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Sharon Li

Sharon Li has 3 papers in our library, in 2026, most of them on Language models & pretraining, Vision & language; often with Changdae Oh, Sean Du, Seongheon Park.

3 papers in our library · 2026

OpenAlex · h-index 1 · 6 works · 1 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 3 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10667

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Wisconsin–MadisonMadison · United States2026 · 5 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities (2026 · 4 citations)
    Most uncertainty quantification (UQ) work for LLMs studies a model that answers once, or follows one chain of reasoning, and gets no new information along the way.
  2. VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation (2026)
    Large vision-language models (LVLMs) often hallucinate. Self-evaluation methods built for text-only LLMs, such as entropy, verbalized confidence, Semantic Entropy and EigenScore, mostly capture linguistic plausibility. They can therefore stay confident when an image contradicts what the language prior expects. VAUQ…
  3. Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks (2026)
    The paper examines why in-context learning in multimodal LLMs gains less from demonstrations than text-only ICL.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and OpportunitiesAnnual Meeting of the Association for Computational Linguistics (ACL)Changdae Oh, Seongheon Park, To Eun Kim, et al.2026 · 4 citations
  2. VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-EvaluationFindings of the Association for Computational LinguisticsSeongheon Park, Changdae Oh, Hyeong Kyu Choi, et al.2026
  3. Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and BottlenecksAnnual Meeting of the Association for Computational Linguistics (ACL)Yu Wang, Sharon Li2026
  4. How Retrieved Context Shapes Internal Representations in RAG ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetSamuel Yeh, Sharon Li2026

 Co-authors here

8 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 3 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/sharon-li/