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Tong Xie

Tong Xie has 4 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning; often with Dongzhan Zhou, Wanli Ouyang, Yuqiang Li.

4 papers in our library · 2023–2025

OpenAlex · h-index 5 · 13 works · 48 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 122 times in our library · ⭐ Paper Stars ranking: #7203

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

🎓UNSW SydneySydney · Australia2024–2026 · 4 paperscurrent🎓University of GeorgiaAthens · United States2021 · 2 papers

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

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 11The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksMaterials Science · 2 worksMaterials Chemistry · 2 worksComputer Vision and Pattern Recognition · 2 worksApplied Mathematics · 1 workMathematics · 1 workComputational Theory and Mathematics · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20251 paper20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DARWIN Series: Domain Specific Large Language Models for Natural Science (2023 · 55 citations)
    General-purpose instruction-tuned LLMs perform poorly on natural science. Fine-tuned GPT models are closed and must be adapted on OpenAI's paid servers. Open alternatives are usually tuned on instructions written by GPT-3.5/GPT-4, which can reduce factual accuracy. The authors address this with DARWIN, a family of…
  2. LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning (2024 · 49 citations)
    LLaMA-Berry is a framework that helps LLMs solve hard math problems, especially at Olympiad level, without retraining the base model. It tackles two weaknesses. Stepwise generation gets little feedback along the way. Iterative self-correction tends to stall in poor local solutions.
  3. MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses (2024 · 36 citations)
    The paper asks whether LLMs can independently propose new, valid chemistry hypotheses when given only a research background (a question and/or a short survey) from any subfield.
  4. ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2025 · 25 citations)
    Scientific hypothesis generation has had no dedicated benchmark for LLMs. ResearchBench addresses this by building on a decomposition from MOOSE-Chem. In that view, a hypothesis comes from combining a research background with one or more inspirations. Discovery can then be split into three sub-tasks: finding…

 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 122 times (of the 149,439 papers whose bibliographies we read); 43 citing papers with a page here →.

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