Papers by Marek Kubis
Polish-English medical knowledge transfer: A new benchmark and results (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated significant potential in specialized tasks, including medical problem-solving. |
| Approach: | They propose to use a Polish medical licensing and specialization exam dataset to evaluate LLMs . they use exam questions and parallel Polish-English corpora professionally translated for foreign candidates . |
| Outcome: | The proposed dataset includes Polish exam questions and parallel Polish-English corpora professionally translated for foreign candidates. |
Using Bibliodata LODification to Create Metadata-Enriched Literary Corpora in Line with FAIR Principles (2024.lrec-main)
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| Challenge: | Literature corpus building is relatively nascent, and standardized procedures for curating literary corpora are not yet developed. |
| Approach: | They propose a workflow for the creation and reuse of literary corpora using a metadata-enriched Polish Novel Corpus from the 19th and 20th centuries. |
| Outcome: | The proposed workflow includes a multi-stage metadata enrichment and verification process and efficient data collection and data sharing according to the FAIR principles and 5- and 7-star data standards. |
A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models (2026.eacl-long)
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Iwona Christop, Mateusz Czyżnikiewicz, Paweł Skórzewski, Łukasz Bondaruk, Jakub Kubiak, Marcin Lewandowski, Marek Kubis
| Challenge: | Existing benchmarks for testing audio modality of multimodal large language models focus on testing audio tasks in isolation. |
| Approach: | They propose a new benchmark to assess multimodal large language models' ability to combine audio tasks. |
| Outcome: | The proposed benchmarks show that multimodal models can solve problems that require reasoning over audio signals with satisfactory results. |
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors (2023.emnlp-main)
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| Challenge: | proposed method combines back transcription with fine-grained technique for categorizing speech recognition errors . proposed method relies on the use of synthesized speech in place of audio recording . |
| Approach: | They propose a method for investigating the impact of speech recognition errors on NLU models . they use a back transcription procedure and a fine-grained technique for categorizing errors . |
| Outcome: | The proposed method relies on synthesized speech in place of audio recording to evaluate the model. |