Papers by Zhengqing Liu

2 papers
Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models (2026.acl-long)

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Challenge: Reinforcement learning (RL) has improved text- and vision-language models, but its application in SDMs is hindered.
Approach: They propose a dual-axis Generative Reward Model that provides semantic quality and interaction timing for SDMs.
Outcome: The proposed model achieves state-of-the-art performance on interaction-quality assessment across a wide spectrum of datasets.
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)

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Challenge: Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding.
Approach: They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss .
Outcome: The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss.

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