \in Q} \mathrm{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \dfrac{|D|}{\mathrm{avgdl}}\right)}

Why this formula appears here

The Text REtrieval Conference, run annually by the U.S. National Institute of Standards and Technology from 1992 onward, gave information retrieval something the field had mostly lacked: a shared, blind evaluation on a common document set, repeated every year with published results. Robertson, Walker, and colleagues at City University London entered TREC-3 in 1994 with the Okapi system, reporting a term-weighting approach within a probabilistic relevance framework and applying it, among other extensions, to phrase weighting and to query expansion using terms drawn from an initial pilot search [ 2 ] . Over that and the following TREC rounds, the Okapi team’s tuning of term-frequency…

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QQ

Symbol Q

Q appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

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qiq_i

Symbol q_i

qiq_i appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

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ff

Symbol f

the frequency of query term qiq_i in D , |D| is the document’s length, avgdl\mathrm{avgdl} is the average document length in the collection, and k1k_1.

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qi∈Qq_i \in Q

Starting index or lower bound: q_i in Q

This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.

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f(qi,D)⋅(k1+1)f(q_i, D) \cdot (k_1 + 1)

Numerator: f(q_i, D) × (k_1 + 1)

The complete quantity above the fraction bar.

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f(qi,D)+k1⋅(1−b+b⋅∣D∣avgdl)f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \dfrac{|D|}{\mathrm{avgdl}}\right)

Denominator: f(q_i, D) + k_1 × (1 - b + b × dfrac|D|avgdl)

The complete quantity below the fraction bar; it must be nonzero for this division.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

Research cited beside this formula

Published contexts (1)

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score(D,Q)=∑qi∈QIDF(qi)⋅f(qi,D)⋅(k1+1)f(qi,D)+k1⋅(1−b+b⋅∣D∣avgdl),\mathrm{score}(D, Q) = \sum_{q_i \in Q} \mathrm{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \dfrac{|D|}{\mathrm{avgdl}}\right)},

Equation 8 · AI Agents & Systems

From BM25 to Agentic Retrieval: A History of Retrieval-Augmented Generation

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

The Text REtrieval Conference, run annually by the U.S. National Institute of Standards and Technology from 1992 onward, gave information retrieval something the field had mostly lacked: a shared, blind evaluation on a common document set, repeated every year with published results. Robertson, Walker, and colleagues at City University London entered TREC-3 in 1994 with the Okapi system, reporting a term-weighting approach within a probabilistic relevance framework and applying it, among other extensions, to phrase weighting and to query expansion using terms drawn from an initial pilot search [ 2 ] . Over that and the following TREC rounds, the Okapi team’s tuning of term-frequency…

Meanings in this article

  • ff: the frequency of query term qiq_i in D , |D| is the document’s length, avgdl\mathrm{avgdl} is the average document length in the collection, and k1k_1.
  • bb: tuned constants controlling term-frequency saturation and length normalization respectively.
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