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Kangyang Luo

Kangyang Luo has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Baobao Chang, Fanchao Qi, Gang Chen.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 5 · 53 works · 129 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 31 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8977

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

🎓Tsinghua UniversityBeijing · China2024–2026 · 22 papersEmploymentcurrent🎓Peking UniversityBeijing · China2024–2026 · 4 paperscurrent🎓University of Science and Technology of ChinaHefei · China2026 · 2 paperscurrent🎓East China Normal UniversityShanghai · China2021–2025 · 7 papers🎓Shanghai University of Engineering ScienceShanghai · China2018–2019 · 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

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

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025 · 11 citations)
    Instruction tuning can teach LLMs to hallucinate when the training examples contain knowledge the model never acquired in pre-training.
  2. GATEAU: Selecting Influential Samples for Long Context Alignment (2025 · 5 citations)
    Teaching LLMs to follow instructions over very long inputs is hard because human annotation of long instruction-following data is costly.
  3. A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks (2026 · 4 citations)
    LLM-based agents on long, multi-step tasks often flail. Without an overall plan, they fall into aimless trial-and-error and produce hallucinated actions. Prior fixes either teach planning implicitly via SFT or RL on the executor, which needs lots of data or converges slowly, or supply explicit guidance that depends…
  4. FaithLens: Detecting and Explaining Faithfulness Hallucination (2026 · 1 citation)
    LLMs often produce claims that contradict, distort, or go beyond the context they were given. This faithfulness hallucination is a problem for RAG and summarization. Current detectors are either expensive prompted LLMs or compact classifiers that return only a binary label, give no reasoning, and generalize unevenly…

All works

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

  1. An LLM-Enhanced Adversarial Editing System for Lexical Simplification ↗Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) · no summary yetKeren Tan et al.2024 · 13 citations
  2. Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data FilteringAnnual Meeting of the Association for Computational Linguistics (ACL)Shuzheng Si, Haozhe Zhao, Gang Chen, et al.2025 · 11 citations
  3. GATEAU: Selecting Influential Samples for Long Context AlignmentConference on Empirical Methods in Natural Language Processing (EMNLP)Shuzheng Si, Haozhe Zhao, Gang Chen, et al.2025 · 5 citations
  4. A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent TasksAnnual Meeting of the Association for Computational Linguistics (ACL)Shuzheng Si, Haozhe Zhao, Kangyang Luo, et al.2026 · 4 citations
  5. FaithLens: Detecting and Explaining Faithfulness HallucinationFindings of the Association for Computational LinguisticsShuzheng Si, Qingyi Wang, Haozhe Zhao, et al.2026 · 1 citation

 Co-authors here

12 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 31 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

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