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Yurun Song

Yurun Song has 2 papers in our library, in 2026, most of them on Language models & pretraining, Question answering, Reasoning; often with Ian Harris, Jianfei Yu, Rui Xia.

2 papers in our library · 2026

OpenAlex · h-index 5 · 27 works · 81 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 18 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #14100

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 California, IrvineIrvine · United States2021–2026 · 7 paperscurrent🎓Nanjing University of Science and TechnologyNanjing · China2025–2026 · 4 paperscurrent🎓Chongqing UniversityChongqing · China2020–2024 · 5 papers🔬State Key Laboratory of Mechanical TransmissionChongqing2020 · 2 papers

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).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 5Research subjects and languages the work is about.Knowledge graphs & ontologies · 1 workLanguage models & pretraining · 1 workQuestion answering · 1 workReasoning · 1 workSemantic parsing & meaning representation · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge (2026)
    The paper asks whether the way new knowledge is supplied to an LLM matters for open-domain multi-hop question answering, especially when the facts postdate the model's pretraining.
  2. Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026)
    LLMs often hallucinate facts on knowledge-heavy questions. Most knowledge-graph (KG) augmented methods simply walk along graph edges, which leaves constraints such as time filters or counting to be handled afterwards. Classical semantic parsing expresses this logic in executable queries, but it depends on scarce…

All works

3 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. SentSim: Crosslingual Semantic Evaluation of Machine Translation ↗Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetYurun Song, Junchen Zhao, Lucia Specia2021 · 37 citations
  2. Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel KnowledgeWorkshop on Generation, Evaluation and Metrics (GEM)Zhuoyi Yang, Yurun Song, Kyler G. Harris, et al.2026
  3. Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph ReasoningFindings of the Association for Computational LinguisticsYurun Song, Xiangqing Shen, Jianfei Yu, et al.2026

 Co-authors here

5 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 18 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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