Add example of SPLADE term expansion

qdrant-landing/content/documentation/guides/text-search.md

@@ -626,7 +626,7 @@ POST /collections/books/points/query
 
 ### SPLADE++
 
-The SPLADE (Sparse Lexical and Dense) family of models are transformer-based models that generate sparse vectors out of text. These models combine the benefits of traditional lexical search with the power of transformer-based models by generating homonyms and synonyms.
+The SPLADE (Sparse Lexical and Dense) family of models are transformer-based models that generate sparse vectors out of text. These models combine the benefits of traditional lexical search with the power of transformer-based models by accounting for homonyms and synonyms. SPLADE models achieve this by expanding the vocabulary of the input text using contextual embeddings from the transformer model. For example, when processing the input text "time travel", a SPLADE model may expand the input to include related terms like "temporal", "journey", and "chronology". This expansion allows SPLADE models to capture the semantic meaning of the text while still leveraging the strengths of lexical search.
 
 The advantage of using SPLADE models is that they [perform better](/articles/sparse-vectors/#splade) than traditional BM25. They also have several downsides though. First, because they use a fixed vocabulary, you can't use SPLADE models to find terms that are not in the vocabulary, such as product IDs and out-of-domain language (words not seen in training). Secondly, because they are transformer-based models, SPLADE models are slower and require more computational resources than the traditional BM25 model.