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Timothee Mickus

ACL 2026ICLR 2026检索与知识强化学习与后训练评测与基准可解释性
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0 一作 · 2 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

2
作者位次 #3/5ICLR 2026Poster· General Machine Learning / Transfer, Multitask and Meta-learning

Knowledge Distillation as Decontamination? Revisiting the “Data Laundering” Concern in Classification Tasks

Hengyu Luo · Raúl Vázquez · Timothee Mickus · Filip Ginter · Jörg Tiedemann

强化学习与后训练评测与基准检索与知识OpenReview
作者位次 #2/10ACL 2026Poster· Natural Language Processing

Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care

Michal Štefánik · Timothee Mickus · Marek Kadlčík · Bertram Højer · Michal Spiegel · Raúl Vázquez · Aman Sinha · Josef Kuchař · Philipp Mondorf · Pontus Stenetorp

摘要

Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations.In this work, we demonstrate that these representations are in fact strikingly systematic, to the point of being almost perfectly universal: different...

可解释性检索与知识OpenReview 项目页

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