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Marek Suppa

7 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
2025

MMTEB: Massive Multilingual Text Embedding Benchmark

ICLR 2025poster

Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-dr…

2025

RAGthoven: A Configurable Toolkit for RAG-enabled LLM Experimentation

COLING 2025system demonstrations

Large Language Models (LLMs) have significantly altered the landscape of Natural Language Processing (NLP), having topped the benchmarks of many standard tasks and problems, particularly when used in combination with Retrieval Augmented Generation (RAG). Despite their impressive performance and rela…

2025

skLEP: A Slovak General Language Understanding Benchmark

ACL 2025finding

In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a…

2021

Cost-effective Deployment of BERT Models in Serverless Environment

NAACL 2021industry

In this study, we demonstrate the viability of deploying BERT-style models to AWS Lambda in a production environment. Since the freely available pre-trained models are too large to be deployed in this environment, we utilize knowledge distillation and fine-tune the models on proprietary datasets for…