Context is Gold to find the Gold Passage: Evaluating and Training
  Contextual Document Embeddings
Authors: name: Max Conti, name: Manuel Faysse, name: Gautier Viaud, name: Antoine Bosselut, name: Céline Hudelot, name: Pierre Colombo

A limitation of modern document retrieval embedding methods is that they
typically encode passages (chunks) from the same documents independently, often
overlooking crucial contextual information from the rest of the document that
could greatly improve individual chunk representations.
  In this work, we introduce ConTEB (Context-aware Text Embedding Benchmark), a
benchmark designed to evaluate retrieval models on their ability to leverage
document-wide context. Our results show that state-of-the-art embedding models
struggle in retrieval scenarios where context is required. To address this
limitation, we propose InSeNT (In-sequence Negative Training), a novel
contrastive post-training approach which combined with late chunking pooling
enhances contextual representation learning while preserving computational
efficiency. Our method significantly improves retrieval quality on ConTEB
without sacrificing base model performance. We further find chunks embedded
with our method are more robust to suboptimal chunking strategies and larger
retrieval corpus sizes. We open-source all artifacts at
https://github.com/illuin-tech/contextual-embeddings.