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Query Expansion with LLMs: Searching Better by Saying More Search has changed a lot since embedding models were introduced. Is there still a role for lexical techniques like query expansion in AI? We think so.

I applied LLMs for query expansion and we wrote this article:
It sees to work out-of-the-box and generally boost the performance of embedding models. However, it requires more latency. Would be interesting to see more about this.
📃: jina.ai/news/query-e...
🛠️: github.com/jina-ai/llm-...

Our submission to ECIR 2025 on jina-embeddings-v3 has been accepted! 🎉
At the ECIR Industry Day my colleague @str-saba.bsky.social presents how we train the latest version of our text embedding model.
More details on ECIR: ecir2025.eu
More details about the model: arxiv.org/abs/2409.10173

Masked Autoencoders Are Scalable Vision Learners This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the mis...

@bowang0911.bsky.social showed me this cool paper that I'd never read before, about Masked Autoencoder (MAE) for images. The idea: an image encoder encodes non-masked patches, followed by a decoder using the non-masked and masked embeddings to regenerate the original image arxiv.org/abs/2111.06377

I'm looking for an intern to introduce Sparse Embedding models to Sentence Transformers! If you're passionate about open source, interested in helping practitioners use your tools, and enjoy embedders/retrievers/rerankers, then I'd love to hear from you!

Links with details and to apply in 🧵

It's pretty sad to see the negative sentiment towards Hugging Face on this platform due to a dataset put by one of the employees. I want to write a small piece. 🧵

Hugging Face empowers everyone to use AI to create value and is against monopolization of AI it's a hosting platform above all.

let's embed them!

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