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Nicholas Meade

Nicholas Meade has 2 papers in our library, in 2022–2024, most of them on Language models & pretraining, Question answering, Evaluation & benchmarks; often with Siva Reddy, Parishad BehnamGhader, Vaibhav Adlakha.

2 papers in our library · 2022–2024

OpenAlex · ORCID · h-index 5 · 32 works · 423 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 217 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3931

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

🎓McGill UniversityMontreal · Canada2021–2026 · 19 paperscurrent🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2022–2026 · 6 paperscurrent🏢AmazonSeattle · United States2023 · 2 papers🎓Centre Universitaire de MilaMila · Algeria2022–2023 · 2 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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 workInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Bias, fairness & safety · 1 workLanguage models & pretraining · 1 workEvaluation & benchmarks · 1 workQuestion answering · 1 work

 Works by year

2020s

20241 paperEvaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering20221 paperAn Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

 Papers

The most cited first (OpenAlex and our library).

  1. An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models (2022 · 211 citations)
    Pre-trained language models absorb social stereotypes from their web-scale training text. Many debiasing methods have been proposed, but they are usually tested narrowly: often only for gender, and rarely for side effects. Under a single evaluation setup, this study compares five popular methods: Self-Debias…
  2. Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering (2024 · 145 citations)
    Instruction-following LLMs can answer questions when given retrieved passages and a task instruction, with no extra training.

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. An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Nicholas Meade, Elinor Poole-Dayan, Siva Reddy2022 · 211 citations
  2. Evaluating Correctness and Faithfulness of Instruction-Following Models for Question AnsweringTransactions of the Association for Computational Linguistics (TACL)Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, et al.2024 · 145 citations
  3. DeepSeek-R1 Thoughtology: Let's think about LLM ReasoningarXiv:2504.07128 · original ↗ · no summary yetSara Vera Marjanovic et al.2025 · 44 citations

 Co-authors here

3 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 217 times (of the 141,840 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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