Papers by Jingxian Xu
Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models (2025.acl-long)
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JianXing Liao, Junyan Xu, Yatao Sun, Maowen Tang, Sicheng He, Jingxian Liao, Shui Yu, Yun Li, Xiaohong Guan
| Challenge: | Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency. |
| Approach: | They propose a language-guided framework that integrates large language models with computer-automated design to address these challenges. |
| Outcome: | The proposed framework outperforms traditional methods in accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. |
Label Representations in Modeling Classification as Text Generation (2020.aacl-srw)
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| Challenge: | Existing methods for text generation use strings to represent labels . linguistic properties of labels do affect performance, though their results are limited to document retrieval. |
| Approach: | They investigate the effect of string representations on how effectively a model learns a task . they use four standard text classification tasks to model string representation . |
| Outcome: | The proposed model improves on four standard text classification tasks . the results are largely negative in the low data setting . |
TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes, but this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs. |
| Approach: | They propose a method that internalizes explicit reasoning into the model’s habitual behavior through a Teacher-Guided compression strategy inspired by human cognition. |
| Outcome: | The proposed method reduces inference-time costs while maintaining high performance while preserving high quality and diversity of the distillation dataset. |