Papers by Songsheng Wang

2 papers
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance (2025.emnlp-main)

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Challenge: Visual Language Models (VLMs) have significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models.
Approach: They propose a framework to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model.
Outcome: Empirical results show that the proposed framework improves the speed of the prediction task by 44%.
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation (2025.findings-emnlp)

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Challenge: Existing LLM-based evaluation methods fail to accurately identify error spans and assess their severity.
Approach: They propose a Hierarchical Multi-Agent Framework for Machine Translation Evaluation based on the MQM error typology and a hierarchical multi-agent system enabling granular evaluation of subtype errors.
Outcome: The proposed framework outperforms baselines in error span detection and severity assessment.

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