--- title: "Nova DasSarma — author profile and papers in our library" description: "Nova DasSarma, Open Philanthropy Project: 2 papers on Elamur Research (2022), h-index 13 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/nova-dassarma/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/nova-dassarma/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Nova DasSarma # Nova DasSarma Nova DasSarma has 2 papers in our library, in 2022, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Interpretability](https://md.research.elamur.ai/free/en/topics/interpretability/); often with [Ben Mann](https://md.research.elamur.ai/free/en/authors/ben-mann/), [Dawn Drain](https://md.research.elamur.ai/free/en/authors/dawn-drain/), [Nelson Elhage](https://md.research.elamur.ai/free/en/authors/nelson-elhage/). 2 papers in our library · 2022 [OpenAlex](https://openalex.org/A5072660054) · [**h-index 13**](https://openalex.org/authors/A5072660054) · [14 works](https://openalex.org/works?filter=author.id:A5072660054) · [1,690 citations](https://openalex.org/works?filter=author.id:A5072660054&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/nova-dassarma/#papers) · [🔗 Cited 3,936 times in our library](https://md.research.elamur.ai/free/en/authors/nova-dassarma/cited-by/) · [6 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #3593](https://md.research.elamur.ai/free/en/paper-stars/page/36/#star-3593) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Nova+DasSarma) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Nova+DasSarma) · [dblp](https://dblp.org/search?q=Nova+DasSarma) · [ACL Anthology](https://aclanthology.org/search/?q=Nova+DasSarma) ## Affiliations [🔬**Open Philanthropy Project** San Francisco · United States 2024 · 1 paper*current*](https://ror.org/004d1k391) · [🤝**Machine Intelligence Research Institute** Berkeley · United States 2022–2023 · 2 papers](https://ror.org/01h4ass90) 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 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Interpretability · 1 work](https://md.research.elamur.ai/free/en/topics/interpretability/) 💡 Concepts 2 Key ideas and methods explained on our concept pages.[Few-shot learning · 1 work](https://md.research.elamur.ai/free/en/concepts/few-shot-learning/) · [Scaling laws · 1 work](https://md.research.elamur.ai/free/en/concepts/scaling-laws/) ## Works by year ### 2020s [**2022** 2 papers](https://md.research.elamur.ai/free/en/authors/nova-dassarma/year/2022/) ## Papers The most cited first (OpenAlex and our library). 1. **[In-context Learning and Induction Heads](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/)** *(2022 · 475 citations)* The authors from Anthropic search for the internal mechanism of transformer language models that is responsible for in-context learning. 2. **[Scaling Laws and Interpretability of Learning from Repeated Data](https://md.research.elamur.ai/free/en/papers/hernandez-2022-scaling-laws-interpretability-learning-repeated/)** *(2022 · 91 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… ## All works 8 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. **[Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback ↗](https://arxiv.org/abs/2204.05862) arXiv:2204.05862 · no summary yet**Yuntao Bai et al.*2022 · 1,778 citations* 2. **[Language Models (Mostly) Know What They Know ↗](https://arxiv.org/abs/2207.05221) arXiv:2207.05221 · no summary yet**Saurav Kadavath et al.*2022 · 827 citations* 3. [**In-context Learning and Induction Heads arXiv:2209.11895**Catherine Olsson, Nelson Elhage, Neel Nanda, et al.*2022 · 475 citations*](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/) 4. **[A General Language Assistant as a Laboratory for Alignment ↗](https://arxiv.org/abs/2112.00861) arXiv:2112.00861 · no summary yet**Amanda Askell et al.*2021 · 432 citations* 5. **[Predictability and Surprise in Large Generative Models ↗](https://doi.org/10.1145/3531146.3533229) arXiv:2202.07785 · no summary yet**Deep Ganguli et al.*2022 · 268 citations* 6. **[Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training ↗](https://arxiv.org/abs/2401.05566) arXiv:2401.05566 · no summary yet**Evan Hubinger et al.*2024 · 123 citations* 7. [**Scaling Laws and Interpretability of Learning from Repeated Data arXiv:2205.10487**Danny Hernandez, Tom Brown, Tom Conerly, et al.*2022 · 91 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2022-scaling-laws-interpretability-learning-repeated/) 8. **[Specific versus General Principles for Constitutional AI ↗](https://arxiv.org/abs/2310.13798) arXiv (Cornell University) · no summary yet**Sandipan Kundu et al.*2023 · 28 citations* ## Co-authors here 12 of 15 co-authors with a page, the most papers together first. - [Ben Mann](https://md.research.elamur.ai/free/en/authors/ben-mann/) ×2 - [Dawn Drain](https://md.research.elamur.ai/free/en/authors/dawn-drain/) ×2 - [Nelson Elhage](https://md.research.elamur.ai/free/en/authors/nelson-elhage/) ×2 - [Tom Conerly](https://md.research.elamur.ai/free/en/authors/tom-conerly/) ×2 - [Tom Henighan](https://md.research.elamur.ai/free/en/authors/tom-henighan/) ×2 - [Amanda Askell](https://md.research.elamur.ai/free/en/authors/amanda-askell/) - [Anna Chen](https://md.research.elamur.ai/free/en/authors/anna-chen/) - [Catherine Olsson](https://md.research.elamur.ai/free/en/authors/catherine-olsson/) - [Danny Hernandez](https://md.research.elamur.ai/free/en/authors/danny-hernandez/) - [Neel Nanda](https://md.research.elamur.ai/free/en/authors/neel-nanda/) - [Nicholas Joseph](https://md.research.elamur.ai/free/en/authors/nicholas-joseph/) - [Scott Johnston](https://md.research.elamur.ai/free/en/authors/scott-johnston/) ## Cited by Papers of our library cite their works 3,936 times (of the 149,439 papers whose bibliographies we read); [714 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/nova-dassarma/cited-by/). Updated 30 October 2026