Papers by Tingting Song
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models (2024.findings-acl)
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Linhao Yu, Yongqi Leng, Yufei Huang, Shang Wu, Haixin Liu, Xinmeng Ji, Jiahui Zhao, Jinwang Song, Tingting Cui, Xiaoqing Cheng, Liutao Liutao, Deyi Xiong
| Challenge: | Recent years have witnessed remarkable progress achieved by large language models in both natural language understanding and generation. |
| Approach: | They propose a large benchmark CMoralEval for moral evaluation of Chinese LLMs . they use a Chinese TV program discussing Chinese moral norms and Chinese moral anomies based on various sources . |
| Outcome: | The proposed dataset is characterized by diversity and authenticity. |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection (2026.findings-acl)
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| Challenge: | Advanced speech synthesis technologies have enabled highly realistic speech generation, posing security risks that motivate research into audio deepfake detection (ADD). |
| Approach: | They propose a modular framework that integrates an XLSR front-end with synergistic Mamba-Attention backbones to capture artifacts in spoofed speech signals. |
| Outcome: | The proposed framework achieves competitive performance on the ASVspoof 2021 LA, DF, and In-the-Wild benchmarks compared to other state-of-the art systems. |