SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511669" target="_blank" >RIV/00216208:11320/25:10511669 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2025.semeval-1.322" target="_blank" >https://aclanthology.org/2025.semeval-1.322</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes

Popis výsledku v původním jazyce

We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).Mu-SHROOM addresses general-purpose LLMs in 14 languages, and frames the hallucination detection problem as a span-labeling task. We received 2,618 submissions from 43 participating teams employing diverse methodologies. The very high number of submissions highlights the interest of the community in hallucination detection. We present the results of the participating systems and provide an empirical analysis in order to better understand the factors that can lead to strong performance in this task. We also underscore current challenges, notably the varying degree of hallucinations across languages and the high annotator disagreement when labeling hallucination spans.

Název v anglickém jazyce

SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes

Popis výsledku anglicky

We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).Mu-SHROOM addresses general-purpose LLMs in 14 languages, and frames the hallucination detection problem as a span-labeling task. We received 2,618 submissions from 43 participating teams employing diverse methodologies. The very high number of submissions highlights the interest of the community in hallucination detection. We present the results of the participating systems and provide an empirical analysis in order to better understand the factors that can lead to strong performance in this task. We also underscore current challenges, notably the varying degree of hallucinations across languages and the high annotator disagreement when labeling hallucination spans.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)

ISBN

979-8-89176-273-2

ISSN

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e-ISSN

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Počet stran výsledku

26

Strana od-do

2472-2497

Název nakladatele

Association for Computational Lingustics

Místo vydání

Kerrville, TX, USA

Místo konání akce

Wien, Austria

Datum konání akce

31. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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