🧑‍🔬 Andrew Lampinen

🏛️ Affiliations

🏢Google DeepMindLondon · United Kingdom2021–2026 · 16 paperscurrent🏢GoogleMountain View · United States2024–2026 · 4 paperscurrent🏢AnthropicSan Francisco · United States2026 · 2 paperscurrent🎓Stanford UniversityStanford · United States2017–2025 · 18 papers🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Can language models learn from explanations in context? (2022 · 295 citations)
   Large language models can pick up new tasks from a handful of in-context examples. The authors test whether adding explanations to those examples, as people do when teaching, also helps models. They took 40 difficult tasks and subtasks from BIG-bench. For each, one author wrote post-answer explanations for 15 sampled…
2. Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans (2024 · 63 citations)
   Earlier work (Lakretz et al. 2021, 2022) suggested that Transformer LMs handle recursively center-embedded sentences worse than people do. On the hardest subject-verb agreement condition, models scored at or below chance. This paper argues that the comparison was unbalanced. Human participants received instructions…

All works

7 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. Can language models learn from explanations in context?Findings of the Association for Computational LinguisticsAndrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.2022 · 295 citations
2. Language models show human-like content effects on reasoning tasks ↗arXiv:2207.07051 · no summary yetIshita Dasgupta et al.2022 · 162 citations
3. Data Distributional Properties Drive Emergent In-Context Learning in Transformers ↗arXiv:2205.05055 · no summary yetStephanie C. Y. Chan et al.2022 · 158 citations
4. Symbol tuning improves in-context learning in language models ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetJerry Wei et al.2023 · 68 citations
5. Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and HumansComputational LinguisticsAndrew Lampinen2024 · 63 citations
6. Interpretability Illusions in the Generalization of Simplified Models ↗arXiv (Cornell University) · no summary yetDan Friedman et al.2023 · 9 citations
7. Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention ↗arXiv (Cornell University) · no summary yetJing Huang et al.2026

🤝 Co-authors here

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

• Antonia Creswell
• Felix Hill
• Stephanie Chan

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Computational Linguistics: Computational Linguistics 2024
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2022
• Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

🔗 Cited by

Papers of our library cite their works 1,659 times (of the 149,439 papers whose bibliographies we read); 368 citing papers with a page here →.

Updated 30 October 2026

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