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Dan Zhao

Dan Zhao has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning; often with Chengyu Huang, Claire Cardie, Jinyan Su.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 2 · 3 works · 47 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 136 times in our library · ⭐ Paper Stars ranking: #5161

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

🎓Massachusetts Institute of TechnologyCambridge · United States2022–2026 · 8 paperscurrent🎓New York UniversityNew York · United States2022–2026 · 13 papers🎓University of MichiganAnn Arbor · United States2019–2024 · 4 papers🎓Columbia UniversityNew York · United States2025 · 2 papers🔬MIT Lincoln LaboratoryLexington · United States2022 · 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.

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksElectrical and Electronic Engineering · 1 workEngineering · 1 workHardware and Architecture · 1 workArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20261 paper20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference (2023 · 288 citations)
    Most published estimates of the energy cost of large language models focus on training. Inference gets far less attention, even though deployed models answer queries constantly and may account for a larger share of their total energy use. This paper measures what it takes, in time, computation and energy, to run…
  2. Layer by Layer: Uncovering Hidden Representations in Language Models (2025 · 64 citations)
    Most pipelines built on large language models take embeddings from the last layer, on the assumption that earlier layers hold only shallow cues.
  3. GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs (2026)
    LLMs that score well on clean math benchmarks often fail when real users send messy prompts. The authors first examined long English prompts from WildChat-1M, using QwQ-32B as a checker. Grammar slips, symbol errors, misordered statements and irrelevant content were all common, and longer prompts contained more of…

 Co-authors here

5 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 136 times (of the 149,439 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated

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