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Dante Everaert

Dante Everaert has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Information retrieval & search, Efficient models & compression; often with Monica Xiao Cheng, Shenglai Zeng, Tianqi Zheng.

2 papers in our library · 2025–2026

OpenAlex · h-index 2 · 10 works · 9 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 13 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #10761

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 States2023–2026 · 4 paperscurrent

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 6The 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 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information retrieval & search · 1 workEfficient models & compression · 1 workLanguage models & pretraining · 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. Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs (2026)
    Personalizing LLMs usually means feeding them long user histories and profiles, which raises inference latency and API costs.

All works

5 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. AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track · no summary yetDante Everaert et al.2024 · 3 citations
  3. GIO: Gradient Information Optimization for Training Dataset Selection ↗arXiv (Cornell University) · no summary yetDante Everaert, Christopher E. Potts2023 · 3 citations
  4. Retrieval Augmented Spelling Correction for E-Commerce Applications ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track · no summary yetXuan Guo et al.2024 · 1 citation
  5. Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Shenglai Zeng, Tianqi Zheng, Chuan Tian, et al.2026

 Co-authors here

11 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 13 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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