Hi there, I’m Ben 👋

I’m interested in NLP&IR, with a special focus on developing cool, smaller models to power LLM-based applications. You may know me from my frequent twitter rants about using late-interaction over dense vectors, or my love for encoders.

I do R&D at Answer.AI 👀, where every quest is a side-quest and scope-creep is encouraged. Currently, I’ve helped gather a really cool crowd of people to bring BERT to 2024 👀!

I’ve also built 🪤RAGatouille, a python library whose grand aim is to bridge the gap between state-of-the-art research code and commonly used practices and Rerankers, a library whose aim is to make it really easy to use just about any common reranking method, and swap them in and out painlessly.

Feel free to reach out if you’d like to chat!

Working while sick

I never really write online about personal things (except a few times, generally about my wife, who is awesome). Not that I don’t want to, I’m not particularly privacy-minded, but more that I never feel like it’s that interesting to share a ton about my pretty mundane day-to-day. There’s one thing that I do off-handedly publicly mention, maybe more than I realise: health. Generally in the context of “sorry about RAGatouille, I’ve had less time to spend on it than I thought because of health”, or some other similar phrasing....

[Answer.AI] Small but Mighty: Introducing answerai-colbert-small

Say hello to answerai-colbert-small-v1, a tiny ColBERT model that punches well above its weight.

[Answer.AI] JaColBERTv2.5🇯🇵: Optimising Retrieval Training for Lower-Resources Languages

Introducing JaColBERTv2.5🇯🇵, the new best Japanese retrieval model. Through this release, we present a thorough analysis to better understand what helps in training a good multi-vector retrieval model.

[Answer.AI] A little pooling goes a long way for multi-vector representations

Blog post offering a quick overview of ColBERT and how it works, and introducing an efficient pooling trick to alleviate the issues it faces.

Questions & Answer(s): thoughts and joining Answer.AI

If you’re old school, you can find a raw HTML version of this post here This is a fairly long, stream-of-thought post about how I currently (Sunday Feb 4, 2024) feel about the broader ML/NLP/IR ecosystem and its future. Everything here’s on-the-fly opinion and can & will change. In summary: I think ML developments are incredibly exciting, and we need to continue to work on bridging the gap between ML-as-a-commodity-for-ML-practitioners to ML-as-a-commodity-for-everyone....