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Adam Pearce

Adam Pearce has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining; often with Andy Coenen, Ann Yuan, Avi Caciularu.

2 papers in our library · 2019–2024

OpenAlex · ORCID · Wikidata · h-index 10 · 18 works · 1,032 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 283 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4627

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

🏥Royal Brisbane and Women's HospitalBrisbane · Australiacurrent🏢GoogleMountain View · United States2019–2022 · 7 papers

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20241 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Visualizing and Measuring the Geometry of BERT (2019 · 349 citations)
    The paper asks how BERT internally organizes the linguistic knowledge that makes it so easy to fine-tune. It combines visualizations with quantitative tests. For syntax, the Google Brain authors build "attention probes". For each token pair, they gather the attention values from every head and layer of BERT-base into…
  2. Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models (2024 · 84 citations)
    Researchers often want to know what a large language model has encoded in its intermediate hidden states. Existing tools have drawbacks. Probes require supervised training on fixed label sets. Vocabulary projections such as Logit Lens become unreliable in early layers. Most methods return only token probabilities…

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. Visualizing and Measuring the Geometry of BERTarXiv:1906.02715Andy Coenen, Emily Reif, Ann Yuan, et al.2019 · 349 citations
  2. Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language ModelsarXiv:2401.06102Asma Ghandeharioun, Avi Caciularu, Adam Pearce, et al.2024 · 84 citations
  3. Auditing language models for hidden objectives ↗arXiv (Cornell University) · no summary yetMarks, Samuel et al.2025 · 19 citations

 Co-authors here

9 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 283 times (of the 149,439 papers whose bibliographies we read); 48 citing papers with a page here →.

Updated

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