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Soyeong Jeong

Soyeong Jeong has 2 papers in our library, in 2024–2026, most of them on Information retrieval & search, Language models & pretraining; often with Jinheon Baek, Sung Ju Hwang, Jong C. Park.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 7 · 40 works · 354 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 241 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4436

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

🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2022–2026 · 22 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 2 worksLanguage models & pretraining · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024 · 382 citations)
    Retrieval-augmented LLMs often apply one fixed strategy to every question. Iterative multi-step pipelines waste computation on easy questions, while single-step or no-retrieval setups break down on multi-hop ones. Adaptive-RAG instead routes each incoming query to one of three strategies: answering from the LLM's own…
  2. UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities (2026 · 4 citations)
    Most retrieval-augmented generation (RAG) systems search one corpus in one modality, although real questions may need text, images, video or a mix, at very different levels of detail.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question ComplexityConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.2024 · 382 citations
  2. CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark ↗arXiv:2406.05967 · no summary yetDavid Romero et al.2024 · 90 citations
  3. CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation ↗Findings of the Association for Computational Linguistics: EMNLP 2025 · no summary yetEmilio Villa-Cueva et al.2025 · 5 citations
  4. UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and GranularitiesAnnual Meeting of the Association for Computational Linguistics (ACL)Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, et al.2026 · 4 citations

 Co-authors here

3 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 241 times (of the 149,439 papers whose bibliographies we read); 50 citing papers with a page here →.

Updated

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