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Melanie Subbiah

Melanie Subbiah has 2 papers in our library, in 2020–2024, most of them on Language models & pretraining, Summarization; often with Amanda Askell, Ariel Herbert-Voss, Arvind Neelakantan.

2 papers in our library · 2020–2024

OpenAlex · h-index 4 · 20 works · 2,908 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 11,345 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #188

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

🎓Columbia UniversityNew York · United States2022–2026 · 20 paperscurrent🎓Brown UniversityProvidence · United States2026 · 1 papercurrent

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

💡 Concepts 6Key ideas and methods explained on our concept pages.Artificial intelligence · 1 workFew-shot learning · 1 workLanguage model · 1 workLarge language models · 1 workNatural language processing · 1 workScaling laws · 1 work

 Works by year

2020s

20241 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Language Models are Few-Shot Learners (2020 · 14,178 citations)
    Pre-trained transformers usually still need a labeled fine-tuning set of thousands of examples for every new task.
  2. Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers (2024 · 30 citations)
    Testing how well LLMs grasp fiction is hard. Published stories have probably been seen during training, and judging long-form summaries is slow and expensive. This paper avoids both problems by working with skilled creative writers, who contributed their own unpublished short stories and then rated model summaries of…

All works

4 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. Language Models are Few-Shot LearnersarXiv:2005.14165Tom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 14,178 citations
  2. Reading Subtext: Evaluating Large Language Models on Short Story Summarization with WritersTransactions of the Association for Computational Linguistics (TACL)Melanie Subbiah, Sean Zhang, Lydia B. Chilton, et al.2024 · 30 citations
  3. Guiding LLM Decision-Making with Fairness Reward Models ↗neural information processing systems · no summary yetZara Hall et al.2025 · 3 citations
  4. Ownership in AI-Assisted Everyday Tasks ↗arXiv (Cornell University) · no summary yetMegan Wei et al.2026

 Co-authors here

11 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 11,345 times (of the 149,439 papers whose bibliographies we read); 1,684 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/melanie-subbiah/