Papers by Ye Jun Jian
C2DLM: Causal Concept-Guided Diffusion Large Language Models (2026.findings-acl)
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Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu, Xinpeng Dong, Ye Jun Jian, Lujia Pan, Fei Wu, Kun Kuang
| Challenge: | Autoregressive (AR) and diffusion language models (DLMs) suffer from insufficient reasoning capabilities. |
| Approach: | They propose a fully connected Diffusion Language Model that uses a concept-level causal graph to guide attention to learn causal relationships between concepts. |
| Outcome: | The proposed model achieves a 12% improvement and 3.2 training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. |
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models (2025.emnlp-main)
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| Challenge: | Existing fine-tuning paradigms focus on aligning LLMs with task-specific objectives. |
| Approach: | They propose a pipeline that leverages human priors to automatically generate token-level causal signals and introduce the Re-Attention mechanism to guide training. |
| Outcome: | The proposed pipeline achieves an average improvement of 5.76% on the STG dataset and 1.56% on downstream tasks. |