6 citations · 7 across the 13 of their papers we have counts for
16 papers
Dynamic Routing as a New Dimension for Test-time Versatility of LLMs
Michal Štefánik, Marek Kadlčík, Josef Kuchař +1
Beyond scaling their parameters and data, large language models currently gain versatility on new problems along a single axis: the tokens they spend on chain-of-thought (CoT). We…
AIMO Interpretability Challenge
Michal Štefánik, Philipp Mondorf, Andreas Waldis +15
We propose the AIMO Interpretability Challenge, a competition on distinguishing robust from spurious reasoning in frontier mathematical language models based on the models' interna…
Language Models Learn Universal Representations of Numbers and Here's Why You Should Care
Michal Štefánik, Timothee Mickus, Marek Kadlčík +7
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 quantify that t…
Can Out-of-Distribution Evaluations Uncover Reliance on Shortcuts? A Case Study in Question Answering
Michal Štefánik, Timothee Mickus, Marek Kadlčík +2
A majority of recent work in AI assesses models' generalization capabilities through the lens of performance on out-of-distribution (OOD) datasets. Despite their practicality, such…
VectorEdits: A Dataset and Benchmark for Instruction-Based Editing of Vector Graphics
Josef Kuchař, Marek Kadlčík, Michal Spiegel +1
We introduce a large-scale dataset for instruction-guided vector image editing, consisting of over 270,000 pairs of SVG images paired with natural language edit instructions. Our d…
Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers
Marek Kadlčík, Michal Štefánik, Timothee Mickus +2
Pretrained language models (LMs) are prone to arithmetic errors. Existing work showed limited success in probing numeric values from models' representations, indicating that these…
Negation: A Pink Elephant in the Large Language Models' Room?
Tereza Vrabcová, Marek Kadlčík, Petr Sojka +2
Negations are key to determining sentence meaning, making them essential for logical reasoning. Despite their importance, negations pose a substantial challenge for large language…
Attend or Perish: Benchmarking Attention in Algorithmic Reasoning
Michal Spiegel, Michal Štefánik, Marek Kadlčík +1
Can transformers learn to perform algorithmic tasks reliably across previously unseen input/output domains? While pre-trained language models show solid accuracy on benchmarks inco…
Self-training Language Models for Arithmetic Reasoning
Marek Kadlčík, Michal Štefánik
Recent language models achieve impressive results in tasks involving complex multistep reasoning, but scaling these capabilities further traditionally requires expensive collection…
Concept-aware Data Construction Improves In-context Learning of Language Models
Michal Štefánik, Marek Kadlčík, Petr Sojka
Many recent language models (LMs) are capable of in-context learning (ICL), manifested in the LMs' ability to perform a new task solely from natural-language instruction. Previous…
Concept-aware Training Improves In-context Learning Ability of Language Models
Michal Štefánik, Marek Kadlčík
Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability, manifested in the LMs' ability to modulate their function by a task de…
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning
Marek Kadlčík, Adam Hájek, Jürgen Kieslich +1
The field of audio captioning has seen significant advancements in recent years, driven by the availability of large-scale audio datasets and advancements in deep learning techniqu…
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems
Marek Kadlčík, Michal Štefánik, Ondřej Sotolář +1
Despite outstanding performance in many tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. We address this deficiency…
Resources and Few-shot Learners for In-context Learning in Slavic Languages
Michal Štefánik, Marek Kadlčík, Piotr Gramacki +1
Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English. However, the abi…
Can In-context Learners Learn a Reasoning Concept from Demonstrations?
Michal Štefánik, Marek Kadlčík
Language models exhibit an emergent ability to learn a new task from a small number of input-output demonstrations. However, recent work shows that in-context learners largely rely…
Soft Alignment Objectives for Robust Adaptation of Language Generation
Michal Štefánik, Marek Kadlčík, Petr Sojka
Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further traini…