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Angelika Romanou

8 accepted papers

2026

Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation

ICLR 2026poster

The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and language, many rely on translations of English datasets, failing t…

Cited by 0SourcecodeScholar
2026

PERK: Long-Context Reasoning as Parameter-Efficient Test-Time Learning

ICLR 2026poster

Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. In this work, we propose PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for learning to encode long contexts using gradient updates at test time. Specifically, PE…

Cited by 0SourceScholar
2025

CAVE : Detecting and Explaining Commonsense Anomalies in Visual Environments

EMNLP 2025

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 unrealistic, synthetically generated anomalies, failing to capture the richness and unpredictability of real-world anomal

Cited by 0SourcePDFScholar
2025

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

ACL 2025long

Reliable multilingual evaluation is difficult, and culturally appropriate evaluation is even harder to achieve.A common practice to fill this gap is to machine-translate English evaluation sets. However, translation introduces language bias and carries over cultural and regional assumptions from the…

Cited by 0SourcePDFScholar
2025

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

ICLR 2025spotlight

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (i.e., mult…

Cited by 9SourcePDFScholar
2025

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

NeurIPS 2025poster

Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstract and complex phenomena such as `safety' and `robustness' requires strong construct validity, that is, having measures t…

Cited by 0SourceScholar
2025

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts

ACL 2025finding

Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-language large models (VLLMs) have demonstrated improvements across various tasks, t…

2023

CRAB: Assessing the Strength of Causal Relationships Between Real-world Events

EMNLP 2023long main

Understanding narratives requires reasoning about the cause-and-effect relationships between events mentioned in the text. While existing foundation models yield impressive results in many NLP tasks requiring reasoning, it is unclear whether they understand the complexity of the underlying network o…

Cited by 0SourcecodeScholar