Papers by Liangxin Liu
Curriculum Consistency Learning for Conditional Sentence Generation (2024.emnlp-main)
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| Challenge: | Consistency learning (CL) has proven to be a valuable technique for improving the robustness of conditional sentence generation models. |
| Approach: | They propose a strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features. |
| Outcome: | The proposed strategy delivers +2.0 accuracy point improvement compared with vanilla IT and +0.7 COMET scores over traditional CL methods in MT tasks. |
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)
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Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi
| Challenge: | Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential. |
| Approach: | They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling. |
| Outcome: | The proposed model improves performance on four benchmarks and significantly mitigates positional bias. |
ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training (2026.acl-long)
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Yu Liang, Liangxin Liu, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Daiting Shi
| Challenge: | ConsistRM is a self-training framework that enables effective and stable GRM training without human annotations. |
| Approach: | They propose a self-training framework that enables effective and stable GRM training without human annotations. |
| Outcome: | The proposed framework outperforms vanilla Reinforcement Fine-Tuning (RFT) by 1.5% on five benchmark datasets. |