Related papers: Findings of the MAGMaR 2026 Shared Task

Findings of the MAGMaR 2026 Shared Task

URL: http://arxiv.org/abs/2606.12295v1
Date: Wed, 10 Jun 2026 16:33:01 GMT
Title: Findings of the MAGMaR 2026 Shared Task
Abstract summary: This paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR)<n>Teams submitted systems focused on either (i) video retrieval or (ii) grounded generation of articles given retrieved videos.<n>All teams had at least one generated report that was labeled the best by a human annotator.
Score: 57.506522365488586
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR). In this shared task participants submitted systems focused on either (i) video retrieval or (ii) grounded generation of articles given retrieved videos. Teams could submit to either task. For the retrieval task, we had 2 participating teams that submitted a total of 17 systems -- all of which beat a baseline derived from the winner of last year's shared task. On the generation side, we had 4 teams submit 16 systems. All teams had at least one generated report that was labeled the best by a human annotator.

Related papers

Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering [59.6753725406784]
FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence.<n>The final-test set contains 256 items, split evenly between easy and expert tiers.<n>The strongest systems are closely clustered, with the top four separated by less than one percentage point in ROUGE-1 F1.
arXiv Detail & Related papers (2026-07-22T07:54:17Z)
M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text [3.91352287996586]
We introduce the Multi-Domain Detection of AI-Generated Text (M-DAIGT) shared task.<n>M-DAIGT comprises two binary classification subtasks: News Article Detection (NAD) and Academic Writing Detection (AWD)<n>A total of 46 unique teams registered for the shared task, of which four teams submitted final results.
arXiv Detail & Related papers (2025-11-14T14:26:31Z)
NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results [107.50621348972328]
The NTIRE 2025 XGC Quality Assessment Challenge will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025.<n>The challenge is divided into three tracks, including user generated video, AI generated video and talking head.<n>Each participating team in every track has proposed a method that outperforms the baseline, which has contributed to the development of fields in three tracks.
arXiv Detail & Related papers (2025-06-03T13:39:57Z)
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human [71.42669028683741]
We present a shared task on binary machine generated text detection conducted as a part of the GenAI workshop at COLING 2025.<n>The task consists of two subtasks: Monolingual (English) and Multilingual.<n>We provide a comprehensive overview of the data, a summary of the results, detailed descriptions of the participating systems, and an in-depth analysis of submissions.
arXiv Detail & Related papers (2025-01-19T11:11:55Z)
ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content [9.287041393988485]
We present an overview of the second edition of the ArAIEval shared task, organized as part of the Arabic 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively.
arXiv Detail & Related papers (2024-07-05T04:28:46Z)
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes [48.83290963506378]
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations. We observe a number of key trends in how this approach was tackled. While a majority of the teams did outperform our proposed baseline system, the performances of top-scoring systems are still consistent with a random handling of the more challenging items.
arXiv Detail & Related papers (2024-03-12T15:06:22Z)
Retrospective on the 2021 BASALT Competition on Learning from Human Feedback [92.37243979045817]
The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
arXiv Detail & Related papers (2022-04-14T17:24:54Z)
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression [1.9078991171384017]
This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases.
arXiv Detail & Related papers (2022-04-01T16:36:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.