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Scott Johnston

Scott Johnston has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Interpretability; often with Zac Hatfield-Dodds, Amanda Askell, Ben Mann.

2 papers in our library · 2022–2023

OpenAlex · ORCID · h-index 10 · 25 works · 467 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 400 citations in our library · ⭐ Paper Star rank #3182

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

🎓Stanford UniversityStanford · United States2015–2022 · 9 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.

 Topics

 Language models & pretraining (1) Interpretability (1)

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Towards Understanding Sycophancy in Language Models (2023 · 354 citations)
    The paper looks at sycophancy: AI assistants telling users what they seem to want to hear instead of what is accurate.
  2. Scaling Laws and Interpretability of Learning from Repeated Data (2022 · 62 citations)
    Large language models often see the same data several times. Sometimes this is done deliberately, to upweight high-quality sources, and sometimes it results from incomplete deduplication. The authors from Anthropic investigate why even a small fraction of repeated data harms models so severely. They train a family of…

 Co-authors here

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

 Cited by

Papers of our library cite their works 400 times (of the 140,884 papers whose bibliographies we read); 39 citing papers with a page here →.

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