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Pengfei He

Pengfei He has 4 papers in our library, in 2022–2026, most of them on Language models & pretraining, Question answering, Reasoning; often with Jiliang Tang, Shenglai Zeng, Yue Xing.

4 papers in our library · 2022–2026

OpenAlex · h-index 4 · 13 works · 74 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 74 times in our library · ⭐ Paper Stars ranking: #6394

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

🎓Michigan State UniversityEast Lansing · United States2024–2026 · 5 paperscurrent🎓University of ManitobaWinnipeg · Canada2025–2026 · 3 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Question answering · 1 workReasoning · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20252 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022 · 82 citations)
    Commonsense question answering often improves when a model receives extra background knowledge alongside the question.
  2. Unveiling Privacy Risks in LLM Agent Memory (2025 · 33 citations)
    LLM agents often keep a long-term memory of earlier user queries paired with the solutions they produced, and they reuse these records as in-context examples.
  3. How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026 · 13 citations)
    LLM agents often keep an episodic memory of earlier task queries and their execution trajectories, then retrieve similar records as in-context demonstrations.
  4. Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach (2025 · 5 citations)
    LLMs that receive retrieved passages, as in RAG, tend to trust that text too much. Misleading or off-topic passages can override knowledge the model already has. The authors define a "context-robust" LLM by four behaviours:

 Co-authors here

12 of 20 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 74 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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