93 citations · 391 across the 45 of their papers we have counts for
51 papers
LLMs as Feature Engineers for Text-and-Tabular Prediction
Merwan Barlier, Blaz Skrlj
We introduce an iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular prediction models. To navig…
Building a User Foundation Model for the Open Web
Solal Vernier, Ivan Can Arisoy, Merwan Barlier +1
User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable a…
PromptPack: Scaling LLM Annotation Agents for Online Recommendation
Sebastian Koralewski, Merwan Barlier, Yulia Stolin +1
Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single-call LLM annotation agent y…
KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models
Boshko Koloski, Xiangjian Jiang, Senja Pollak +3
Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks. Yet in niche domains, where data…
Challenges in Explaining Pretrained Clinical Text Classifiers
Kristian Miok, Matej Klemen, Blaz Škrlj +1
Explaining the predictions of neural models in clinical NLP remains a significant challenge, especially for complex tasks involving long, unstructured medical texts. While post-hoc…
TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning
Kristian Miok, Blaz Škrlj, Daniela Zaharie +1
Clinical language models often struggle to provide trustworthy predictions and explanations when applied to lengthy, unstructured electronic health records (EHRs). This work introd…