11 papers
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…
PERK: Long-Context Reasoning as Parameter-Efficient Test-Time Learning
Zeming Chen, Angelika Romanou, Gail Weiss +1
Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. Previous research shows that using test-time learning to encode cont…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Measuring what Matters: Construct Validity in Large Language Model Benchmarks
Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39
Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…
CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments
Rishika Bhagwatkar, Syrielle Montariol, Angelika Romanou +3
Humans can naturally identify, reason about, and explain anomalies in their environment. In computer vision, this long-standing challenge remains limited to industrial defects or u…
WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
Negar Foroutan, Angelika Romanou, Matin Ansaripour +3
Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic docume…