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Cheng-Yu Hsieh

Cheng-Yu Hsieh has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Ranjay Krishna, Alexander Ratner, James Glass.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 13 · 74 works · 828 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 563 citations in our library · ⭐ Paper Star rank #2639

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

🎓Royal Holloway University of LondonEgham · United Kingdom2022–2026 · 8 paperscurrent🎓Purdue University West LafayetteWest Lafayette · United States2026 · 1 papercurrent🎓University of WashingtonSeattle · United States2022–2025 · 17 papers🎓National Taiwan UniversityTaipei · Taiwan2004–2023 · 10 papers🎓University of LondonLondon · United Kingdom2022–2025 · 7 papers🏢Taiwan Semiconductor Manufacturing CompanyHsinchu · Taiwan2011–2025 · 6 papers🎓National Chiayi UniversityChiayi City · Taiwan2012–2013 · 4 papers🎓Carnegie Mellon UniversityPittsburgh · United States2001–2009 · 4 papers🎓National Tsing Hua UniversityHsinchu · Taiwan2007–2021 · 3 papers🎓National Sun Yat-sen UniversityKaohsiung City · Taiwan2005–2020 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5 Artificial Intelligence · 2 works Language and Linguistics · 2 works Arts and Humanities · 2 works Computer Science · 2 works Mathematics · 1 work

🧭 Topics 1 Language models & pretraining · 2 works

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023 · 685 citations)
    Large language models do well from only a few examples, but they are expensive to serve. Compact task-specific models are cheaper, but they usually need a lot of human-labelled or LLM-labelled data before they perform as well. The authors propose Distilling step-by-step, which uses the LLM as a source of explanations…
  2. Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps (2024 · 76 citations)
    The paper addresses contextual hallucinations, where an LLM has the correct facts in its input but still produces unsupported content, for example in summarization or document-grounded QA.

 Co-authors here

6 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 563 times (of the 141,840 papers whose bibliographies we read); 38 citing papers with a page here →.

Updated

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