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Bryan Kian Hsiang Low

Bryan Kian Hsiang Low has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Finale Doshi-Velez.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 17 · 158 works · 792 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 97 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8587

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

🎓National University of SingaporeSingapore · Singapore2013–2026 · 70 paperscurrent

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

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks (2023 · 65 citations)
    Even strong large language models such as GPT-4 make frequent mistakes in elementary integer arithmetic. Their accuracy on multiplying or dividing large numbers falls close to zero. The authors build Goat by instruction-tuning LLaMA on about one million synthetic arithmetic examples generated with a Python script.…
  2. Prompting the Unknown: Understanding Response Uncertainty in Large Language Models (2026 · 3 citations)
    LLM applications in sensitive fields such as medicine or finance rely on carefully designed prompts, but it is unclear how the content of a prompt affects how stable and trustworthy the model's answers are.

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. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic TasksarXiv:2305.14201Tiedong Liu, Bryan Kian Hsiang Low2023 · 65 citations
  2. ReasonIR: Training Retrievers for Reasoning Tasks ↗arXiv (Cornell University) · no summary yetRulin Shao et al.2025 · 41 citations
  3. CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery ↗arXiv:2604.01658 · no summary yetAo Qu et al.2026 · 9 citations
  4. Prompting the Unknown: Understanding Response Uncertainty in Large Language ModelsFindings of the Association for Computational LinguisticsZe Yu Zhang, Arun Verma, Finale Doshi-Velez, et al.2026 · 3 citations

 Co-authors here

1 co-author 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 97 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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