Yimeng Zhang
Yimeng Zhang has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Sentiment & emotion, Vision & language; often with Chen Luo, Dengyun Peng, Dong She.
OpenAlex · h-index 0 · 6 works · 0 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 49 times in our library · ⭐ Paper Stars ranking: #7356
Affiliations
Fields, topics and concepts
📚 Shelves 11The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksComputer Science · 4 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksDecision Sciences · 1 workInformation Systems and Management · 1 workMaterials Chemistry · 1 workMaterials Science · 1 workComputer Vision and Pattern Recognition · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 work
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workSentiment & emotion · 1 workVision & language · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 1 work
Works by year
2020s
Papers
- SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning (2024 · 46 citations)
The paper addresses LLM unlearning: removing the effect of unwanted data, such as private, copyrighted or toxic content, without full retraining and without harming general utility. - AI4Research: A Survey of Artificial Intelligence for Scientific Research (2025 · 21 citations)
Reasoning-focused LLMs such as OpenAI-o1 and DeepSeek-R1 have prompted many efforts to automate parts of scientific work. - Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026 · 2 citations)
Tool-calling agents used in customer service often meet users whose requests lack key details, shift during the conversation, or break system policy. - EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness (2026)
The paper asks how multimodal large language models (MLLM) actually weigh text, audio and video when recognizing emotion, especially when those signals contradict each other or one is absent.
Co-authors here
- Chen Luo
- Dengyun Peng
- Dong She
- Hanqing Lu
- Jiannan Guan
- Jing Huang
- Jinghan Jia
- Jinhao Liu
- Jiri Gesi
- Libo Qin
- Mengkang Hu
- Nuo Chen
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2024
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Science China Information Sciences: Science China Information Sciences 2025
Cited by
Updated
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