Papers with suboptimal alignment
DWA-KD: Dual-Space Weighting and Time-Warped Alignment for Cross-Tokenizer Knowledge Distillation (2026.findings-eacl)
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| Challenge: | Existing cross-tokenizer distillation methods are limited by suboptimal alignment across sequence and vocabulary levels. |
| Approach: | They propose a cross-tokenizer distillation framework that enhances token-wise distillation . they use dual-space entropy-based weighting to achieve precise sequence-level alignment . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in large language models but has high computational and memory costs. |
From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment (2025.acl-long)
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| Challenge: | Existing approaches to align large language models with human preferences suffer from inconsistent scoring and suboptimal alignment. |
| Approach: | They propose a dual-consistency framework that aligns partial sequences with human preferences. |
| Outcome: | The proposed framework significantly reduces granularity discrepancies and improves GPT-4 evaluation scores. |
MAFMO: Multi-modal Adaptive Fusion with Meta-template Optimization for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing approaches focus on single-modality adjustments, leading to suboptimal alignment and limited generalization. |
| Approach: | They propose a plug-and-play framework for visual recognition that integrates a Harmonic Cross-Modal Adapter and a Meta-Template Optimization module. |
| Outcome: | Extensive experiments across multiple fine-grained visual recognition benchmarks show that MAFMO consistently improves existing methods’ performance on both novel classes and harmonic mean while maintaining robustness under various challenging conditions with minimal computational overhead. |