🧑‍🔬 Jeremy R. Cole

🏛️ Affiliations

🏢GoogleMountain View · United States2019–2023 · 10 papersEmployment🎓Pennsylvania State UniversityState College · United States2016–2019 · 7 papers🏢Google DeepMindLondon · United Kingdom2022–2023 · 4 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.

🧭 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. Time-Aware Language Models as Temporal Knowledge Bases (2022 · 349 citations)
   Language models are often viewed as repositories of facts. However, many facts become outdated over time: someone changes club, position, or employer. Models, meanwhile, are trained on a snapshot of data collected at a particular moment. The authors from Google Research investigate how this affects the knowledge of…
2. Selectively Answering Ambiguous Questions (2023 · 84 citations)
   Trustworthy language models should decline to answer when unsure. Earlier work on confidence estimation mostly assumed the question itself is clear, so that only the model's knowledge could be lacking. This paper separates two sources of doubt. Epistemic uncertainty means not knowing the fact. Denotational…
3. Gecko: Versatile Text Embeddings Distilled from Large Language Models (2024 · 74 citations)
   General-purpose text embedding models need large, varied training data to work across many tasks, and human-labeled collections are costly and limited in coverage.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Time-Aware Language Models as Temporal Knowledge BasesTransactions of the Association for Computational Linguistics (TACL)Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, et al.2022 · 349 citations
2. Selectively Answering Ambiguous QuestionsConference on Empirical Methods in Natural Language Processing (EMNLP)Jeremy Cole, Michael Zhang, Daniel Gillick, et al.2023 · 84 citations
3. Gecko: Versatile Text Embeddings Distilled from Large Language ModelsarXiv:2403.20327Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.2024 · 74 citations
4. DiffQG: Generating Questions to Summarize Factual Changes ↗Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · no summary yetJeremy R. Cole et al.2023 · 4 citations

🤝 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

• Bhuwan Dhingra ×2
• Daniel Gillick ×2
• Jacob Eisenstein ×2
• Julian Eisenschlos ×2
• Daniel Cer
• Jinhyuk Lee
• Kai Hui
• Michael Boratko
• Michael Zhang
• William W. Cohen
• Xiaoqi Ren
• Yi Luan

Venues

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

• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2023
• Transactions of the Association for Computational Linguistics (TACL): Transactions of the Association for Computational Linguistics (TACL) 2022
• Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

🔗 Cited by

Papers of our library cite their works 384 times (of the 149,439 papers whose bibliographies we read); 61 citing papers with a page here →.

Updated 30 October 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jeremy-r-cole/