Team Eye-Be-Am at SHROOM-Visions

Eli Schwartz

Team Eye-Be-Am at SHROOM-Visions

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Abstract

We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small (4B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states—the token being judged together with the state that predicted it—and is ensembled with a ~400B zero-shot VLM judge at prediction time. Both components see off-the-shelf OCR of any visible in-image text. We use synthetic hallucination data generated by the large model as a source of ensemble diversity, and use validation to select feature layer, training data and OCR grounding. Our official entry reaches mean Cor 0.487 / Cor-lbl 0.387 on the hidden test set, placing 6th/28 (EN), 6th/21 (FR), 8th/21 (IT) and 7th/22 (ZH) on the task’s primary Cor-lbl metric.

Anthology ID:: 2026.uncertainlp-main.51
Volume:: The Proceedings of the Third Workshop on Uncertainty-Aware NLP
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Wilker Aziz, Jonathan Berant, Bryan Eikema, Evgenia Ilia, Marie-Catherine de Marneffe, Barbara Plank, Artem Shelmanov, Swabha Swayamdipta, Jörg Tiedemann, Artem Vazhentsev, Raúl Vázquez, Chrysoula Zerva
Venues:: UncertaiNLP | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 771–779
Language:
URL:: https://aclanthology.org/2026.uncertainlp-main.51/
DOI:
Bibkey:
Cite (ACL):: Eli Schwartz. 2026. Team Eye-Be-Am at SHROOM-Visions. In The Proceedings of the Third Workshop on Uncertainty-Aware NLP, pages 771–779, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Team Eye-Be-Am at SHROOM-Visions (Schwartz, UncertaiNLP 2026)
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PDF:: https://aclanthology.org/2026.uncertainlp-main.51.pdf

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@inproceedings{schwartz-2026-team,
    title = "Team Eye-Be-Am at {SHROOM}-Visions",
    author = "Schwartz, Eli",
    editor = {Aziz, Wilker  and
      Berant, Jonathan  and
      Eikema, Bryan  and
      Ilia, Evgenia  and
      de Marneffe, Marie-Catherine  and
      Plank, Barbara  and
      Shelmanov, Artem  and
      Swayamdipta, Swabha  and
      Tiedemann, J{\"o}rg  and
      Vazhentsev, Artem  and
      V{\'a}zquez, Ra{\'u}l  and
      Zerva, Chrysoula},
    booktitle = "The Proceedings of the Third Workshop on Uncertainty-Aware {NLP}",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.uncertainlp-main.51/",
    pages = "771--779",
    ISBN = "979-8-89176-486-6",
    abstract = "We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small (4B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states{---}the token being judged together with the state that predicted it{---}and is ensembled with a {\textasciitilde}400B zero-shot VLM judge at prediction time. Both components see off-the-shelf OCR of any visible in-image text. We use synthetic hallucination data generated by the large model as a source of ensemble diversity, and use validation to select feature layer, training data and OCR grounding. Our official entry reaches mean Cor 0.487 / Cor-lbl 0.387 on the hidden test set, placing 6th/28 (EN), 6th/21 (FR), 8th/21 (IT) and 7th/22 (ZH) on the task{'}s primary Cor-lbl metric."
}

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%0 Conference Proceedings
%T Team Eye-Be-Am at SHROOM-Visions
%A Schwartz, Eli
%Y Aziz, Wilker
%Y Berant, Jonathan
%Y Eikema, Bryan
%Y Ilia, Evgenia
%Y de Marneffe, Marie-Catherine
%Y Plank, Barbara
%Y Shelmanov, Artem
%Y Swayamdipta, Swabha
%Y Tiedemann, Jörg
%Y Vazhentsev, Artem
%Y Vázquez, Raúl
%Y Zerva, Chrysoula
%S The Proceedings of the Third Workshop on Uncertainty-Aware NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-486-6
%F schwartz-2026-team
%X We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small (4B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states—the token being judged together with the state that predicted it—and is ensembled with a ~400B zero-shot VLM judge at prediction time. Both components see off-the-shelf OCR of any visible in-image text. We use synthetic hallucination data generated by the large model as a source of ensemble diversity, and use validation to select feature layer, training data and OCR grounding. Our official entry reaches mean Cor 0.487 / Cor-lbl 0.387 on the hidden test set, placing 6th/28 (EN), 6th/21 (FR), 8th/21 (IT) and 7th/22 (ZH) on the task’s primary Cor-lbl metric.
%U https://aclanthology.org/2026.uncertainlp-main.51/
%P 771-779

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Markdown (Informal)

[Team Eye-Be-Am at SHROOM-Visions](https://aclanthology.org/2026.uncertainlp-main.51/) (Schwartz, UncertaiNLP 2026)

Team Eye-Be-Am at SHROOM-Visions (Schwartz, UncertaiNLP 2026)

ACL

Eli Schwartz. 2026. Team Eye-Be-Am at SHROOM-Visions. In The Proceedings of the Third Workshop on Uncertainty-Aware NLP, pages 771–779, Budapest, Hungary. Association for Computational Linguistics.