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Feihu Che

Feihu Che has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Jianhua Tao, Jinyang Wu, Mingkuan Feng.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 7 · 22 works · 247 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 14 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10358

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 · China2023–2026 · 9 papersEmploymentcurrent🔬Beijing National Research Center for Information Science and TechnologyBeijing · China2026 · 1 papercurrent🏛️Chinese Academy of SciencesBeijing · China2020–2023 · 5 papers🔬Institute of AutomationBeijing · China2021–2023 · 4 papers🎓University of Chinese Academy of SciencesBeijing · China2021 · 2 papers

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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models (2025 · 19 citations)
    RAG is widely used to reduce LLM hallucinations, but real retrieval results are messy. Earlier robustness studies looked at only a few noise types, usually three or fewer, and assumed noise always hurts. This paper builds a broader picture. It defines seven retrieval noise types grounded in linguistics and releases…
  2. Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026)
    Standard in-context learning (ICL) teaches LLMs by showing worked examples. The model then copies their surface form, which breaks down on hard multi-step problems. Results also depend heavily on how the demonstrations are chosen and ordered. ThoughtICR swaps concrete examples for abstract reasoning strategies.

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. ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetFeihu Che et al.2020 · 55 citations
  2. Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jinyang Wu, Shuai Zhang, Feihu Che, et al.2025 · 19 citations
  3. Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jinyang Wu, Mingkuan Feng, Shuai Zhang, et al.2026

 Co-authors here

9 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 14 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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