Papers by Ziyu Ma
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
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| Challenge: | Existing evaluation metrics are expensive and easy to conduct but ineffective to reflect dialogue quality. |
| Approach: | They propose a self-supervised fine-grained dialogue evaluation framework which can automatically assign fine-granular scores for arbitrarily dialogue data. |
| Outcome: | The proposed framework is highly consistent with human evaluations and better than the state-of-the-art models. |
Pun2Pun: Benchmarking LLMs on Textual-Visual Chinese-English Pun Translation via Pragmatics Model and Linguistic Reasoning (2025.acl-srw)
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| Challenge: | Current approaches resort to suboptimal compromises and computational methods remain inadequate for translation. |
| Approach: | They propose a Constant-Variable Optimization (CVO) model for translation strategy and an Ovl metric for translation quality assessment that adapts to Chinese and English. |
| Outcome: | The proposed model improves performance on textual and visual puns while maintaining linguistic mechanisms and humorous effects. |
UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions (2026.acl-long)
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Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang
| Challenge: | Generative audio modeling has been fragmented into specialized tasks such as text-to-speech (TTS), text- to-music (TTM), and text-ta (TTA) specialized models require reference audio for timbre cloning and strict phoneme alignment, whereas TTA models generate unstructured textures from open-ended captions. |
| Approach: | They propose a unified flow-matching framework capable of synthesizing speech, music, sound effects . they propose 'token injection mechanism' that projects unstructured environmental sounds into structured temporal latent space . |
| Outcome: | The proposed framework achieves state-of-the-art performance in instruction-based TTS and TTM while maintaining competitive fidelity in TTA. |
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)
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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang
| Challenge: | Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs. |
| Approach: | They propose a multi-modal reward model that aligns LVLMs with human preferences. |
| Outcome: | The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model. |
Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)
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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu
| Challenge: | Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps. |
| Approach: | They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling. |
| Outcome: | The proposed method outperforms open- and closed-source models on most metrics. |
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)
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| Challenge: | Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively. |
| Approach: | They propose a framework that extracts feedback signals such as forgetting and uncertainty from rollout trajectories and utilizes them to guide LLM-based task synthesis. |
| Outcome: | Extensive experiments on AppWorld and BFCL show that the proposed framework improves over strong base models. |