# Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge

Source: [Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge](<https://www.sota2.com/research/paper/68fa78771e209131cea7883e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:39:15.781Z

Authors: Bin Li, Shenxi Liu, Yixuan Weng, Yue Du, Yuhang Tian, Shoujun Zhou

Published: 2025-05-11T02:15:14.000Z

## Abstract

Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task has been introduced to further advance research in multi-modal, multilingual, and multi-hop medical instructional question answering (M4IVQA) systems, with a specific focus on medical instructional videos. The M4IVQA challenge focuses on evaluating models that integrate information from medical instructional videos, understand multiple languages, and answer multi-hop questions requiring reasoning over various modalities. This task consists of three tracks: multi-modal, multilingual, and multi-hop Temporal Answer Grounding in Single Video (M4TAGSV), multi-modal, multilingual, and multi-hop Video Corpus Retrieval (M4VCR) and multi-modal, multilingual, and multi-hop Temporal Answer Grounding in Video Corpus (M4TAGVC). Participants in M4IVQA are expected to develop algorithms capable of processing both video and text data, understanding multilingual queries, and providing relevant answers to multi-hop medical questions. We believe the newly introduced M4IVQA challenge will drive innovations in multimodal reasoning systems for healthcare scenarios, ultimately contributing to smarter emergency response systems and more effective medical education platforms in multilingual communities. Our official website is https://cmivqa.github.io/

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2505.06814>)

[PDF](<https://arxiv.org/pdf/2505.06814.pdf>)