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Tenghao Huang

Tenghao Huang has 2 papers in our library, in 2022–2024, most of them on Language models & pretraining; often with Colin Raffel, Derek Tam, Haokun Liu.

2 papers in our library · 2022–2024

OpenAlex · ORCID · h-index 3 · 13 works · 58 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 415 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #2809

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

🎓University of Southern CaliforniaLos Angeles · United States2023–2025 · 10 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

💡 Concepts 2Key ideas and methods explained on our concept pages.Few-shot learning · 1 workFine-tuning · 1 work

 Works by year

2020s

20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022 · 754 citations)
    Few-shot in-context learning (ICL) lets a large language model take on a new task without training. However, every prediction must reprocess all demonstration examples, which is costly, and results shift with prompt wording and example order. The authors test whether parameter-efficient fine-tuning (PEFT) is a better…
  2. Are Large Language Models Capable of Generating Human-Level Narratives? (2024 · 67 citations)
    The paper asks whether LLM-written stories match human ones in structure, not just fluency. The authors propose a framework that examines narrative discourse at three levels: macro story arcs (seven Vonnegut-style types), meso turning points (five types following Papalampidi et al., 2019) and micro affect (arousal…

All works

3 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. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.2022 · 754 citations
  2. Are Large Language Models Capable of Generating Human-Level Narratives?Conference on Empirical Methods in Natural Language Processing (EMNLP)Yufei Tian, Tenghao Huang, Miri Liu, et al.2024 · 67 citations
  3. WebDS: An End-to-End Benchmark for Web-based Data Science ↗arXiv (Cornell University) · no summary yetHsu, Ethan et al.2025 · 1 citation

 Co-authors here

9 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 415 times (of the 149,439 papers whose bibliographies we read); 70 citing papers with a page here →.

Updated

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