Papers with AAM
Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation (2024.lrec-main)
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| Challenge: | Existing knowledge distillation approaches to NMT often rely on heuristics when deciding which teacher layers to distill from. |
| Approach: | They propose an approach to align student attention heads with their teacher counterparts by heuristics to solve a feature mapping problem. |
| Outcome: | The proposed strategy shows gains of +3.61 and +0.63 BLEU points for WMT-2022 DeDsb and WMT-2014 EnDe compared to baselines. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li
| Challenge: | Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences. |
| Approach: | They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks. |
| Outcome: | The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge. |