Papers by Yichen Jin
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)
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| Challenge: | Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. |
| Approach: | They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse. |
| Outcome: | The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages. |
VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition (2026.acl-long)
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| Challenge: | Reinforcement Learning (RL) is crucial for Video-LLMs with complex spatiotemporal reasoning. |
| Approach: | They propose a framework that decomposes difficulty into two axes in video understanding . they employ efficient, training-free proxies to map data onto a 2D curriculum grid . |
| Outcome: | The proposed framework surpasses strong RL baselines on reasoning and perception tasks. |
TinyChart: Efficient Chart Understanding with Program-of-Thoughts Learning and Visual Token Merging (2024.emnlp-main)
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| Challenge: | Recent studies have shown that multimodal large language models can be useful for chart understanding, but their size limits their use in resource-constrained environments. |
| Approach: | They propose an efficient multimodal large language model with only 3B parameters for chart understanding. |
| Outcome: | The proposed model outperforms several chart-understanding MLLMs with up to 13B parameters on ChartQA, Chart-to-Text, Chart to Table, OpenCQA, and ChartX. |
A Survey of Large Models in Sports (2026.findings-acl)
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| Challenge: | Increasing interest in sports has led to the rapid advancement of large models, particularly multimodal large language models (MLLMs) . linguistic intelligence is a key component of large-model-driven sports intelligence . |
| Approach: | They propose to establish a foundation for advancing research and practical development of large-model-driven sports intelligence. |
| Outcome: | The proposed model-driven sports intelligence will be able to process and generate sports-related language effectively and process multiple data modalities. |