Update README.md

README.md

@@ -9,6 +9,16 @@ pipeline_tag: sentence-similarity
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+Why use a ColBERT-like approach for your RAG application?
+
+Most retrieval methods have strong tradeoffs: 
+ * __Traditional sparse approaches__, such as BM25, are strong baselines, __but__ do not leverage any semantic understanding, and thus hit a hard ceiling.
+ * __Cross-encoder__ retriever methods are powerful, __but__ prohibitively expensive over large datasets: they must process the query against every single known document to be able to output scores.
+ * __Dense retrieval__ methods, using dense embeddings in vector databases, are lightweight and perform well, __but__ are data-inefficient (they require hundreds of millions if not billions of  training examples pairs to reach state-of-the-art performance) and generalise poorly in a lot of cases, as representing every single aspect of a document, to be able to match it to any related query, into a single vector is not a solved problem.
+
+ColBERT and its variants, including JaColBERT, aim to combine the best of all worlds: by representing the documents as essentially *bags-of-embeddings*, we obtain superior performance and strong out-of-domain generalisation at much lower compute cost than cross-encoders. The strong out-of-domain performance can be seen in our results: JaColBERT, despite not having been trained on Mr.TyDi and MIRACL, nearly matches e5 dense retrievers, who have been trained on these datasets. On JSQuAD, which is partially out-of-domain for e5 (it has only been exposed to the English version) and entirely out-of-domain for JaColBERT, it noticeably outperforms all e5 models.
+Moreover, this approach requires **considerably less data than dense embeddings**: To reach its current performance, JaColBERT v1 is only trained on 10M training triplets, compared to billion of examples for the multilingual e5 models.
+
 # Usage
 
 ## Installation