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Ninghao Liu

Ninghao Liu has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Low-resource languages, Interpretability; often with Fan Yang, Mengnan Du, Dawei Yin.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 32 · 145 works · 5,131 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 192 citations in our library · 2 more in our library without a page yet · ⭐ Paper Star rank #2410

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 GeorgiaAthens · United States2021–2026 · 81 paperscurrent🎓Hong Kong Polytechnic UniversityHong Kong · Hong Kong2026 · 3 paperscurrent🎓Sichuan UniversityChengdu · China2025–2026 · 2 paperscurrent🎓Texas A&M UniversityCollege Station · United States2016–2022 · 37 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 4 Artificial Intelligence · 2 works Arts and Humanities · 2 works Computer Science · 2 works Language and Linguistics · 2 works

🧭 Topics 3 Language models & pretraining · 2 works Interpretability · 1 work Low-resource languages · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Explainability for Large Language Models: A Survey (2023 · 785 citations)
    Large language models are widely deployed but remain opaque. This makes it hard to anticipate harmful outputs, hallucinations or hidden biases. This survey introduces no new data or models. Instead, it organizes the literature on explaining Transformer-based language models into a single taxonomy. Methods are split…
  2. Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages (2024 · 95 citations)
    LLMs are trained on corpora dominated by English. For example, about 92.65% of GPT-3 tokens and 89.7% of LlaMa2 pre-training data are English. As a result, low-resource languages get weaker support, yet there are few quantitative ways to measure this gap. The authors propose Language Ranker, an intrinsic metric that…

Also in our library

2 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

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

 Cited by

Papers of our library cite their works 192 times (of the 141,840 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/ninghao-liu/