Yanshu Li
Yanshu Li has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Sentiment & emotion, Multilingual & cross-lingual NLP; often with Barbara Plank, Bolei Ma, Chen Liu.
OpenAlex · ORCID · h-index 23 · 112 works · 1,518 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 29 times in our library · ⭐ Paper Stars ranking: #9137
Affiliations
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work
🧭 Topics 5Research subjects and languages the work is about.Language models & pretraining · 1 workVision & language · 1 workMultilingual & cross-lingual NLP · 1 workSentiment & emotion · 1 workEfficient models & compression · 1 work
Works by year
2020s
Papers
- TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration (2025 · 22 citations)
Large vision–language models (LVLMs) can learn tasks from interleaved image–text demonstrations, but their in-context learning (ICL) is fragile. Small changes to the chosen demonstrations (ICDs) can noticeably shift outputs, and existing selection methods rely on hand-crafted relevance metrics. - M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025 · 21 citations)
Research on aspect-based sentiment analysis (ABSA) relies mostly on English data. Earlier multilingual resources either use different content per language or cover only a few languages without aspect categories, which makes controlled cross-lingual comparison hard. The authors built M-ABSA, a parallel dataset for… - Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026 · 3 citations)
The paper argues that LoRA has two weaknesses. First, it spends its small rank budget equally on every low-rank direction, even though many directions carry noise; the authors call this semantic drift. Second, it tunes each layer in isolation, so updates in neighbouring layers can point in unrelated directions; they…
Co-authors here
- Barbara Plank
- Bolei Ma
- Chen Liu
- ChengYan Wu
- Hao Xu
- Jinfa Huang
- Lin Zhao
- Ningyuan Deng
- Pinyuan Feng
- Ruixiang Tang
- Tianyang Wang
- Xi Xiao
Venues
- Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2025 ×2
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
Cited by
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