Papers by Jianxin Ma
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)
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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li
| Challenge: | Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion. |
| Approach: | They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree . |
| Outcome: | The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. |
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification (D19-1)
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| Challenge: | Various neural networks are designed for text classification on the basis of word embedding, but polysemy is a fundamental feature of the natural language, which brings challenges to text classification. |
| Approach: | They propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses. |
| Outcome: | The proposed model extracts more discriminative semantic features and yields significant performance gain compared to baseline methods. |
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning (2025.findings-acl)
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| Challenge: | Existing approaches to enhance agent capabilities for Large Language Models treat all tokens equally . however, reasoning tokens versus boilerplate tokens differ in importance and learning complexity . recent research has focused on enhancing agent capabilities in large language models . |
| Approach: | They propose a Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination . they propose SHAD method which adaptively emphasizes reasoning tokens during fine-tuning . |
| Outcome: | The proposed method improves performance over standard fine-tuning methods. |