Papers by Panagiotis Kaliosis
Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition (2025.findings-emnlp)
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| Challenge: | Character sets change over time and character frequency distributions shift across historical periods or regions . character distribution alignment can improve existing models at inference time without requiring retraining . |
| Approach: | They propose a loss function that incorporates the Wasserstein distance between predicted and target distributions. |
| Outcome: | The proposed method improves accuracy and robustness under temporal and contextual shifts. |
A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning (2024.findings-acl)
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Panagiotis Kaliosis, John Pavlopoulos, Foivos Charalampakos, Georgios Moschovis, Ion Androutsopoulos
| Challenge: | Diagnostic Captioning (DC) systems receive one or more medical images of a patient, such as X-Rays or Magnetic Resonance Images (MRIs). |
| Approach: | They propose a data-driven guided decoding method that incorporates medical information into the beam search of the diagnostic text generation process. |
| Outcome: | The proposed method improves on two medical datasets and can be used in few- and zero-shot learning scenarios. |
LVLMs and Humans Ground Differently in Referential Communication (2026.acl-long)
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Peter Zeng, Weiling Li, Amie J. Paige, Zhengxiang Wang, Panagiotis Kaliosis, Dimitris Samaras, Gregory J. Zelinsky, Susan Brennan, Owen Rambow
| Challenge: | generative AI agents cannot model common ground in a way that enables smooth communication . a recent study examined whether large language models and large vision language models engage in grounding as human discourse partners do . |
| Approach: | They propose to use referential communication to model common ground between a pair of directors and a picture matching system. |
| Outcome: | The proposed experiment shows that generative AI agents cannot model common ground . human conversation relies on common ground accrued and updated by interacting partners . |
LVLMs are Bad at Overhearing Human Referential Communication (2025.emnlp-main)
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| Challenge: | a crucial skill for embodied AI agents working with humans is grounding in referential communication. |
| Approach: | They use large vision language models to overhear spontaneous conversations between humans . they find that current LVLMs fail to show consistent performance improvement . |
| Outcome: | The proposed models fail to show consistent performance improvement over previous models . the authors release the results to facilitate future research . |