Shengping Liu
Shengping Liu has 2 papers in our library, in 2023–2026, most of them on Reasoning, Language models & pretraining, Sentiment & emotion; often with Bin Li, Fei Xia, Jian Yu.
OpenAlex · ORCID · h-index 15 · 80 works · 1,197 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 197 times in our library · ⭐ Paper Stars ranking: #5512
Affiliations
Fields, topics and concepts
📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 work
🧭 Topics 3Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 1 workSentiment & emotion · 1 work
Works by year
2020s
Papers
- Large Language Models are Better Reasoners with Self-Verification (2023 · 304 citations)
Chain-of-thought (CoT) prompting helps large language models reason through multi-step problems. One wrong step, though, can carry through the chain and spoil the final answer. Earlier fixes trained a separate verifier model, which needs human labels and fine-tuning and gives scores that are hard to interpret. The… - Beyond Modality Collapse: Taming Guided Modality Entropy for Omni-modal Emotion Reasoning (2026)
Omni-modal LLMs that process video, audio and text often handle emotion inference poorly. The authors trace this to "modality collapse": the model leans heavily on one channel, usually vision, and ignores acoustic and verbal evidence. They address it in two ways.
Co-authors here
- Bin Li
- Fei Xia
- Jian Yu
- Jitao Sang
- Jun Zhao
- Kang Liu
- Minjun Zhu
- Rui Hu
- Shizhu He
- Yining Wang
- Yixuan Weng
- Yuxiang Zhang
Venues
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026, Findings of the Association for Computational Linguistics 2023
Cited by
Updated
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