36 citations · 63 across the 32 of their papers we have counts for
11 papers · 1 filter
Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata
Pedro Bernardo, Alex Ploner, Marco Squarcina +2
Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…
Scaling Laws for Multimodal Data Mixtures
Aditi Khandelwal, Ayush K. Tarun, Yixuan Xu +5
Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the m…
Learning What to Imitate: Entropy-Aware Distribution Mixing
Juan Garcia Giraldo, Matteo Santelmo, Eduard Durech +3
Small language models are often post-trained as students on reasoning traces from stronger teacher models to efficiently learn new skills. However, token-level imitation on traces…
Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes
Paola Mejia-Domenzain, Jibril Frej, Seyed Parsa Neshaei +5
Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all l…
Lingua Franca or Probing Artifact? Rethinking Latent Language in Multilingual LLMs
Deniz Bayazit, Badr AlKhamissi, Antoine Bosselut
Latent language identification is often used to argue that multilingual language models route computation through language-specific states, such as English pivots. However, existin…
DynaVieW: Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics
Silin Gao, Hao Zhao, Zeming Chen +8
Multimodal LLMs struggle to systematically model the temporal evolution of visual scenes in videos or multi-image sequences. Such inputs require models to predict or simulate multi…
Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models
Yijie Tong, Yifan Hou, Shaobo Cui +2
Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their application and benefits for vision-lan…
An Engineering Journey Training Large Language Models at Scale on Alps: The Apertus Experience
Jonathan Coles, Stefano Schuppli, Lukas Drescher +20
Large Language Models (LLMs) have surged as a transformative technology for science and society, prompting governments worldwide to pursue sovereign AI capabilities that ensure dat…
Brittlebench: Quantifying LLM robustness via prompt sensitivity
Angelika Romanou, Mark Ibrahim, Candace Ross +8
Existing evaluation methods largely rely on clean, static benchmarks, which can overestimate true model performance by failing to capture the noise and variability inherent in real…
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
Nivya Talokar, Ayush K Tarun, Murari Mandal +2
LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to carry out complex misuse scenarios…
Tracking the Limits of Knowledge Propagation: How LLMs Fail at Multi-Step Reasoning with Conflicting Knowledge
Yiyang Feng, Zeming Chen, Haotian Wu +2
A common solution for mitigating outdated or incorrect information in Large Language Models (LLMs) is to provide updated facts in-context or through knowledge editing. However, the…