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[8.10] [DOCS] Adds section about tokens to ELSER conceptual (backport #2568) #2570

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23 changes: 19 additions & 4 deletions docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -20,13 +20,28 @@ meaning and user intent, rather than exact keyword matches.
ELSER is an out-of-domain model which means it does not require fine-tuning on
your own data, making it adaptable for various use cases out of the box.


[discrete]
[[elser-tokens]]
== Tokens - not synonyms

ELSER expands the indexed and searched passages into collections of terms that
are learned to co-occur frequently within a diverse set of training data. The
terms that the text is expanded into by the model _are not_ synonyms for the
search terms; they are learned associations. These expanded terms are weighted
as some of them are more significant than others. Then the {es}
{ref}/rank-features.html[rank features field type] is used to store the terms
and weights at index time, and to search against later.
search terms; they are learned associations capturing relevance. These expanded
terms are weighted as some of them are more significant than others. Then the
{es} {ref}/rank-features.html[rank features] field type is used to store the
terms and weights at index time, and to search against later.

This approach provides a more understandable search experience compared to
vector embeddings. However, attempting to directly interpret the tokens and
weights can be misleading, as the expansion essentially results in a vector in a
very high-dimensional space. Consequently, certain tokens, especially those with
low weight, contain information that is intertwined with other low-weight tokens
in the representation. In this regard, they function similarly to a dense vector
representation, making it challenging to separate their individual
contributions. This complexity can potentially lead to misinterpretations if not
carefully considered during analysis.


[discrete]
Expand Down