most citedMM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks

3 citations · 3 across the 3 of their papers we have counts for

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cs.CL20241 cited

PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children

Yiqun Zhang, Xiaocui Yang, Xiaobai Li +5

Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-r…

cs.CL20241 cited

Language Models as Continuous Self-Evolving Data Engineers

Peidong Wang, Ming Wang, Zhiming Ma +5

Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addit…

cs.CL20241 cited

Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction

Qi Sun, Kun Huang, Xiaocui Yang +3

Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in information systems that aims to simultaneously extract entities with semantic relations from a documen…

cs.CL20233 cited

MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks

Xiaocui Yang, Wenfang Wu, Shi Feng +7

The popularity of multimodal large language models (MLLMs) has triggered a recent surge in research efforts dedicated to evaluating these models. Nevertheless, existing evaluation…

cs.CL2023

Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction

Qi Sun, Kun Huang, Xiaocui Yang +3

Document-level relation extraction (DocRE) aims to infer complex semantic relations among entities in a document. Distant supervision (DS) is able to generate massive auto-labeled…