Madelon Hulsebos

I'm hiring PhDs through the ELLIS PhD program for research on table representation learning to democratize insights from tabular data!

Info: trl-lab.github.io/open-positio...
Apply: apply.ellis.eu (31 Oct)

Join us in beautiful Amsterdam at @cwi-amsterdam.bsky.social & @ellisamsterdam.bsky.social ✨

This exciting workshop is organized by @effylix.bsky.social, @lennartpurucker.bsky.social, Peter Baile Chen, Frank Hutter and me.

With talks by Marine Le Morvan (Inria), Floris Geerts (University of Antwerp) and @akhtarmubashara.bsky.social (ETH Zurich), and more TBA.

We hope to meet you there!!

Announcement for the EurIPS'25 Workshop on AI for Tabular Data. More information about the workshop can be found at: https://sites.google.com/view/eurips25-ai-td/home.

🚀 Excited to announce the AI for Tabular Data workshop at EurIPS 2025 in Copenhagen!

CfP: sites.google.com/view/eurips2... (papers due 20 Oct)

Join us @euripsconf.bsky.social to discuss neural tabular models and systems for predictive ML, tabular reasoning and retrieval, table synthesis and more ✨

Also hope attendees will enjoy the tutorial program, which I helped compile this year, with exciting tutorials on topics such as vector search, data discovery, graph databases, AI and relational data! Full list at vldb.org/2025/?papers...

Off to London 🇬🇧 for @vldb.bsky.social!

Looking forward to the panel discussion on neural models and tabular data Tue 10:45 🔥. I’ll share my thoughts on the state of affairs, promising paradigms, and future directions for TRL.

Will share my input and some take-aways of the discussions post conf.

We show that metrics like sacrebleu and bertscore aren't fit for tabular QA eval of LLMs as scores are inseparable. We also find a large gap between multiple-choice eval as in TQA-Bench and an LLM-judge which aligns with human annotation.

Bottomline: LLMs aren't robust for real-world multi-table QA

In our contribution led by @cowolff.bsky.social from the TRL Lab (trl-lab.github.io) we wondered: how well do LLMs reason over tabular data, really? We find that LLMs don't ack nor handle tables with disturbances (eg missing vals or duplicates) necessitating explicit prompting or cleaning pipelines.

How well do LLMs reason over tabular data, really? Large Language Models (LLMs) excel in natural language tasks, but less is known about their reasoning capabilities over tabular data. Prior analyses devise evaluation strategies that poorly reflect an...

The full program of the workshop is at: table-representation-learning.github.io/ACL2025/. Besides excellent contributed work (proceedings: aclanthology.org/2025.trl-1.0...), we'll have invited talks by Dan Roth, Tao Yu, Edward Choi and Julian Eisenschlos!

Excited to be at ACL! Join us at the Table Representation Learning workshop tomorrow in room 2.15 to talk about tables and AI.

We also present a paper showing the sensitivity of LLMs in tabular reasoning to e.g. missing vals and duplicates, by @cowolff.bsky.social at 16:50: arxiv.org/abs/2505.07453

The paper's called:
"How well do LLMs reason over tabular data, really?" 📊

We dig into two important questions:
1️⃣ Are general-purpose LLMs robust with real-world tables?
2️⃣ How should we actually evaluate them? (2/4)

Very excited about this! Aligns well with my 🇪🇺 focus this year.

Join us for discussions and talks on data management aspects for end-to-end ML on 27 June at @deem-workshop.bsky.social in Berlin. Keynotes by @pinartozun.bsky.social and @gaelvaroquaux.bsky.social 🤩

Check the full schedule deem-workshop.github.io#schedule & proceedings dl.acm.org/doi/proceedi...

Very interesting! I’ve seen synthetic data, with certain patterns (eg TabPFN with SCMs), come real far, but not entirely random compositions of tokens. Do you understand or have an hypothesis for what leads to this observation?

