Papers with automated annotation pipeline
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)
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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
| Challenge: | Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations. |
| Approach: | They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision. |
| Outcome: | The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant. |
MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)
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| Challenge: | Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints. |
| Approach: | They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts. |
| Outcome: | The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains. |
NUMINA: A Natural Understanding Benchmark for Multi-dimensional Intelligence and Numerical Reasoning Abilities (2025.findings-emnlp)
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Changyu Zeng, Yifan Wang, Zimu Wang, Wei Wang, Zhengni Yang, Muyi Bao, Jimin Xiao, Anh Nguyen, Yutao Yue
| Challenge: | Existing 3D benchmarks lack fine-grained numerical reasoning task annotations, limiting MLLMs’ ability to perform precise spatial measurements and complex numerical reasoning. |
| Approach: | They propose a 3D-based benchmark to enhance indoor perceptual understanding by using multi-scale annotations and question-answer pairs. |
| Outcome: | The proposed benchmark improves indoor perceptual understanding by incorporating multi-scale annotations and question-answer pairs. |
IntentionESC: An Intention-Centered Framework for Enhancing Emotional Support in Dialogue Systems (2025.findings-acl)
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| Challenge: | IntentionESC defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring these intentions, and maps them to appropriate support strategies. |
| Approach: | They propose an Intention-centered Emotional Support Conversation framework which defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring intentions, and maps them to appropriate support strategies. |
| Outcome: | The proposed framework defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring these intentions, and maps them to appropriate support strategies. |