Papers with data construction methods
More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning (2025.emnlp-industry)
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| Challenge: | Despite various proposed data construction methods, their practical utility in real-world pipelines remains underexplored. |
| Approach: | They conduct a comprehensive analysis of open-source datasets and data synthesis techniques for mathematical reasoning under a unified pipeline designed to mirror training and deployment scenarios. |
| Outcome: | The proposed pipelines mirror training and deployment scenarios and are suitable for industrial applications. |
Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale (2024.emnlp-main)
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Junying Chen, Chi Gui, Ruyi Ouyang, Anningzhe Gao, Shunian Chen, Guiming Chen, Xidong Wang, Zhenyang Cai, Ke Ji, Xiang Wan, Benyou Wang
| Challenge: | Multimodal large language models (MLLMs) lack visual knowledge in medical applications due to data privacy concerns and high annotation costs. |
| Approach: | They refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) to denoise and reformat the data. |
| Outcome: | The proposed model significantly improves the MMMU Health & Medicine track and shows that it can be used in multimodal scenarios. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
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| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture (2025.findings-emnlp)
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Xidong Wang, Dingjie Song, Shunian Chen, Junying Chen, Zhenyang Cai, Chen Zhang, Lichao Sun, Benyou Wang
| Challenge: | Long-context Large Language Models (MLLMs) are critical for video understanding and image analysis. |
| Approach: | They propose a hybrid architecture that integrates Mamba and Transformer blocks . they introduce data construction methods that capture both temporal and spatial dependencies . |
| Outcome: | The proposed model achieves competitive results across various benchmarks while maintaining high throughput and low memory consumption. |