231 citations · 337 across the 12 of their papers we have counts for
5 papers · 1 filter
Amplifying human performance in combinatorial competitive programming
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva +4
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady prog…
Round and Round We Go! What makes Rotary Positional Encodings useful?
Federico Barbero, Alex Vitvitskyi, Christos Perivolaropoulos +2
Positional Encodings (PEs) are a critical component of Transformer-based Large Language Models (LLMs), providing the attention mechanism with important sequence-position informatio…
Transformers meet Neural Algorithmic Reasoners
Wilfried Bounsi, Borja Ibarz, Andrew Dudzik +5
Transformers have revolutionized machine learning with their simple yet effective architecture. Pre-training Transformers on massive text datasets from the Internet has led to unma…
The CLRS-Text Algorithmic Reasoning Language Benchmark
Larisa Markeeva, Sean McLeish, Borja Ibarz +7
Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning foc…
Transformers need glasses! Information over-squashing in language tasks
Federico Barbero, Andrea Banino, Steven Kapturowski +5
We study how information propagates in decoder-only Transformers, which are the architectural backbone of most existing frontier large language models (LLMs). We rely on a theoreti…