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Shiyan Liu

Shiyan Liu has 2 papers in our library, in 2026, most of them on Information retrieval & search.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 13 · 39 works · 477 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #12219

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

🎓Henan University of Urban ConstructionPingdingshan · China2026 · 1 papercurrent🎓Huazhong University of Science and TechnologyWuhan · China2026 · 1 papercurrent🎓Shaoxing UniversityShaoxing · China2013–2025 · 16 papers🎓Shenzhen UniversityShenzhen · China2025 · 3 papers🎓Beihang UniversityBeijing · China2020–2024 · 3 papers🏢State Grid Corporation of ChinaBeijing · China2022 · 3 papers🎓Shizuoka UniversityShizuoka · Japan2024–2025 · 2 papers🎓Hebei UniversityBaoding · China2022–2023 · 2 papers🏢State Grid Hebei Electric Power CompanyShijiazhuang2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization (2026)
    Reflective automatic prompt optimization (APO) methods such as GEPA improve prompts by having an LLM reflect on failed examples.
  2. Test-Time Training for Zero-Resource Dense Retrieval Reranking (2026)
    The paper addresses reranking for dense retrieval when the target domain has no relevance labels. Cross-encoders need supervised training and add hundreds of milliseconds per query. BM25 is often unavailable in vector-database-only deployments. The authors propose DART (Dense Adaptive Reranking at Test-time), a…

Venues

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

Updated

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