I’m thrilled to participate in such a prestigious conference with my first paper! See you in Dublin at #ACL2022 😎 #NLProc

We are very excited to share that the work of @caesar_one_ , @antonio_mallia , @JackPertschuk and @fabreetseo has been accepted to #ECIR2025 as a #shortpaper. See you in #Lucca. @ecir2025 @pinecone #AI #Research #IR #industry

Congratulations to our very own @antonio_mallia, @caesar_one_, and @JackPertschuk – as well as their co-authors – on their accepted #ECIR2025 research papers! 🎉 They continue to push the state-of-the-art forward on information retrieval, and we as an industry are better for it! 📚 📜 Sean MacAvaney, Antonio Mallia and Nicola Tonellotto: “Efficient Constant-Space Multi-Vector Retrieval", 2025 📜 Kaili Huang, Thejas Venkatesh, Uma Dingankar, Antonio Mallia, Daniel Campos, Jian Jiao, Christopher Potts, Matei Zaharia, Kwabena Boahen, Omar Khattab, Saarthak Sarup and Keshav Santhanam: “ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring”, 2025 📜 Cesare Campagnano, Antonio Mallia, Jack Pertschuk and Fabrizio Silvestri: “E2Rank: Efficient and Effective Layer-wise Reranking”, 2025

RT @RSTLessGroup: Congratulations to @caesar_one_ who defended his #PhD #thesis entitled "Foundational Advancements of Large Language Mode…

RT @RSTLessGroup: Today @Andrea_Bacciu  has presented the paper "DanteLLM: Let’s Push Italian LLM Research Forward!” @LrecColing , coautho…

RT @RSTLessGroup: Don't miss @Andrea_Bacciu and @caesar_one_ ’s presentation on Thursday at @LrecColing .They’ll be sharing their paper "Da…

Llama 3 is insanely moving fast. People are really pushing Llama 3 to its limits in incredible ways. 10 wild examples (and use cases)

This is actually huge: - No SFT stage (e.g., Zephyr used 200k examples) - Preference tuning with 7K examples only (other models trained with at least 60k samples) I've put a lot of care & love building the DPO version of the amazing Capybara dataset from @ldjconfirmed so I'm really pleased to see these results. Let's double down on useful open data for OSS AI developers and researchers

Redefining Retrieval in RAG A nice comprehensive study that focuses on the components needed to improve the retrieval component of a RAG system. Confirms that the position of relevant information should be placed near the query. The model will struggle to attend to the information if this is not the case. Surprisingly, it finds that related documents don't necessarily lead to improved performance for the RAG system. Even more unexpectedly, irrelevant and noisy documents can actually help drive up accuracy if placed correctly. We need more systematic studies around RAG. The hard part of a RAG system is typically the retriever component. Just dumping relevant docs into the context is not an effective approach but it's what a lot of LLM devs do. I like that the Ragas library proposes the use of several metrics for assessing a RAG system at both the generation and retrieval stages, including an end-to-end evaluation. It's a good first step but we still need better ways to integrate external information that can be effectively leveraged by the generative component.

Still can't handle the indecisiveness between Barbie and Oppenheimer? 😫💥 Don't fret! Come to the presentation of our new perspective paper, "Multimodal Neural Databases", where we lay out the vision for database-like queries on multimodal data. Tomorrow @SIGIR2023, 1.30pm GMT 8

QLoRA: 4-bit finetuning of LLMs is here! With it comes Guanaco, a chatbot on a single GPU, achieving 99% ChatGPT performance on the Vicuna benchmark: Paper: arxiv.org/abs/2305.14314 Code Demo: github.com/artidoro/qlora Samples: colab.research.google.com/dr… Colab: colab.research.google.com/dr…

LIMA : LLaMA 65B 1000 supervised samples = {GPT4, Bard} level performance. From @MetaAI arxiv.org/abs/2305.11206

MMS: Massively Multilingual Speech. - Can do speech2text and text speech in 1100 languages. - Can recognize 4000 spoken languages. - Code and models available under the CC-BY-NC 4.0 license. - half the word error rate of Whisper. Code Models: github.com/facebookresearch/… Paper: scontent-lga3-2.xx.fbcdn.net… Blog: ai.facebook.com/blog/multili…

Presentiamo il più grande LLM italiano realizzato dal gruppo di ricerca RSTLess della Sapienza Università di Roma. Il team di ricerca dietro Fauno comprende @Andrea_Bacciu, @GioTrappolini, Prof @EmanueleRodola , @teelinsan e il Prof @fabreetseo . github.com/RSTLess-research/…

Three papers accepted at NeurIPS'22 (!!) 1) Efficiently training low-curvature neural networks (arxiv.org/abs/2206.07144), w/ Kyle Matoba, @hima_lakkaraju, @francoisfleuret We propose to build NNs that are "as linear as possible", and thus eliminate excess model curvature.

YouTube is free education. But 99% don’t know the best spots on its virtual campus. Here are the top channels to accelerate your learning:

Hey #NLProc, I built this little tool to make working with @huggingface 🤗Transformers a bit easier. If you want to directly access whole-word embeddings hassle-free, give it a try! 👉GitHub: github.com/Riccorl/transform…

This week @Google researchers announced Minerva, an internally developed project that can answer mathematical questions and tackle other complex topics such as physics. 1/5

BTW.... dalle-mega from @borisdayma is now openly accessible on @huggingface ⚡️ To download it (10GB): git clone huggingface.co/dalle-mini/da…

Presenting in front of a live audience was an amazing experience :) hope we nailed it! Thanks @aclmeeting