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Monica Xiao Cheng

Monica Xiao Cheng has 2 papers in our library, in 2025–2026, most of them on Information retrieval & search, Efficient models & compression; often with Chen Luo, Hanghang Tong, Hanqing Lu.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 4 · 17 works · 40 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 11 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9379

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

🏢AmazonSeattle · United States2024–2026 · 6 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2026 · 1 papercurrent🎓Beijing Information Science & Technology UniversityBeijing · ChinaEmployment

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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025 · 14 citations)
    RAG systems often fail when the retrieved passages are misleading (for example, after PoisonedRAG-style injection of false answers) or only loosely related to the question. LLMs tend to trust such context over what they already know. The authors frame knowledge checking as four tasks:
  2. Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR (2026 · 2 citations)
    RLVR methods such as GRPO and DAPO are expensive because they sample many rollouts per prompt. Their learning signal is also often weak: when a group's rewards are almost all correct or almost all wrong, group-normalized advantages approach zero. The authors propose ARRoL (Accelerating RLVR via online RoLlout…

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. Towards Knowledge Checking in Retrieval-augmented Generation: A Representation PerspectiveConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Shenglai Zeng, Jiankun Zhang, Bingheng Li, et al.2025 · 14 citations
  2. Prune as You Generate: Online Rollout Pruning for Faster and Better RLVRAnnual Meeting of the Association for Computational Linguistics (ACL)Haobo Xu, Sirui Chen, Ruizhong Qiu, et al.2026 · 2 citations
  3. Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetShenglai Zeng et al.2026

 Co-authors here

12 of 14 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 11 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/monica-xiao-cheng/