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Laura Ruis

Laura Ruis has 2 papers in our library, in 2022–2024, most of them on Implicature, Language models & pretraining; often with Akbir Khan, Edward Grefenstette, Tim Rocktaschel.

2 papers in our library · 2022–2024

OpenAlex · h-index 4 · 23 works · 113 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 153 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6614

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

🎓Massachusetts Institute of TechnologyCambridge · United States2026 · 6 paperscurrent🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2026 · 2 paperscurrent🏢AnthropicSan Francisco · United States2026 · 1 papercurrent🎓Imperial College LondonLondon · United Kingdom2026 · 1 papercurrent🎓University College LondonLondon · United Kingdom2022–2025 · 5 papers🎓University of AmsterdamAmsterdam · Netherlands2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workImplicature · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Implicature · 1 work

 Works by year

2020s

20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Debating with More Persuasive LLMs Leads to More Truthful Answers (2024 · 101 citations)
    The paper asks whether weaker overseers can reliably check stronger AI systems, a problem that grows as models outpace human expertise and ground-truth labels become unavailable.
  2. The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs (2022 · 86 citations)
    The authors test whether large language models (LLMs) can understand conversational implicature, that is, an indirect answer.

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. Debating with More Persuasive LLMs Leads to More Truthful AnswersarXiv:2402.06782Akbir Khan, John Hughes, Dan Valentine, et al.2024 · 101 citations
  2. The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMsConference on Neural Information Processing Systems (NeurIPS)Laura Ruis, Akbir Khan, Stella Biderman, et al.2022 · 86 citations
  3. 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

9 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 153 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

Updated

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