Learned sparse representations form an attractive class of contextual embeddings for text
retrieval. That is so because they are effective models of relevance and are interpretable by …

Learned sparse representations form an effective and interpretable class of embeddings for
text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges …

A companion to the release of the latest version of the SPLADE library. We describe
changes to the training structure and present our latest series of models--SPLADE-v3. We …

Fine-tuning transformer-based deep-learning models are currently at the forefront of natural
language processing (NLP) and information retrieval (IR) tasks. However, fine-tuning these …

Recent large language models (LLMs) have demonstrated exceptional performance on
general-purpose text embedding tasks. While dense embeddings have dominated related …

Experiments often result in the need to compare an observation against a reference, where
observation and reference are selections made from some specified domain. The goal is to …

Learned sparse text embeddings have gained popularity due to their effectiveness in top-k
retrieval and inherent interpretability. Their distributional idiosyncrasies, however, have long …