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Zhimeng Jiang

Zhimeng Jiang has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Information retrieval & search; often with Chia-Yuan Chang, Guanchu Wang, Hongye Jin.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 12 · 45 works · 738 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 94 times in our library · ⭐ Paper Stars ranking: #6241

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

🎓Texas A&M UniversityCollege Station · United States2019–2026 · 20 paperscurrent🏢VisaFoster City · United States2024–2026 · 5 paperscurrent🏢GoogleMountain View · United States2026 · 2 paperscurrent🎓University of Science and Technology of ChinaHefei · China2018–2020 · 9 papers🏛️Chinese Academy of SciencesBeijing · China2018–2020 · 7 papers🏢VisaLondon · United Kingdom2024–2025 · 3 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

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

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning (2024 · 81 citations)
    Large language models that use RoPE usually fail on inputs longer than their pretraining sequence length. Perplexity spikes and outputs become erratic. In this ICML 2024 paper, the authors argue that the problem is not a lack of long-text understanding. Instead, they blame a "positional O.O.D." effect: at inference…
  2. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025 · 65 citations)
    Retrieval-augmented generation can be undermined by retrieved passages that are off-topic or misleading. Such passages lower answer quality and add inference cost. MAIN-RAG is a post-retrieval filtering framework that needs no training or fine-tuning and runs on top of a standard retriever. It uses three LLM agents:

 Co-authors here

10 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 94 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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