🚨What is SOTA on tabular data, really? We are excited to announce 𝗧𝗮𝗯𝗔𝗿𝗲𝗻𝗮, a living benchmark for machine learning on IID tabular data with:

📊 an online leaderboard (submit!)
📑 carefully curated datasets
📈 strong tree-based, deep learning, and foundation models

🧵

Let’s collaborate on democratizing insights from tabular data in Amsterdam! ✨

PhD directions: 1) fundamental techniques for tabular foundation models, 2) reliable mechanisms for AI-powered tabular data analysis.

Sharing w/ friends appreciated! ⬇️

Join us for discussions and talks on data management aspects for end-to-end ML on 27 June at @deem-workshop.bsky.social in Berlin. Keynotes by @pinartozun.bsky.social and @gaelvaroquaux.bsky.social 🤩

Check the full schedule deem-workshop.github.io#schedule & proceedings dl.acm.org/doi/proceedi...

Absolutely! Please send me an email then we can arrange a chat :)

Let’s collaborate on democratizing insights from tabular data in Amsterdam! ✨

PhD directions: 1) fundamental techniques for tabular foundation models, 2) reliable mechanisms for AI-powered tabular data analysis.

Sharing w/ friends appreciated! ⬇️

🏹 Job alert: 2 fully-funded PhD Positions at Table Representation Learning Lab - @ellisamsterdam.bsky.social‬

📍 Amsterdam 🇳🇱
📅 Apply by June 30
🔗 More info: https://bit.ly/4519pj1

"Can LLMs really reason over tabular data, really?"
That’s the title and central question of my first paper in my new role as a PhD student, which has been accepted to the 4th Table Representation Learning Workshop @ ACL 2025! arxiv.org/pdf/2505.07453

🧵Here’s what we found:

Eager to contribute to democratizing insights from tabular data? We have 2 new PhD openings! ✨

1) Fundamental Techniques in Table Representation Learning
2) Reliable AI-powered Tabular Data Analysis Systems

⏰ Apply by: 30 June 2025
📅 Start: Fall/Winter 2025
🔗 Info: trl-lab.github.io/open-positions

Eager to contribute to democratizing insights from tabular data? We have 2 new PhD openings! ✨

1) Fundamental Techniques in Table Representation Learning
2) Reliable AI-powered Tabular Data Analysis Systems

⏰ Apply by: 30 June 2025
📅 Start: Fall/Winter 2025
🔗 Info: trl-lab.github.io/open-positions

This is really cool!! 👏

Curious to hear other thoughts on this nice survey. We’ll continue doing this going forward, and everyone is welcome to join: trl-lab.github.io/trl-reading-.... Also for the monthly seminar btw ✨

We’re running a TRL reading group, and captured some reflections on the survey “Large Language Models on Tabular Data: Prediction, Generation, and Understanding“: trl-lab.github.io/blog/2025/re... by @daniel-gomm.bsky.social @effylix.bsky.social @zeyuzhang.bsky.social @cowolff.bsky.social and me!

We're currently finalizing some quite detailed insights on this (can share the paper next week, and will send some plots on DM). TLDR is that an LLM-as-a-judge is best, for now, for evaluating free-form text against precise answers. Methods like sacrebleu and bert-score do not give a clear signal...

This is also an issue for evaluation.. one approach could be to make a second LLM call to extract the concise answer / value from generated text.

This talk is today, 4-5pm UvA lab42 Amsterdam and Zoom. Looking forward 🤩

Good news for authors of submissions to the Table Representation Learning workshop @ ACL: we're extending the deadline a little bit. Papers are due 21 April. No exceptions from there :).

Details about the seminar talk titled TabICL: A Tabular Foundation Model for In-Context Learning on Large Data by Marine Le Morvan

Excited to share the new monthly Table Representation Learning (TRL) Seminar under the ELLIS Amsterdam TRL research theme! To recur every 2nd Friday.

Who: Marine Le Morvan, Inria (in-person)
When: Friday 11 April 4-5pm (+drinks)
Where: L3.36 Lab42 Science Park / Zoom

trl-lab.github.io/trl-seminar/