Papers by Fengze Liu
TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training (2026.acl-long)
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Yifan Wang, null Binbinliu, Fengze Liu, Yuanfan Guo, Jiyao Deng, Xuecheng Wu, Weidong Zhou, Xiaohuan Zhou, Taifeng Wang
| Challenge: | Static data mixing strategies in large language models are often suboptimal as they fail to adapt to the model’s evolving learning states. |
| Approach: | They propose a semi-dynamic data mixing framework that uses a key observation of influence ranking invariance to reduce computational overhead by 80% . |
| Outcome: | The proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, effectively mitigating data under-digestion. |
AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents (2025.emnlp-main)
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| Challenge: | Clinical trials are expensive and time-consuming, and accurate trial prediction is key to advancing medical treatments. |
| Approach: | They propose a framework that combines reasoning capabilities of large language models with the explainability of classical machine learning to generate, evaluate, and refine tabular features without human input. |
| Outcome: | The proposed framework performs better than SOTA methods on clinical trial prediction tasks within a limited number of iterations. |
Event Causality Identification with Synthetic Control (2024.emnlp-main)
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| Challenge: | Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality . |
| Approach: | They propose to use the Rubin Causal Model to identify event causality by generating a twin from existing corpora. |
| Outcome: | The proposed method can identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard. |