Incredibly proud to be building the best embedding models on the planet, alongside so many brilliant colleagues.

If you are at #EMNLP2024 and interested in RAG, come to discuss our BERGEN library! @VNikoulina will present the BERGEN poster tomorrow, Nov 13, 16:00-17:30, location: Jasmine Repo: https://t.co/5uyOaRf6kN Paper: https://t.co/3prXmD9s4C @naverlabseurope #NLProc #RAG

Happy to see our work on Context Embeddings for efficient answer generation in RAG being featured among other great works. @dylan_wangs @HerveDejean @sclincha

What’s a good baseline for RAG? 🤔 The literature shows consistent differences in experimental setups, retrievers, datasets, and metrics. So, we built the BERGEN library https://t.co/9srOoFQNQ5 to enhance reproducibility and identify strong baselines : 🧵 @naverlabseurope

Context Embeddings for Efficient Answer Generation in RAG Proposes an effective context compression method to reduce long context and speed up generation time in RAG systems. The long contexts are compressed into to a small number of context embeddings which allow different

Context Embeddings for Efficient Answer Generation in RAG Speeds up generation time while improving answer quality by compressing multiple contexts into a small number of embeddings, offering flexible compression rates. 📝 https://t.co/ETcrjOnIgx

Excited to share that our participation in @trec_ikat 2023 ranked 1st! We submitted Retrieve-the-Generate and Generate-then-Retrieve runs,combining #LLMs & #search for conversational search. joint work with @z_abbasiantaeb @davidmrau @ChuanMg @srahmanidashti @maliannejadi

📢 SEA December: A Year in IR in Amsterdam 🎇 With 8 speakers from the IR Amsterdam ecosystem: 3⃣ @davidmrau (U. Amsterdam) - The Role of Complex NLP in Transformers for Text Ranking 📄 4⃣ @bobvanluijt (@SeMI_Tech) - About @weaviate_io. 1/2