Our new paper "Magneto: Combining Small and Large Language Models for Schema Matching" has just been published in the new issue of #PVLDB! The paper introduces a new framework that combines both small and large language models for effective schema matching. www.vldb.org/pvldb/vol18/...

PVLDB@pvldb.bsky.social · last yr.

Vol:18 No:8 → Magneto: Combining Small and Large Language Models for Schema Matching 👥 Authors: Yurong Liu, Eduardo Pena, Aecio Santos, Eden Wu, Juliana Freire 📄 PDF: https://www.vldb.org/pvldb/vol18/p2681-freire.pdf

📢 Tomorrow, I'll be presenting our new paper on LLM-based agents for interactive data integration at the #SIGMOD2025 NOVAS workshop. I'll also be in Berlin for the whole week, so please reach out if you'd like to chat or hang out! Paper: arxiv.org/abs/2502.07132

Interactive Data Harmonization with LLM Agents

Data harmonization is an essential task that entails integrating datasets from diverse sources. Despite years of research in this area, it remains a time-consuming and challenging task due to schema m...

arxiv.org

Transformer-based neural networks achieve impressive performance on coding, math & reasoning tasks that require keeping track of variables and their values. But how can they do that without explicit memory? 📄 Our new ICML paper investigates this in a synthetic setting! 🎥 youtu.be/Ux8iNcXNEhw 🧵 1/13

How Do Transformers Learn Variable Binding in Symbolic Programs?

YouTube video by Raphaël Millière

youtu.be

The Data Management for End-to-End Machine Learning workshop (@deem-workshop.bsky.social) will be back at #SIGMOD2025! ✨ 🔗 Check out the CfP: deem-workshop.github.io 📝 Submission deadline: March 21 📢 Notifications: April 25 Join us for the 9th edition in Berlin! #DEEM2025

SIGMOD/PODS Conference@sigmodconf.bsky.social · 2y ago

DEEM - The 9th Workshop on End-to-End Data Management is also co-located with SIGMOD/PODS 2025. The deadline for papers is March 21st. For more details checkout the website deem-workshop.github.io

Slides for "Table Foundation Models" I explain why these models can strongly outperform tree-based models, what are the intuitions, hopefully pointing to ways forward for more improvement speakerdeck.com/gaelvaroquau...

Table foundation models for analytics

Deep-learning typically does not outperform tree-based models on tabular data. Often this may be explained by the small size of such datasets. For image…

speakerdeck.com

@madelonhulsebos.bsky.social kicking off the 3rd Table Representation Learning workshop (@trl-research.bsky.social) at NeurIPS 2024. First keynote by @gaelvaroquaux.bsky.social.